Current natural language understanding (NLU) models have been continuously scaling up, both in terms of model size and input context, introducing more hidden and input neurons. While this generally improves performance on average, the extra neurons do not yield a consistent improvement for all instances. This is because some hidden neurons are redundant, and the noise mixed in input neurons tends to distract the model. Previous work mainly focuses on extrinsically reducing low-utility neurons by additional post- or pre-processing, such as network pruning and context selection, to avoid this problem. Beyond that, can we make the model reduce redundant parameters and suppress input noise by intrinsically enhancing the utility of each neuron? If a model can efficiently utilize neurons, no matter which neurons are ablated (disabled), the ablated submodel should perform no better than the original full model. Based on such a comparison principle between models, we propose a cross-model comparative loss for a broad range of tasks. Comparative loss is essentially a ranking loss on top of the task-specific losses of the full and ablated models, with the expectation that the task-specific loss of the full model is minimal. We demonstrate the universal effectiveness of comparative loss through extensive experiments on 14 datasets from three distinct NLU tasks based on five widely used pre-trained language models and find it particularly superior for models with few parameters or long input.

Molecular property prediction is essential for drug discovery. In recent years, deep learning methods have been introduced to this area and achieved state-of-the-art performances. However, most of existing methods ignore the intrinsic relations between molecular properties which can be utilized to improve the performances of corresponding prediction tasks. In this paper, we propose a new approach, namely Physics properties Enhanced Molecular Property prediction (PEMP), to utilize relations between molecular properties revealed by previous physics theory and physical chemistry studies. Specifically, we enhance the training of the chemical and physiological property predictors with related physics property prediction tasks. We design two different methods for PEMP, respectively based on multi-task learning and transfer learning. Both methods include a model-agnostic molecule representation module and a property prediction module. In our implementation, we adopt both the state-of-the-art molecule embedding models under the supervised learning paradigm and the pretraining paradigm as the molecule representation module of PEMP, respectively. Experimental results on public benchmark MoleculeNet show that the proposed methods have the ability to outperform corresponding state-of-the-art models.

Pseudo-relevance feedback (PRF) has proven to be an effective query reformulation technique to improve retrieval accuracy. It aims to alleviate the mismatch of linguistic expressions between a query and its potential relevant documents. Existing PRF methods independently treat revised queries originating from the same query but using different numbers of feedback documents, resulting in severe query drift. Without comparing the effects of two different revisions from the same query, a PRF model may incorrectly focus on the additional irrelevant information increased in the more feedback, and thus reformulate a query that is less effective than the revision using the less feedback. Ideally, if a PRF model can distinguish between irrelevant and relevant information in the feedback, the more feedback documents there are, the better the revised query will be. To bridge this gap, we propose the Loss-over-Loss (LoL) framework to compare the reformulation losses between different revisions of the same query during training. Concretely, we revise an original query multiple times in parallel using different amounts of feedback and compute their reformulation losses. Then, we introduce an additional regularization loss on these reformulation losses to penalize revisions that use more feedback but gain larger losses. With such comparative regularization, the PRF model is expected to learn to suppress the extra increased irrelevant information by comparing the effects of different revised queries. Further, we present a differentiable query reformulation method to implement this framework. This method revises queries in the vector space and directly optimizes the retrieval performance of query vectors, applicable for both sparse and dense retrieval models. Empirical evaluation demonstrates the effectiveness and robustness of our method for two typical sparse and dense retrieval models.

Neural text matching models have been widely used in community question answering, information retrieval, and dialogue. However, these models designed for short texts cannot well address the long-form text matching problem, because there are many contexts in long-form texts can not be directly aligned with each other, and it is difficult for existing models to capture the key matching signals from such noisy data. Besides, these models are computationally expensive for simply use all textual data indiscriminately. To tackle the effectiveness and efficiency problem, we propose a novel hierarchical noise filtering model, namely Match-Ignition. The main idea is to plug the well-known PageRank algorithm into the Transformer, to identify and filter both sentence and word level noisy information in the matching process. Noisy sentences are usually easy to detect because previous work has shown that their similarity can be explicitly evaluated by the word overlapping, so we directly use PageRank to filter such information based on a sentence similarity graph. Unlike sentences, words rely on their contexts to express concrete meanings, so we propose to jointly learn the filtering and matching process, to well capture the critical word-level matching signals. Specifically, a word graph is first built based on the attention scores in each self-attention block of Transformer, and key words are then selected by applying PageRank on this graph. In this way, noisy words will be filtered out layer by layer in the matching process. Experimental results show that Match-Ignition outperforms both SOTA short text matching models and recent long-form text matching models. We also conduct detailed analysis to show that Match-Ignition efficiently captures important sentences and words, to facilitate the long-form text matching process.

Community Question Answering (CQA) sites such as Yahoo! Answers and Baidu Knows have emerged as rich knowledge resources for information seekers. However, answers posted to CQA sites often vary a lot in their qualities. User votes from the community may partially reflect the overall quality of the answer, but they are often missing. Hence, automatic selection of "good'' answers becomes a practical research problem that will help us manage the quality of accumulated knowledge. Without loss of generality, a good answer should deliver not only relevant but also trustworthy information that can help resolve the information needs of the posted question, but the latter has received less investigation in the past. In this paper, we propose a novel matching-verification framework for automatic answer selection. The matching component assesses the relevance of a candidate answer to a given question as conventional QA methods. The major enhancement is the verification component, which aims to leverage the wisdom of crowds, e.g., some big information repository, for trustworthiness measurement. Given a question, we take the top retrieved results from the information repository as the supporting evidences to distill the consensus representation. A major challenge is that there is no guarantee that one can always obtain reliable consensus from the wisdom of crowds for a question due to the noisy nature and the limitation of the existing search technology.Therefore, we decompose the trustworthiness measurement into two parts, i.e., a verification score which measures the consistency between a candidate answer and the consensus representation, and a confidence score which measures the reliability of the consensus itself. Empirical studies on three real-world CQA data collections, i.e. YahooQA, QuoraQA and AmazonQA, show that our approach can significantly outperform the state-of-the-art methods on the answer selection task.

Relevance plays a central role in information retrieval (IR), which has received extensive studies starting from the 20th century. The definition and the modeling of relevance has always been critical challenges in both information science and computer science research areas. Along with the debate and exploration on relevance, IR has already become a core task in many real-world applications, such as Web search engines, question answering systems, conversational bots, and so on. While relevance acts as a unified concept in all these retrieval tasks, the inherent definitions are quite different due to the heterogeneity of these tasks. This raises a question to us: Do these different forms of relevance really lead to different modeling focuses? To answer this question, in this work, we conduct an empirical study on relevance modeling in three representative IR tasks, i.e., document retrieval, answer retrieval, and response retrieval. Specifically, we attempt to study the following two questions: 1) Does relevance modeling in these tasks really show differences in terms of natural language understanding (NLU)? We employ 16 linguistic tasks to probe a unified retrieval model over these three retrieval tasks to answer this question. 2) If there do exist differences, how can we leverage the findings to enhance the relevance modeling? We proposed three intervention methods to investigate how to leverage different modeling focuses of relevance to improve these IR tasks. We believe the way we study the problem as well as our findings would be beneficial to the IR community.

How to effectively utilize the dialogue history is a crucial problem in multi-turn dialogue generation. Previous works usually employ various neural network architectures (e.g., recurrent neural networks, attention mechanisms, and hierarchical structures) to model the history. However, a recent empirical study by Sankar et al. has shown that these architectures lack the ability of understanding and modeling the dynamics of the dialogue history. For example, the widely used architectures are insensitive to perturbations of the dialogue history, such as words shuffling, utterances missing, and utterances reordering. To tackle this problem, we propose a Ranking Enhanced Dialogue generation framework in this paper. Despite the traditional representation encoder and response generation modules, an additional ranking module is introduced to model the ranking relation between the former utterance and consecutive utterances. Specifically, the former utterance and consecutive utterances are treated as query and corresponding documents, and both local and global ranking losses are designed in the learning process. In this way, the dynamics in the dialogue history can be explicitly captured. To evaluate our proposed models, we conduct extensive experiments on three public datasets, i.e., bAbI, PersonaChat, and JDC. Experimental results show that our models produce better responses in terms of both quantitative measures and human judgments, as compared with the state-of-the-art dialogue generation models. Furthermore, we give some detailed experimental analysis to show where and how the improvements come from.

Machine reading comprehension (MRC) has become a core component in a variety of natural language processing (NLP) applications such as question answering and dialogue systems. It becomes a practical challenge that an MRC model needs to learn in non-stationary environments, in which the underlying data distribution changes over time. A typical scenario is the domain drift, i.e. different domains of data come one after another, where the MRC model is required to adapt to the new domain while maintaining previously learned ability. To tackle such a challenge, in this work, we introduce the Continual Domain Adaptation (CDA) task for MRC. So far as we know, this is the first study on the continual learning perspective of MRC. We build two benchmark datasets for the CDA task, by re-organizing existing MRC collections into different domains with respect to context type and question type, respectively. We then analyze and observe the catastrophic forgetting (CF) phenomenon of MRC under the CDA setting. To tackle the CDA task, we propose several BERT-based continual learning MRC models using either regularization-based methodology or dynamic-architecture paradigm. We analyze the performance of different continual learning MRC models under the CDA task and show that the proposed dynamic-architecture based model achieves the best performance.

Query understanding is a fundamental problem in information retrieval (IR), which has attracted continuous attention through the past decades. Many different tasks have been proposed for understanding users' search queries, e.g., query classification or query clustering. However, it is not that precise to understand a search query at the intent class/cluster level due to the loss of many detailed information. As we may find in many benchmark datasets, e.g., TREC and SemEval, queries are often associated with a detailed description provided by human annotators which clearly describes its intent to help evaluate the relevance of the documents. If a system could automatically generate a detailed and precise intent description for a search query, like human annotators, that would indicate much better query understanding has been achieved. In this paper, therefore, we propose a novel Query-to-Intent-Description (Q2ID) task for query understanding. Unlike those existing ranking tasks which leverage the query and its description to compute the relevance of documents, Q2ID is a reverse task which aims to generate a natural language intent description based on both relevant and irrelevant documents of a given query. To address this new task, we propose a novel Contrastive Generation model, namely CtrsGen for short, to generate the intent description by contrasting the relevant documents with the irrelevant documents given a query. We demonstrate the effectiveness of our model by comparing with several state-of-the-art generation models on the Q2ID task. We discuss the potential usage of such Q2ID technique through an example application.

In learning-to-rank for information retrieval, a ranking model is automatically learned from the data and then utilized to rank the sets of retrieved documents. Therefore, an ideal ranking model would be a mapping from a document set to a permutation on the set, and should satisfy two critical requirements: (1) it should have the ability to model cross-document interactions so as to capture local context information in a query; (2) it should be permutation-invariant, which means that any permutation of the inputted documents would not change the output ranking. Previous studies on learning-to-rank either design uni-variate scoring functions that score each document separately, and thus failed to model the cross-document interactions; or construct multivariate scoring functions that score documents sequentially, which inevitably sacrifice the permutation invariance requirement. In this paper, we propose a neural learning-to-rank model called SetRank which directly learns a permutation-invariant ranking model defined on document sets of any size. SetRank employs a stack of (induced) multi-head self attention blocks as its key component for learning the embeddings for all of the retrieved documents jointly. The self-attention mechanism not only helps SetRank to capture the local context information from cross-document interactions, but also to learn permutation-equivariant representations for the inputted documents, which therefore achieving a permutation-invariant ranking model. Experimental results on three benchmarks showed that the SetRank significantly outperformed the baselines include the traditional learning-to-rank models and state-of-the-art Neural IR models.

Community Question Answering (CQA) has become a primary means for people to acquire knowledge, where people are free to ask questions or submit answers. To enhance the efficiency of the service, similar question identification becomes a core task in CQA which aims to find a similar question from the archived repository whenever a new question is asked. However, it has long been a challenge to properly measure the similarity between two questions due to the inherent variation of natural language, i.e., there could be different ways to ask a same question or different questions sharing similar expressions. To alleviate this problem, it is natural to involve the existing answers for the enrichment of the archived questions. Traditional methods typically take aone-side usage, which leverages the answer as some expanded representation of the corresponding question. Unfortunately, this may introduce unexpected noises into the similarity computation since answers are often long and diverse, leading to inferior performance. In this work, we propose atwo-side usage, which leverages the answer as a bridge of the two questions. The key idea is based on our observation that similar questions could be addressed by similar parts of the answer while different questions may not. In other words, we can compare the matching patterns of the two questions over the same answer to measure their similarity. In this way, we propose a novel matching over matching model, namely Match2, which compares the matching patterns between two question-answer pairs for similar question identification. Empirical experiments on two benchmark datasets demonstrate that our model can significantly outperform previous state-of-the-art methods on the similar question identification task.

This paper concerns reinforcement learning~(RL) of the document ranking models for information retrieval~(IR). One branch of the RL approaches to ranking formalize the process of ranking with Markov decision process~(MDP) and determine the model parameters with policy gradient. Though preliminary success has been shown, these approaches are still far from achieving their full potentials. Existing policy gradient methods directly utilize the absolute performance scores (returns) of the sampled document lists in its gradient estimations, which may cause two limitations: 1) fail to reflect the relative goodness of documents within the same query, which usually is close to the nature of IR ranking; 2) generate high variance gradient estimations, resulting in slow learning speed and low ranking accuracy. To deal with the issues, we propose a novel policy gradient algorithm in which the gradients are determined using pairwise comparisons of two document lists sampled within the same query. The algorithm, referred to as Pairwise Policy Gradient (PPG), repeatedly samples pairs of document lists, estimates the gradients with pairwise comparisons, and finally updates the model parameters. Theoretical analysis shows that PPG makes an unbiased and low variance gradient estimations. Experimental results have demonstrated performance gains over the state-of-the-art baselines in search result diversification and text retrieval.

Recommendation reason generation, aiming at showing the selling points of products for customers, plays a vital role in attracting customers' attention as well as improving user experience. A simple and effective way is to extract keywords directly from the knowledge-base of products, i.e., attributes or title, as the recommendation reason. However, generating recommendation reason from product knowledge doesn't naturally respond to users' interests. Fortunately, on some E-commerce websites, there exists more and more user-generated content (user-content for short), i.e., product question-answering (QA) discussions, which reflect user-cared aspects. Therefore, in this paper, we consider generating the recommendation reason by taking into account not only the product attributes but also the customer-generated product QA discussions. In reality, adequate user-content is only possible for the most popular commodities, whereas large sums of long-tail products or new products cannot gather a sufficient number of user-content. To tackle this problem, we propose a user-inspired multi-source posterior transformer (MSPT), which induces the model reflecting the users' interests with a posterior multiple QA discussions module, and generating recommendation reasons containing the product attributes as well as the user-cared aspects. Experimental results show that our model is superior to traditional generative models. Additionally, the analysis also shows that our model can focus more on the user-cared aspects than baselines.

The abductive natural language inference task (αNLI) is proposed to evaluate the abductive reasoning ability of a learning system. In the αNLI task, two observations are given and the most plausible hypothesis is asked to pick out from the candidates. Existing methods simply formulate it as a classification problem, thus a cross-entropy log-loss objective is used during training. However, discriminating true from false does not measure the plausibility of a hypothesis, for all the hypotheses have a chance to happen, only the probabilities are different. To fill this gap, we switch to a ranking perspective that sorts the hypotheses in order of their plausibilities. With this new perspective, a novel L2R2 approach is proposed under the learning-to-rank framework. Firstly, training samples are reorganized into a ranking form, where two observations and their hypotheses are treated as the query and a set of candidate documents respectively. Then, an ESIM model or pre-trained language model, e.g. BERT or RoBERTa, is obtained as the scoring function. Finally, the loss functions for the ranking task can be either pair-wise or list-wise for training. The experimental results on the ART dataset reach the state-of-the-art in the public leaderboard.

Reading comprehension (RC) aims to locate a text span from a context passage to answer the given question. Despite the effectiveness of modern neural RC models, most existing work relies on maximum likelihood estimation (MLE) and ignores the structure of the output space. That is during training, one treats all the text spans do not match the ground truth as equally poor, leading to overconfident predictions on ground truth labels and reduced generalization ability in test. One way to bridge the gap between training and test is to take into account the task reward of alternative outputs using the reinforcement learning (RL) algorithms, which is often deficient in optimization as compared with MLE. In this paper, we propose a new learning criterion for the RC task which combines the merits of both MLE and RL-based methods. Specifically, we show that we are able to derive the distribution of the outputs, i.e., label distribution, using their corresponding task rewards based on the decomposition property of the RC problem. We then optimize the RC model by directly learning towards the auxiliary label distribution, instead of the ground truth label, using the MLE framework. In this way, we can make use of the structure of the output space for better generalization (as RL) via efficient optimization (as MLE). We name our approach as Label Distribution augmented MLE (LD-MLE), which is a general learning criterion that could be adopted by almost all the existing RC models. Experiments on three representative benchmark datasets demonstrate that RC models learned with the LD-MLE criterion can achieve consistently improved results over those based on the traditional MLE and RL-based criteria.

The conversation task is usually formulated as a conditional generation problem, i.e., to generate a natural and meaningful response given the input utterance. Generally speaking, this formulation is apparently based on an oversimplified assumption that the response is solely dependent on the input utterance. It ignores the subjective factor of the responder, e.g., his/her emotion or knowledge state, which is a major factor that affects the response in practice. Without explicitly differentiating such subjective factor behind the response, existing generation models can only learn the general shape of conversations, leading to the blandness problem of the response. Moreover, there is no intervention mechanism within the existing generation process, since the response is fully decided by the input utterance. In this work, we propose to view the conversation task as a dual-factor generation problem, including an objective factor denoting the input utterance and a subjective factor denoting the responder state. We extend the existing neural sequence-to-sequence (Seq2Seq) model to accommodate the responder state modeling. We introduce two types of responder state, i.e., discrete and continuous state, to model emotion state and topic preference state, respectively. We show that with our dual-factor generation model, we can not only better fit the conversation data, but also actively control the generation of the response with respect to sentiment or topic specificity.

Web question answering (QA) has become an dispensable component in modern search systems, which can significantly improve users' search experience by providing a direct answer to users' information need. This could be achieved by applying machine reading comprehension (MRC) models over the retrieved passages to extract answers with respect to the search query. With the development of deep learning techniques, state-of-the-art MRC performances have been achieved by recent deep methods. However, existing studies on MRC seldom address the predictive uncertainty issue, i.e., how likely the prediction of an MRC model is wrong, leading to uncontrollable risks in real-world Web QA applications. In this work, we first conduct an in-depth investigation over the risk of Web QA. We then introduce a novel risk control framework, which consists of a qualify model for uncertainty estimation using the probe idea, and a decision model for selectively output. For evaluation, we introduce risk-related metrics, rather than the traditional EM and F1 in MRC, for the evaluation of risk-aware Web QA. The empirical results over both the real-world Web QA dataset and the academic MRC benchmark collection demonstrate the effectiveness of our approach.

In this paper, we introduce and tackle the Outline Generation (OG) task, which aims to unveil the inherent content structure of a multi-paragraph document by identifying its potential sections and generating the corresponding section headings. Without loss of generality, the OG task can be viewed as a novel structured summarization task. To generate a sound outline, an ideal OG model should be able to capture three levels of coherence, namely the coherence between context paragraphs, that between a section and its heading, and that between context headings. The first one is the foundation for section identification, while the latter two are critical for consistent heading generation. In this work, we formulate the OG task as a hierarchical structured prediction problem, i.e., to first predict a sequence of section boundaries and then a sequence of section headings accordingly. We propose a novel hierarchical structured neural generation model, named HiStGen, for the task. Our model attempts to capture the three-level coherence via the following ways. First, we introduce a Markov paragraph dependency mechanism between context paragraphs for section identification. Second, we employ a section-aware attention mechanism to ensure the semantic coherence between a section and its heading. Finally, we leverage a Markov heading dependency mechanism and a review mechanism between context headings to improve the consistency and eliminate duplication between section headings. Besides, we build a novel Wriptsize IKI OG dataset, a public collection which consists of over 1.75 million document-outline pairs for research on the OG task. Experimental results on our benchmark dataset demonstrate that our model can significantly outperform several state-of-the-art sequential generation models for the OG task.

Ranking SVM, which formalizes the problem of learning a ranking model as that of learning a binary SVM on preference pairs of documents, is a state-of-the-art ranking model in information retrieval. The dual form solution of a linear Ranking SVM model can be written as a linear combination of the preference pairs, i.e., w = Σ(i,j)αijxi-xj), where αijdenotes the Lagrange parameters associated with each preference pair (i,j). It is observed that there exist obvious interactions among the document pairs because two preference pairs could share a same document as their items, e.g., preference pairs (d1,d2) and (d1,d3) share the document d1. Thus it is natural to ask if there also exist interactions over the model parameters αij, which may be leveraged to construct better ranking models. This paper aims to answer the question. We empirically found that there exists a low-rank structure over the rearranged Ranking SVM model parameters αij, which indicates that the interactions do exist. Based on the discovery, we made modifications on the original Ranking SVM model by explicitly applying low-rank constraints to the Lagrange parameters, achieving two novel algorithms called Factorized Ranking SVM and Regularized Ranking SVM, respectively. Specifically, in Factorized Ranking SVM each parameter αijis decomposed as a product of two low-dimensional vectors, i.e., αij=〈vi,vj〉, where vectors viand vjcorrespond to document i and j, respectively; In Regularized Ranking SVM, a nuclear norm is applied to the rearranged parameters matrix for controlling its rank. Experimental results on three LETOR datasets show that both of the proposed methods can outperform state-of-the-art learning to rank models including the conventional Ranking SVM.

Hashing techniques for approximate nearest neighbor search (ANNS) encode data points into a set of short binary codes, while trying to preserve the neighborhood structure of the original data as much as possible. With the binary codes, the task of ANNS can be easily conducted over large-scale dataset, due to the high efficiency of pairwise comparison with the Hamming distance. Although binary codes have low computation and storage cost, the data are heavily compressed so that partial neighborhood structure information would be inevitably lost. To address this issue, we propose to introduce the k-nearest neighbors (k-NNs) in the original space into the Hamming space (i.e., associating a binary code with its original k-NNs) to enhance the effectiveness of existing hashing techniques with little overhead. Based on this idea, we develop a novel search scheme for hashing techniques namely neighborhood voting, i.e., each point retrieved by a query code will vote for its neighbors and itself, and the more voted, the better candidates. In this way, search in hashing is not simply the collision between codes (i.e., query code and candidate code), but also the collision between neighbors (i.e., neighbors of candidate points). The underlying assumption is that the true neighbors of a query point should be close to each other, while points with similar binary codes but seldom be the neighbors of other candidate points would be false positives. We introduce a novel data structure called aggregated hash table for implementing our idea and accelerating the online search process. Experimental results show that our search scheme can significantly improve the search effectiveness while having good efficiency over different existing hashing techniques.

In this paper, we introduce and tackle the Question Headline Generation (QHG) task. The motivation comes from the investigation of a real-world news portal where we find that news articles with question headlines often receive much higher click-through ratio than those with non-question headlines. The QHG task can be viewed as a specific form of the Question Generation (QG) task, with the emphasis on creating a natural question from a given news article by taking the entire article as the answer. A good QHG model thus should be able to generate a question by summarizing the essential topics of an article. Based on this idea, we propose a novel dual-attention sequence-to-sequence model (DASeq2Seq) for the QHG task. Unlike traditional sequence-to-sequence models which only employ the attention mechanism in the decoding phase for better generation, our DASeq2Seq further introduces a self-attention mechanism in the encoding phase to help generate a good summary of the article. We investigate two ways of the self-attention mechanism, namely global self-attention and distributed self-attention. Besides, we employ a vocabulary gate over both generic and question vocabularies to better capture the question patterns. Through the offline experiments, we show that our approach can significantly outperform the state-of-the-art question generation or headline generation models. Furthermore, we also conduct online evaluation to demonstrate the effectiveness of our approach using A/B test.

Assessing relevance between a query and a document is challenging in ad-hoc retrieval due to its diverse patterns, i.e., a document could be relevant to a query as a whole or partially as long as it provides sufficient information for users' need. Such diverse relevance patterns require an ideal retrieval model to be able to assess relevance in the right granularity adaptively. Unfortunately, most existing retrieval models compute relevance at a single granularity, either document-wide or passage-level, or use fixed combination strategy, restricting their ability in capturing diverse relevance patterns. In this work, we propose a data-driven method to allow relevance signals at different granularities to compete with each other for final relevance assessment. Specifically, we propose a HIerarchical Neural maTching model (HiNT) which consists of two stacked components, namely local matching layer and global decision layer. The local matching layer focuses on producing a set of local relevance signals by modeling the semantic matching between a query and each passage of a document. The global decision layer accumulates local signals into different granularities and allows them to compete with each other to decide the final relevance score.Experimental results demonstrate that our HiNT model outperforms existing state-of-the-art retrieval models significantly on benchmark ad-hoc retrieval datasets.

The goal of search result diversification is to select a subset of documents from the candidate set to satisfy as many different subtopics as possible. In general, it is a problem of subset selection and selecting an optimal subset of documents is NP-hard. Existing methods usually formalize the problem as ranking the documents with greedy sequential document selection. At each of the ranking position the document that can provide the largest amount of additional information is selected. It is obvious that the greedy selections inevitably produce suboptimal rankings. In this paper we propose to partially alleviate the problem with a Monte Carlo tree search (MCTS) enhanced Markov decision process (MDP), referred to as M$^2$Div. In M$^2$Div, the construction of diverse ranking is formalized as an MDP process where each action corresponds to selecting a document for one ranking position. Given an MDP state which consists of the query, selected documents, and candidates, a recurrent neural network is utilized to produce the policy function for guiding the document selection and the value function for predicting the whole ranking quality. The produced raw policy and value are then strengthened with MCTS through exploring the possible rankings at the subsequent positions, achieving a better search policy for decision-making. Experimental results based on the TREC benchmarks showed that M$^2$Div can significantly outperform the state-of-the-art baselines based on greedy sequential document selection, indicating the effectiveness of the exploratory decision-making mechanism in M$^2$Div.

Policy gradient, which makes use of Monte Carlo method to get an unbiased estimation of the parameter gradients, has been widely used in reinforcement learning. One key issue in policy gradient is reducing the variance of the estimation. From the viewpoint of statistics, policy gradient with baseline, a successful variance reduction method for policy gradient, directly applies the control variates method, a traditional variance reduction technique used in Monte Carlo, to policy gradient. One problem with control variates method is that the quality of estimation heavily depends on the choice of the control variates. To address the issue and inspired by the antithetic variates method for variance reduction, we propose to combine the antithetic variates method with traditional policy gradient for the multi-armed bandit problem. Furthermore, we achieve a new policy gradient algorithm called Antithetic-Arm Bandit (AAB). In AAB, the gradient is estimated through coordinate ascent where at each iteration gradient of the target arm is estimated through: 1) constructing a sequence of arms which is approximately monotonic in terms of estimated gradients, 2) sampling a pair of antithetic arms over the sequence, and 3) re-estimating the target gradient based on the sampled pair. Theoretical analysis proved that AAB achieved an unbiased and variance reduced estimation. Experimental results based on a multi-armed bandit task showed that AAB can achieve state-of-the-art performances.

When applying learning to rank algorithms to Web search, a large number of features are usually designed to capture the relevance signals. Most of these features are computed based on the extracted textual elements, link analysis, and user logs. However, Web pages are not solely linked texts, but have structured layout organizing a large variety of elements in different styles. Such layout itself can convey useful visual information, indicating the relevance of a Web page. For example, the query-independent layout (i.e., raw page layout) can help identify the page quality, while the query-dependent layout (i.e., page rendered with matched query words) can further tell rich structural information (e.g., size, position and proximity) of the matching signals. However, such visual information of layout has been seldom utilized in Web search in the past. In this work, we propose to learn rich visual features automatically from the layout of Web pages (i.e., Web page snapshots) for relevance ranking. Both query-independent and query-dependent snapshots are considered as the new inputs. We then propose a novel visual perception model inspired by human's visual search behaviors on page viewing to extract the visual features. This model can be learned end-to-end together with traditional human-crafted features. We also show that such visual features can be efficiently acquired in the online setting with an extended inverted indexing scheme. Experiments on benchmark collections demonstrate that learning visual features from Web page snapshots can significantly improve the performance of relevance ranking in ad-hoc Web retrieval tasks.

This paper concerns a deep learning approach to relevance ranking in information retrieval (IR). Existing deep IR models such as DSSM and CDSSM directly apply neural networks to generate ranking scores, without explicit understandings of the relevance. According to the human judgement process, a relevance label is generated by the following three steps: 1) relevant locations are detected; 2) local relevances are determined; 3) local relevances are aggregated to output the relevance label. In this paper we propose a new deep learning architecture, namely DeepRank, to simulate the above human judgment process. Firstly, a detection strategy is designed to extract the relevant contexts. Then, a measure network is applied to determine the local relevances by utilizing a convolutional neural network (CNN) or two-dimensional gated recurrent units (2D-GRU). Finally, an aggregation network with sequential integration and term gating mechanism is used to produce a global relevance score. DeepRank well captures important IR characteristics, including exact/semantic matching signals, proximity heuristics, query term importance, and diverse relevance requirement. Experiments on both benchmark LETOR dataset and a large scale clickthrough data show that DeepRank can significantly outperform learning to ranking methods, and existing deep learning methods.

One of the central issues in learning to rank for information retrieval is to develop algorithms that construct ranking models by directly optimizing evaluation measures such as normalized discounted cumulative gain~(ND CG). Existing methods usually focus on optimizing a specific evaluation measure calculated at a fixed position, e.g., NDCG calculated at a fixed position K. In information retrieval the evaluation measures, including the widely used NDCG and [email protected], are usually designed to evaluate the document ranking at all of the ranking positions, which provide much richer information than only measuring the document ranking at a single position. Thus, it is interesting to ask if we can devise an algorithm that has the ability of leveraging the measures calculated at all of the ranking postilions, for learning a better ranking model. In this paper, we propose a novel learning to rank model on the basis of Markov decision process (MDP), referred to as MDPRank. In the learning phase of MDPRank, the construction of a document ranking is considered as a sequential decision making, each corresponds to an action of selecting a document for the corresponding position. The policy gradient algorithm of REINFORCE is adopted to train the model parameters. The evaluation measures calculated at every ranking positions are utilized as the immediate rewards to the corresponding actions, which guide the learning algorithm to adjust the model parameters so that the measure is optimized. Experimental results on LETOR benchmark datasets showed that MDPRank can outperform the state-of-the-art baselines.

In this paper we address the issue of learning diverse ranking models for search result diversification. Typical methods treat the problem of constructing a diverse ranking as a process of sequential document selection. At each ranking position, the document that can provide the largest amount of additional information to the users is selected, because the search users usually browse the documents in a top-down manner. Thus, to select an optimal document for a position, it is critical for a diverse ranking model to capture the utility of information the user have perceived from the preceding documents. Existing methods usually calculate the ranking scores (e.g., the marginal relevance) directly based on the query and the selected documents, with heuristic rules or handcrafted features. The utility the user perceived at each of the ranks, however, is not explicitly modeled. In this paper, we present a novel diverse ranking model on the basis of continuous state Markov decision process (MDP) in which the user perceived utility is modeled as a part of the MDP state. Our model, referred to as MDP-DIV, sequentially takes the actions of selecting one document according to current state, and then updates the state for the chosen of the next action. The transition of the states are modeled in a recurrent manner and the model parameters are learned with policy gradient. Experimental results based on the TREC benchmarks showed that MDP-DIV can significantly outperform the state-of-the-art baselines.

The queries issued to search engines are often ambiguous or multifaceted, which requires search engines to return diverse results that can fulfill as many different information needs as possible; this is called search result diversification . Recently, the relational learning to rank model, which designs a learnable ranking function following the criterion of maximal marginal relevance, has shown effectiveness in search result diversification [Zhu et al. 2014]. The goodness of a diverse ranking model is usually evaluated with diversity evaluation measures such as α-NDCG [Clarke et al. 2008], ERR-IA [Chapelle et al. 2009], and D#-NDCG [Sakai and Song 2011]. Ideally the learning algorithm would train a ranking model that could directly optimize the diversity evaluation measures with respect to the training data. Existing relational learning to rank algorithms, however, only train the ranking models by optimizing loss functions that loosely relate to the evaluation measures. To deal with the problem, we propose a general framework for learning relational ranking models via directly optimizing any diversity evaluation measure . In learning, the loss function upper-bounding the basic loss function defined on a diverse ranking measure is minimized. We can derive new diverse ranking algorithms under the framework, and several diverse ranking algorithms are created based on different upper bounds over the basic loss function. We conducted comparisons between the proposed algorithms with conventional diverse ranking methods using the TREC benchmark datasets. Experimental results show that the algorithms derived under the diverse learning to rank framework always significantly outperform the state-of-the-art baselines.

Search result diversification has attracted considerable attention as a means to tackle the ambiguous or multi-faceted information needs of users. One of the key problems in search result diversification is novelty, that is, how to measure the novelty of a candidate document with respect to other documents. In the heuristic approaches, the predefined document similarity functions are directly utilized for defining the novelty. In the learning approaches, the novelty is characterized based on a set of handcrafted features. Both the similarity functions and the features are difficult to manually design in real world due to the complexity of modeling the document novelty. In this paper, we propose to model the novelty of a document with a neural tensor network. Instead of manually defining the similarity functions or features, the new method automatically learns a nonlinear novelty function based on the preliminary representation of the candidate document and other documents. New diverse learning to rank models can be derived under the relational learning to rank framework. To determine the model parameters, loss functions are constructed and optimized with stochastic gradient descent. Extensive experiments on three public TREC datasets show that the new derived algorithms can significantly outperform the baselines, including the state-of-the-art relational learning to rank models.

Demographic attributes play an important role in retail market to characterize different types of users. Such signals however are often only available for a small fraction of users in practice due to the difficulty in manual collection process by retailers. In this paper, we aim to harness the power of big data to automatically infer users' demographic attributes based on their purchase data. Typically, demographic prediction can be formalized as a multi-task multi-class prediction problem, i.e., multiple demographic attributes (e.g., gender, age and income) are to be inferred for each user where each attribute may belong to one of N possible classes (N-2). Most previous work on this problem explores different types of features and usually predicts different attributes independently. However, modeling the tasks separately may lose the ability to leverage the correlations among different attributes. Meanwhile, manually defined features require professional knowledge and often suffer from under specification. To address these problems, we propose a novel Structured Neural Embedding (SNE) model to automatically learn the representations from users' purchase data for predicting multiple demographic attributes simultaneously. Experiments are conducted on a real-world retail dataset where five attributes (gender, marital status, income, age, and education level) are to be predicted. The empirical results show that our SNE model can improve the performance significantly compared with state-of-the-art baselines.

Ranking SVM, which formalizes the problem of learning a ranking model as that of learning a binary SVM on preference pairs of documents, is a state-of-the-art ranking model in information retrieval. The dual form solution of Ranking SVM model can be written as a linear combination of the preference pairs, i.e., w = ∑(i,j) αij (xi - xj), where αij denotes the Lagrange parameters associated with each pair (i,j). It is obvious that there exist significant interactions over the document pairs because two preference pairs could share a same document as their items. Thus it is natural to ask if there also exist interactions over the model parameters αij, which we may leverage to propose better ranking model. This paper aims to answer the question. Firstly, we found that there exists a low-rank structure over the Ranking SVM model parameters αij, which indicates that the interactions do exist. Then, based on the discovery, we made a modification on the original Ranking SVM model by explicitly applying a low-rank constraint to the parameters. Specifically, each parameter αij is decomposed as a product of two low-dimensional vectors, i.e., αij = vi, vj, where vectors vi and vj correspond to document i and j, respectively. The learning process, thus, becomes to optimize the modified dual form objective function with respect to the low-dimensional vectors. Experimental results on three LETOR datasets show that our method, referred to as Factorized Ranking SVM, can outperform state-of-the-art baselines including the conventional Ranking SVM.

Next basket recommendation is a crucial task in market basket analysis. Given a user's purchase history, usually a sequence of transaction data, one attempts to build a recommender that can predict the next few items that the user most probably would like. Ideally, a good recommender should be able to explore the sequential behavior (i.e., buying one item leads to buying another next), as well as account for users' general taste (i.e., what items a user is typically interested in) for recommendation. Moreover, these two factors may interact with each other to influence users' next purchase. To tackle the above problems, in this paper, we introduce a novel recommendation approach, namely hierarchical representation model (HRM). HRM can well capture both sequential behavior and users' general taste by involving transaction and user representations in prediction. Meanwhile, the flexibility of applying different aggregation operations, especially nonlinear operations, on representations allows us to model complicated interactions among different factors. Theoretically, we show that our model subsumes several existing methods when choosing proper aggregation operations. Empirically, we demonstrate that our model can consistently outperform the state-of-the-art baselines under different evaluation metrics on real-world transaction data.

In this paper we address the issue of learning a ranking model for search result diversification. In the task, a model concerns with both query-document relevance and document diversity is automatically created with training data. Ideally a diverse ranking model would be designed to meet the criterion of maximal marginal relevance, for selecting documents that have the least similarity to previously selected documents. Also, an ideal learning algorithm for diverse ranking would train a ranking model that could directly optimize the diversity evaluation measures with respect to the training data. Existing methods, however, either fail to model the marginal relevance, or train ranking models by minimizing loss functions that loosely related to the evaluation measures. To deal with the problem, we propose a novel learning algorithm under the framework of Perceptron, which adopts the ranking model that \emph{maximizes marginal relevance at ranking and can optimize any diversity evaluation measure in training}. The algorithm, referred to as PAMM (Perceptron Algorithm using Measures as Margins), first constructs positive and negative diverse rankings for each training query, and then repeatedly adjusts the model parameters so that the margins between the positive and negative rankings are maximized. Experimental results on three benchmark datasets show that PAMM significantly outperforms the state-of-the-art baseline methods.

Inferring a gold-standard ranking over a set of objects, such as documents or images, is a key task to build test collections for various applications like Web search and recommender systems. Crowdsourcing services provide an efficient and inexpensive way to collect judgments via labeling by sets of annotators. We thus study the problem of finding a consensus ranking from crowdsourced judgments. In contrast to conventional rank aggregation methods which minimize the distance between predicted ranking and input judgments from either pointwise or pairwise perspective, we argue that it is critical to consider the distance in a listwise way to emphasize the position importance in ranking. Therefore, we introduce a new listwise approach in this paper, where ranking measure based objective functions are utilized for optimization. In addition, we also incorporate the annotator quality into our model since the reliability of annotators can vary significantly in crowdsourcing. For optimization, we transform the optimization problem to the Linear Sum Assignment Problem, and then solve it by a very efficient algorithm named CrowdAgg guaranteeing the optimal solution. Experimental results on two benchmark data sets from different crowdsourcing tasks show that our algorithm is much more effective, efficient and robust than traditional methods.

This paper presents a practical paper recommender system, which aims to provide personalized research paper recommendations to users within an online social scholar system. As an online recommender system, there are three basic problems we need to tackle: 1) How to formalize and solve the recommendation problem; 2) How to achieve real time recommendation; and 3) How to interact with users. In our work, we take the personalized paper recommendation as a ranking problem with respect to users' research interests, and employ a supervised learning to rank approach to solve the problem. However, most previous learning to rank methods rely on manually labeled training data which are both expensive and limited in size. We propose automatical training data construction by mining the existing large scale academic network, and extract various heterogeneous features for learning. With the learned model, we conduct real time personalized recommendation based on our novel efficient candidate generation approach. In addition, update recommendation is employed to interact with users according to different types of user feedbacks. Finally, we demonstrate the effectiveness of our system by both offline and online evaluation.

Retail transaction data conveys rich preference information on brands and goods from customers. How to mine the transaction data to provide personalized recommendation to customers becomes a critical task for retailers. Previous recommendation methods either focus on the user-product matrix and ignore the transactions, or only use the partial information of transactions, leading to inferior performance in recommendation. Inspired by association rule mining, we introduce association pattern as a basic unit to capture the correlation between products from both intra- and intertransactions. A Probabilistic model over the Association Patterns (PAP for short) is then employed to learn the potential shopping interests and also to provide personalized recommendations. Experimental results on two real world retail data sets show that our proposed method can outperform the state-of-the-art recommendation methods.

When applying learning to rank algorithms in real search applications, noise in human labeled training data becomes an inevitable problem which will affect the performance of the algorithms. Previous work mainly focused on studying how noise affects ranking algorithms and how to design robust ranking algorithms. In our work, we investigate what inherent characteristics make training data robust to label noise. The motivation of our work comes from an interesting observation that a same ranking algorithm may show very different sensitivities to label noise over different data sets. We thus investigate the underlying reason for this observation based on two typical kinds of learning to rank algorithms (i.e.~pairwise and listwise methods) and three different public data sets (i.e.~OHSUMED, TD2003 and MSLR-WEB10K). We find that when label noise increases in training data, it is the \emph{document pair noise ratio} (i.e.~\emph{pNoise}) rather than \emph{document noise ratio} (i.e.~\emph{dNoise}) that can well explain the performance degradation of a ranking algorithm.

Search result diversification has gained attention as a way to tackle the ambiguous or multi-faceted information needs of users. Most existing methods on this problem utilize a heuristic predefined ranking function, where limited features can be incorporated and extensive tuning is required for different settings. In this paper, we address search result diversification as a learning problem, and introduce a novel relational learning-to-rank approach to formulate the task. However, the definitions of ranking function and loss function for the diversification problem are challenging. In our work, we firstly show that diverse ranking is in general a sequential selection process from both empirical and theoretical aspects. On this basis, we define ranking function as the combination of relevance score and diversity score between the current document and those previously selected, and loss function as the likelihood loss of ground truth based on Plackett-Luce model, which can naturally model the sequential generation of a diverse ranking list. Stochastic gradient descent is then employed to conduct the unconstrained optimization, and the prediction of a diverse ranking list is provided by a sequential selection process based on the learned ranking function. The experimental results on the public TREC datasets demonstrate the effectiveness and robustness of our approach.

Short texts are popular on today's web, especially with the emergence of social media. Inferring topics from large scale short texts becomes a critical but challenging task for many content analysis tasks. Conventional topic models such as latent Dirichlet allocation (LDA) and probabilistic latent semantic analysis (PLSA) learn topics from document-level word co-occurrences by modeling each document as a mixture of topics, whose inference suffers from the sparsity of word co-occurrence patterns in short texts. In this paper, we propose a novel way for short text topic modeling, referred as biterm topic model (BTM). BTM learns topics by directly modeling the generation of word co-occurrence patterns (i.e., biterms) in the corpus, making the inference effective with the rich corpus-level information. To cope with large scale short text data, we further introduce two online algorithms for BTM for efficient topic learning. Experiments on real-word short text collections show that BTM can discover more prominent and coherent topics, and significantly outperform the state-of-the-art baselines. We also demonstrate the appealing performance of the two online BTM algorithms on both time efficiency and topic learning.

Recently,`top-k learning to rank' has attracted much attention in the community of information retrieval. The motivation comes from the difficulty in obtaining a full-order ranking list for training, when employing reliable pairwise preference judgment. Inspired by the observation that users mainly care about top ranked search result, top-k learning to rank proposes to utilize top-k ground-truth for training, where only the total order of top k items are provided, instead of a full-order ranking list. However, it is not clear whether the underlying assumption holds, i.e. top-k ground-truth is sufficient for training. In this paper, we propose to study this problem from both empirical and theoretical aspects. Empirically, our experimental results on benchmark datasets LETOR4.0 show that the test performances of both pairwise and listwise ranking algorithms will quickly increase to a stable value, with the growth of k in the top-k ground-truth. Theoretically, we prove that the losses of these typical ranking algorithms in top-k setting are tighter upper bounds of (1--NDCG@k), compared with that in full-order setting. Therefore, our studies reveal that learning on top-k ground-truth is surely sufficient for ranking, which lay a foundation for the new learning to rank framework.

Topic-focused multi-document summarization aims to produce a summary over a set of documents and conveys the most important aspects of a given topic. Most existing extractive methods view the task as a multi-criteria ranking problem over sentences, where relevance, salience and diversity are three typical requirements. However, diversity is a challenging problem as it involves modeling the relationship between sentences during ranking, where traditional methods usually tackle it in a heuristic or implicit way. In this paper, we propose a novel relational learning-to-rank approach (R-LTR) to solve this problem. Relational learning-to-rank is a new learning framework which further incorporates relationships into traditional learning-to-rank in an elegant way. Specifically, the ranking function is defined as the combination of content-based score of individual sentence, and relation-based score between the current sentence and those already selected. On this basis, we propose to learn the ranking function by minimizing the likelihood loss based on Plackett-Luce model, which can naturally model the sequential ranking procedure of candidate sentences. Stochastic gradient descent is then employed to conduct the learning process, and the summary is predicted by the greedy selection procedure based on the learned ranking function. Finally, we conduct extensive experiments on benchmark data sets TAC2008 and TAC2009. Experimental results show that our approach can significantly outperform the state-of-the-art methods from both quantitative and qualitative aspects.

Recommender system has become an effective tool for information filtering, which usually provides the most useful items to users by a top-k ranking list. Traditional recommendation techniques such as Nearest Neighbors (NN) and Matrix Factorization (MF) have been widely used in real recommender systems. However, neither approaches can well accomplish recommendation task since that: (1) most NN methods leverage the neighbor's behaviors for prediction, which may suffer the severe data sparsity problem; (2) MF methods are less sensitive to sparsity, but neighbors' influences on latent factors are not fully explored, since the latent factors are often used independently. To overcome the above problems, we propose a new framework for recommender systems, called collaborative factorization. It expresses the user as the combination of his own factors and those of the neighbors', called collaborative latent factors, and a ranking loss is then utilized for optimization. The advantage of our approach is that it can both enjoy the merits of NN and MF methods. In this paper, we take the logistic loss in RankNet and the likelihood loss in ListMLE as examples, and the corresponding collaborative factorization methods are called CoF-Net and CoF-MLE. Our experimental results on three benchmark datasets show that they are more effective than several state-of-the-art recommendation methods.

It is well recognized that users rely on social media (e.g. Twitter or Digg) to fulfill two common needs (i.e. social need and informational need) that is to keep in touch with their friends in the real world and to have access to information they are interested in. Traditional friend recommendation methods in social media mainly focus on a user's social need, but seldom address their informational need (i.e. suggesting friends that can provide information one may be interested in but have not been able to obtain so far). In this paper, we propose to recommend friends according to the informational utility, which stands for the degree to which a friend satisfies the target user's unfulfilled informational need, called informational friend recommendation. In order to capture users' informational need, we view a post in social media as an item and utilize collaborative filtering techniques to predict the rating for each post. The candidate friends are then ranked according to their informational utility for recommendation. In addition, we also show how to further consider diversity in such recommendations. Experiments on benchmark datasets demonstrate that our approach can significantly outperform the traditional friend recommendation methods under informational evaluation measures.

Learning low dimensional representations of text corpora is critical in many content analysis and data mining applications. It is even more desired and challenging to learn a sparse representation in practice for large scale text modeling. However, traditional probabilistic topic models (PTM) lack a mechanism to directly control the posterior sparsity of the inferred representations; While the emerged non-probabilistic models (NPM) can explicitly control sparsity using sparse constraint like l_1 norm, they convey different limitations in latent representations. To address the existing problems, we propose a novel non-probabilistic topic model for discovering sparse latent representations of large text corpora, referred as group sparse topical coding (GSTC). Our model enjoys both the merits of the PTMs and NPMs. On one hand, GSTC can naturally derive document-level admixture proportions in topic simplex like PTMs, which is useful for semantic analysis, classification or retrieval. On the other hand, GSTC can directly control the sparsity of the inferred representations with group lasso by relaxing the normalization constraint. Moreover, the relaxed non-probabilistic GSTC can be effectively learned using coordinate descent method. Experimental results on benchmark datasets show that GSTC can discover meaningful compact latent representations of documents, and improve the document classification accuracy and time efficiency.

Uncovering the topics within short texts, such as tweets and instant messages, has become an important task for many content analysis applications. However, directly applying conventional topic models (e.g. LDA and PLSA) on such short texts may not work well. The fundamental reason lies in that conventional topic models implicitly capture the document-level word co-occurrence patterns to reveal topics, and thus suffer from the severe data sparsity in short documents. In this paper, we propose a novel way for modeling topics in short texts, referred as biterm topic model (BTM). Specifically, in BTM we learn the topics by directly modeling the generation of word co-occurrence patterns (i.e. biterms) in the whole corpus. The major advantages of BTM are that 1) BTM explicitly models the word co-occurrence patterns to enhance the topic learning; and 2) BTM uses the aggregated patterns in the whole corpus for learning topics to solve the problem of sparse word co-occurrence patterns at document-level. We carry out extensive experiments on real-world short text collections. The results demonstrate that our approach can discover more prominent and coherent topics, and significantly outperform baseline methods on several evaluation metrics. Furthermore, we find that BTM can outperform LDA even on normal texts, showing the potential generality and wider usage of the new topic model.

This paper is concerned with top-k ranking problem, which reflects the fact that people pay more attention to the top ranked objects in real ranking application like information retrieval. A popular approach to top-k ranking problem is based on probabilistic models, such as Luce model and Mallows model. However, whether the sequential generative process described in these models is a suitable way for top-k ranking remains a question. According to the riffled independence factorization proposed in recent literature, which is a natural structural assumption on top-k ranking, we propose a new generative process of top-k ranking data. Our approach decomposes distributions over the top-k ranking into two layers: the first layer describes the relative ordering between the top k objects and the rest n-k objects, and the second layer describes the full ordering on the top k objects. On this basis, we propose a new probabilistic model for top-k ranking problem, called hierarchical ordering model. Specifically, we use three different probabilistic models to describe different generative processes of the first layer, and Luce model to describe the sequential generative process of the second layer, thus we obtain three different specific hierarchical ordering models. We also conduct extensive experiments on benchmark datasets to show that our proposed models can outperform previous models significantly.

Query recommendation plays a critical role in helping users' search. Most existing approaches on query recommendation aim to recommend relevant queries. However, the ultimate goal of query recommendation is to assist users to reformulate queries so that they can accomplish their search task successfully and quickly. Only considering relevance in query recommendation is apparently not directly toward this goal. In this paper, we argue that it is more important to directly recommend queries with high utility, i.e., queries that can better satisfy users' information needs. For this purpose, we propose a novel generative model, referred to as Query Utility Model (QUM), to capture query utility by simultaneously modeling users' reformulation and click behaviors. The experimental results on a publicly released query log show that, our approach is more effective in helping users find relevant search results and thus satisfying their information needs.

In this paper, we propose a novel top-k learning to rank framework, which involves labeling strategy, ranking model and evaluation measure. The motivation comes from the difficulty in obtaining reliable relevance judgments from human assessors when applying learning to rank in real search systems. The traditional absolute relevance judgment method is difficult in both gradation specification and human assessing, resulting in high level of disagreement on judgments. While the pairwise preference judgment, as a good alternative, is often criticized for increasing the complexity of judgment from O(n) to (n log n). Considering the fact that users mainly care about top ranked search results, we propose a novel top-k labeling strategy which adopts the pairwise preference judgment to generate the top k ordering items from n documents (i.e. top-k ground-truth) in a manner similar to that of HeapSort. As a result, the complexity of judgment is reduced to O(n log k). With the top-k ground-truth, traditional ranking models (e.g. pairwise or listwise models) and evaluation measures (e.g. NDCG) no longer fit the data set. Therefore, we introduce a new ranking model, namely FocusedRank, which fully captures the characteristics of the top-k ground-truth. We also extend the widely used evaluation measures NDCG and ERR to be applicable to the top-k ground-truth, referred as κ-NDCG and κ-ERR, respectively. Finally, we conduct extensive experiments on benchmark data collections to demonstrate the efficiency and effectiveness of our top-k labeling strategy and ranking models.