The Visual Question Answering (VQA) task utilizes both visual image and language analysis to answer a textual question with respect to an image. It has been a popular research topic with an increasing number of real-world applications in the last decade. This paper introduces a novel hierarchical integration of vision and language AliceMind-MMU (ALIbaba’s Collection of Encoder-decoders from Machine IntelligeNce lab of Damo academy - MultiMedia Understanding) , which leads to similar or even slightly better results than a human being does on VQA. A hierarchical framework is designed to tackle the practical problems of VQA in a cascade manner including: (1) diverse visual semantics learning for comprehensive image content understanding; (2) enhanced multi-modal pre-training with modality adaptive attention; and (3) a knowledge-guided model integration with three specialized expert modules for the complex VQA task. Treating different types of visual questions with corresponding expertise needed plays an important role in boosting the performance of our VQA architecture up to the human level. An extensive set of experiments and analysis are conducted to demonstrate the effectiveness of the new research work.

In this paper, we present Duplex Conversation, a multi-turn, multimodal spoken dialogue system that enables telephone-based agents to interact with customers like a human. We use the concept of full-duplex in telecommunication to demonstrate what a human-like interactive experience should be and how to achieve smooth turn-taking through three subtasks: user state detection, backchannel selection, and barge-in detection. Besides, we propose semi-supervised learning with multimodal data augmentation to leverage unlabeled data to increase model generalization. Experimental results on three sub-tasks show that the proposed method achieves consistent improvements compared with baselines. We deploy the Duplex Conversation to Alibaba intelligent customer service and share lessons learned in production. Online A/B experiments show that the proposed system can significantly reduce response latency by 50%.

The importance of building text-to-SQL parsers which can be applied to new databases has long been acknowledged, and a critical step to achieve this goal is schema linking, i.e., properly recognizing mentions of unseen columns or tables when generating SQLs. In this work, we propose a novel framework to elicit relational structures from large-scale pre-trained language models (PLMs) via a probing procedure based on Poincaré distance metric, and use the induced relations to augment current graph-based parsers for better schema linking. Compared with commonly-used rule-based methods for schema linking, we found that probing relations can robustly capture semantic correspondences, even when surface forms of mentions and entities differ. Moreover, our probing procedure is entirely unsupervised and requires no additional parameters. Extensive experiments show that our framework sets new state-of-the-art performance on three benchmarks. We empirically verify that our probing procedure can indeed find desired relational structures through qualitative analysis.

Pre-trained language models have contributed significantly to relation extraction by demonstrating remarkable few-shot learning abilities. However, prompt tuning methods for relation extraction may still fail to generalize to those rare or hard patterns. Note that the previous parametric learning paradigm can be viewed as memorization regarding training data as a book and inference as the close-book test. Those long-tailed or hard patterns can hardly be memorized in parameters given few-shot instances. To this end, we regard RE as an open-book examination and propose a new semiparametric paradigm of retrieval-enhanced prompt tuning for relation extraction. We construct an open-book datastore for retrieval regarding prompt-based instance representations and corresponding relation labels as memorized key-value pairs. During inference, the model can infer relations by linearly interpolating the base output of PLM with the non-parametric nearest neighbor distribution over the datastore. In this way, our model not only infers relation through knowledge stored in the weights during training but also assists decision-making by unwinding and querying examples in the open-book datastore. Extensive experiments on benchmark datasets show that our method can achieve state-of-the-art in both standard supervised and few-shot settings

Multimodal Knowledge Graphs (MKGs), which organize visual-text factual knowledge, have recently been successfully applied to tasks such as information retrieval, question answering, and recommendation system. Since most MKGs are far from complete, extensive knowledge graph completion studies have been proposed focusing on the multimodal entity, relation extraction and link prediction. However, different tasks and modalities require changes to the model architecture, and not all images/objects are relevant to text input, which hinders the applicability to diverse real-world scenarios. In this paper, we propose a hybrid transformer with multi-level fusion to address those issues. Specifically, we leverage a hybrid transformer architecture with unified input-output for diverse multimodal knowledge graph completion tasks. Moreover, we propose multi-level fusion, which integrates visual and text representation via coarse-grained prefix-guided interaction and fine-grained correlation-aware fusion modules. We conduct extensive experiments to validate that our MKGformer can obtain SOTA performance on four datasets of multimodal link prediction, multimodal RE, and multimodal NER1. https://github.com/zjunlp/MKGformer.

Recently, pre-training methods have shown remarkable success in task-oriented dialog (TOD) systems. However, most existing pre-trained models for TOD focus on either dialog understanding or dialog generation, but not both. In this paper, we propose SPACE, a novel unified pre-trained dialog model learning from large-scale dialog corpora with limited annotations, which can be effectively fine-tuned on a wide range of downstream dialog tasks. Specifically, SPACE consists of four successive components in a single transformer to maintain a task-flow in TOD systems: (i) a dialog encoding module to encode dialog history, (ii) a dialog understanding module to extract semantic vectors from either user queries or system responses, (iii) a dialog policy module to generate a policy vector that contains high-level semantics of the response, and (iv) a dialog generation module to produce appropriate responses. We design a dedicated pre-training objective for each component. Concretely, we pre-train the dialog encoding module with span mask language modeling to learn contextualized dialog information. To capture the structured dialog semantics, we pre-train the dialog understanding module via a novel tree-induced semi-supervised contrastive learning objective with the help of extra dialog annotations. In addition, we pre-train the dialog policy module by minimizing the ℒ2 distance between its output policy vector and the semantic vector of the response for policy optimization. Finally, the dialog generation model is pre-trained by language modeling. Results show that SPACE achieves state-of-the-art performance on eight downstream dialog benchmarks, including intent prediction, dialog state tracking, and end-to-end dialog modeling. We also show that SPACE has a stronger few-shot ability than existing models under the low-resource setting.

Recently, prompt-tuning has achieved promising results for specific few-shot classification tasks. The core idea of prompt-tuning is to insert text pieces (i.e., templates) into the input and transform a classification task into a masked language modeling problem. However, for relation extraction, determining an appropriate prompt template requires domain expertise, and it is cumbersome and time-consuming to obtain a suitable label word. Furthermore, there exists abundant semantic and prior knowledge among the relation labels that cannot be ignored. To this end, we focus on incorporating knowledge among relation labels into prompt-tuning for relation extraction and propose a Knowledge-aware Prompt-tuning approach with synergistic optimization (KnowPrompt). Specifically, we inject latent knowledge contained in relation labels into prompt construction with learnable virtual type words and answer words. Then, we synergistically optimize their representation with structured constraints. Extensive experimental results on five datasets with standard and low-resource settings demonstrate the effectiveness of our approach. Our code and datasets are available in GitHub1 for reproducibility.

Millions of trademarks were registered last year in China, and thousands of applications are submitted daily. A trademark must be unique in the category it belongs to. Therefore, each new trademark application needs to be checked against all the existing ones in its category. A trademark can be a text string (characters, words or phrases), a figure (symbol or design), or both. In this study, we focus on the textual trademark in Chinese, and propose a model for finding similar trademarks for a given one. This neural network model exploits the semantic, phonetic and visual similarities between two textual trademarks. We evaluated our model based on a dataset that were built from the real trademark application data. Our evaluation shows that the proposed model outperforms other approaches.

Aiming at expanding few-shot relations' coverage in knowledge graphs (KGs), few-shot knowledge graph completion (FKGC) has recently gained more research interests. Some existing models employ a few-shot relation's multi-hop neighbor information to enhance its semantic representation. However, noise neighbor information might be amplified when the neighborhood is excessively sparse and no neighbor is available to represent the few-shot relation. Moreover, modeling and inferring complex relations of one-to-many (1-N), many-to-one (N-1), and many-to-many (N-N) by previous knowledge graph completion approaches requires high model complexity and a large amount of training instances. Thus, inferring complex relations in the few-shot scenario is difficult for FKGC models due to limited training instances. In this paper, we propose a few-shot relational learning with global-local framework to address the above issues. At the global stage, a novel gated and attentive neighbor aggregator is built for accurately integrating the semantics of a few-shot relation's neighborhood, which helps filtering the noise neighbors even if a KG contains extremely sparse neighborhoods. For the local stage, a meta-learning based TransH (MTransH) method is designed to model complex relations and train our model in a few-shot learning fashion. Extensive experiments show that our model outperforms the state-of-the-art FKGC approaches on the frequently-used benchmark datasets NELL-One and Wiki-One. Compared with the strong baseline model MetaR, our model achieves 5-shot FKGC performance improvements of 8.0% on NELL-One and 2.8% on Wiki-One by the metric [email protected]

Recently more and more personal stories about sexual harassment are shared online, mainly inspired by the \#MeToo movement. Safecity is an online forum for victims of sexual harassment to share their personal experience. Previous study applied neural network models to classify the harassment forms of the stories. To uncover patterns of sexual harassment, the extraction of the key elements and the categorization of these stories in different dimensions can be useful as well. In this study, we proposed neural network models to extract key elements including harasser, time, location and trigger words. In addition, we categorized these stories from different dimensions, such as location, time, and harassers' characteristics, including their age range, single/multiple harassers, profession, and relationship with the victims. We further demonstrated that encoding the key element information in the story categorization model can improve its performance. The proposed approaches and analysis would be helpful in automatically filing reports, raising public awareness, making preventing strategies and etc.

It is an important and urgent research problem for decentralized eCommerce services, e.g., eBay, eBid, and Taobao, to detect illegal products, e.g., unclassified pornographic products. However, it is a challenging task as some sellers may utilize and change camouflaged text to deceive the current detection algorithms. In this study, we propose a novel task to dynamically locate the pornographic products from very large product collections. Unlike prior product classification efforts focusing on textual information, the proposed model, BerryPIcking TRee MoDel (BIRD), utilizes both product textual content and buyers' seeking behavior information as berrypicking trees. In particular, the BIRD encodes both semantic information with respect to all branches sequence and the overall latent buyer intent during the whole seeking process. An extensive set of experiments have been conducted to demonstrate the advantage of the proposed model against alternative solutions. To facilitate further research of this practical and important problem, the codes and buyers' seeking behavior data have been made publicly available1.

Various e-commerce platforms produce millions of transactions per day with many transaction disputes. This generates the demand for effective and efficient dispute resolutions for e-commerce transactions. This paper proposes a novel research task of Legal Dispute Judgment (LDJ) prediction for e-commerce transactions, which connects two yet isolated domains, e-commerce data mining and legal intelligence. Different from traditional legal intelligence with the focus on textual evidence of the dispute itself, the new research utilizes multiview information such as past behavior information of seller and buyer as well as textual evidence of the current transaction. The multiview dispute representation is integrated into an innovative multi-task learning framework for predicting the legal result. An extensive set of experiments with a large dispute case dataset collected from a world leading e-commerce platform shows that the proposed model can more accurately characterize a dispute case through buyer, seller, and transaction viewpoints for legal judgment prediction against several alternatives.

TweetSenti is a system for analyzing the sentiment of an entity in tweets. A sentence or tweet may contain multiple entities, and they do not always have the same sentiment polarity. Therefore, it is necessary to detect the sentiment for a specific target entity. This type of target-dependent (entity level) sentiment analysis has become attractive and has been used in many applications, but it is still a challenging task. TweetSenti employs a new approach for detecting the entity level sentiment. Our model splits a sentence into a left context and a right context according to the target entity, and it also exploits two different types of word embeddings to represent a word, the general word embedding and the sentiment specific word embedding. A hybrid neural network is used to capture both the sequence and structure information of the two sides of the target entity. The sequence information is learned by attention-based bi-directional LSTM models. The structure information is captured by multi-context CNN models. Based on this algorithm, we built a web-based application that users can interact with and analyze an entity's sentiment in Twitter at real-time.

Event trigger detection remains a challenging task. Most of previous studies focused on variations of model structures to extract features from the local context of the trigger words. However, few studies focused on the utilization of document level information. In this work, we studied the benefit of exploiting the document level information for event trigger detections in textual data. Two approaches of extracting document features are proposed, and the document features are integrated with the embeddings generated from the local context of the trigger word using a convolutional neural network (CNN) model. Our experiment shows that these two methods both outperform the CNN-based baseline model.

Tasks such as search and recommendation have become increasingly important for E-commerce to deal with the information overload problem. To meet the diverse needs of different users, personalization plays an important role. In many large portals such as Taobao and Amazon, there are a bunch of different types of search and recommendation tasks operating simultaneously for personalization. However, most of current techniques address each task separately. This is suboptimal as no information about users shared across different tasks.

With the explosive growth of smartphone market, mobile applications (short as apps) have recently gained great attention. One mature business paradigm nowadays is that apps can be financially sponsored and appstores can benefit from the distribution of these apps. A good mobile app recommender system should be able to pursue such sponsored profit while maintaining the recommendation quality. We name this scenario as SPONSORED MOBILE APP RECOMMENDATION (SMART), a research topic that has not been fully explored before. To solve this problem, we propose a Similar App Substitution (SAS) principle, stating that among apps with similar properties we can safely select those with high profits. Guided by SAS, we propose a Profit-regularized Kernel Least Square (PKLS) algorithm. In PKLS, multi-kernel representation is applied to capture app properties, the Profit-Per-Download (PPD) of apps serves as regularization, and we design a dynamic learning strategy to update parameters based on user feedbacks. Extensive experiments are conducted with both offline simulation and online deployment on a well-known appstore in China. The results show that our PKLS algorithm achieves better balance between app downloads and appstore profit than the comparison algorithms.

In the 'Big Data' era, many real-world applications like search involve the ranking problem for a large number of items. It is important to obtain effective ranking results and at the same time obtain the results efficiently in a timely manner for providing good user experience and saving computational costs. Valuable prior research has been conducted for learning to efficiently rank like the cascade ranking (learning) model, which uses a sequence of ranking functions to progressively filter some items and rank the remaining items. However, most existing research of learning to efficiently rank in search is studied in a relatively small computing environments with simulated user queries.

eCommerce Information Retrieval has received little attention in the academic literature, yet it is an essential component of some of the largest web sites (such as eBay, Amazon, Airbnb, Alibaba, Taobao, Target, Facebook, and others). SIGIR has for several years seen sponsorship from these kinds of organizations, who clearly value the importance of research into Information Retrieval. This workshop brings together researchers and practitioners of eCommerce IR to discuss topics unique to it, to set a research agenda, and to examine how to build a dataset for research into this fascinating topic. eCommerce IR is ripe for research and has a unique set of problems. For example, in eCommerce search there may be no hypertext links between documents (products); there is a click stream, but more importantly, there is often a buy stream. eCommerce problems are wide in scope and range from user interaction modalities (the kinds of search seen in when buying are different from those of web-page search (i.e. it is not clear how shopping and buying relate to the standard web-search interaction models)) through to dynamic updates of a rapidly changing collection on auction sites, and the experienceness of some products (such as Airbnb bookings).

Push notification is a key component for E-commerce mobile applications, which has been extensively used for user growth and engagement. The effectiveness of the push notification is generally measured by message open rate. A push message can contain a recommended product, a shopping news and etc., but often only one or two items can be shown in the push message due to the limit of display space. This paper proposes a mixture model approach for predicting push message open rate for a post-purchase complementary product recommendation task. The mixture model is trained to learn latent prediction contexts, which are determined by user and item profiles, and then make open rate predictions accordingly. The item with the highest predicted open rate is then chosen to be included in the push notification message for each user. The parameters of the mixture model are optimized using an EM algorithm. A set of experiments are conducted to evaluate the proposed method live with a popular E-Commerce mobile app. The results show that the proposed method is superior than several existing solutions by a significant margin.

Query classification is an important part of exploring the characteristics of web queries. Existing studies are mainly based on Broder's classification scheme and classify user queries into navigational, informational, and transactional categories according to users' information needs. In this article, we present a novel classification scheme from the perspective of queries' temporal patterns. Queries' temporal patterns are inherent time series patterns of the search volumes of queries that reflect the evolution of the popularity of a query over time. By analyzing the temporal patterns of queries, search engines can more deeply understand the users' search intents and thus improve performance. Furthermore, we extract three groups of features based on the queries' search volume time series and use a support vector machine (SVM) to automatically detect the temporal patterns of user queries. Extensive experiments on the Million Query Track data sets of the Text REtrieval Conference (TREC) demonstrate the effectiveness of our approach.

Query expansion (QE) is a well known technique to improve retrieval effectiveness, which expands original queries with extra terms that are predicted to be relevant. A recent trend in the literature is Supervised Query Expansion (SQE), where supervised learning is introduced to better select expansion terms. However, an important but neglected issue for SQE is its efficiency, as applying SQE in retrieval can be much more time-consuming than applying Unsupervised Query Expansion (UQE) algorithms. In this paper, we point out that the cost of SQE mainly comes from term feature extraction, and propose a Two-stage Feature Selection framework (TFS) to address this problem. The first stage is adaptive expansion decision, which determines if a query is suitable for SQE or not. For unsuitable queries, SQE is skipped and no term features are extracted at all, which reduces the most time cost. For those suitable queries, the second stage is cost constrained feature selection, which chooses a subset of effective yet inexpensive features for supervised learning. Extensive experiments on four corpora (including three academic and one industry corpus) show that our TFS framework can substantially reduce the time cost for SQE, while maintaining its effectiveness.

Along with the exponential growth on markets of mobile Applications (apps), comes the serious public concern about the security and privacy issues. Therefore automatic app risk assessment becomes increasingly important to support users with useful evidences for their decisions. User comment provides a unique perspective from actual user experience, and should be considered valuable information source for risk assessment for mobile apps. In this paper, we provide a novel perspective to view the risk assessment of an app from its user comments as a crowdsourcing problem and adopt ranking model as the evaluation method. We develop a co-training scheme to amalgamate feature learning and learning to rank models. Experiments conducted on two different real-world datasets show substantial performance improvements (i.e., 6%–7%) over the state-of- the-art methods.

This paper studies Cumulative Citation Recommendation (CCR) for Knowledge Base Acceleration (KBA). The CCR task aims to detect potential citations of a set of target entities with priorities from a volume of temporally-ordered stream corpus. Previous approaches for CCR that build an individual relevance model for each entity fail to handle unseen entities without annotation. A baseline solution is to build a global entity-unspecific model for all entities regardless of the relationship information among entities, which cannot guarantee to achieve satisfactory result for each entity. In this paper, we propose a novel entity class-dependent discriminative mixture model by introducing a latent entity class layer to model the correlations between entities and latent entity classes. The model can better adjust to different types of entities and achieve better performance when dealing with a broad range of entities. An extensive set of experiments has been conducted on TREC-KBA-2013 dataset, and the experimental results demonstrate that the proposed model can achieve the state-of-the-art performance.

Sentiment analysis of such opinionated online texts as reviews and comments has received increasingly close attention, yet most of the work is intended to deal with the detection of authors’ emotion. In contrast, this article presents our study of the social emotion detection problem, the objective of which is to identify the evoked emotions of readers by online documents such as news articles. A novel Latent Discriminative Model (LDM) is proposed for this task. LDM works by introducing intermediate hidden variables to model the latent structure of input text corpora. To achieve this, it defines a joint distribution over emotions and latent variables, conditioned on the observed text documents. Moreover, we assume that social emotions are not independent but correlated with one another, and the dependency of them is capable of providing additional guidance to LDM in the training process. The inclusion of this emotional dependency into LDM gives rise to a new Emotional Dependency-based LDM (eLDM). We evaluate the proposed models through a series of empirical evaluations on two real-world corpora of news articles. Experimental results verify the effectiveness of LDM and eLDM in social emotion detection.

Similarity search, or finding approximate nearest neighbors, is an important technique in various large scale information retrieval applications such as document retrieval. Many recent research demonstrate that hashing methods can achieve promising results for large scale similarity search due to its computational and memory efficiency. However, most existing hashing methods ignore the hidden semantic structure of documents but only use the keyword features (e.g., tf-idf) in hashing codes learning. This paper proposes a novel sparse semantic hashing (SpSH) approach that explores the hidden semantic representation of documents in learning their corresponding hashing codes. In particular, a unified framework is designed for ensuring the hidden semantic structure among the documents by a sparse coding model, while at the same time preserving the document similarity via graph Laplacian. An iterative coordinate descent procedure is then proposed for solving the optimization problem. Extensive experiments on two large scale datasets demonstrate the superior performance of the proposed research over several state-of-the-art hashing methods.

Information retrieval (IR) and information privacy/security are two fast-growing computer science disciplines. There are many synergies and connections between these two disciplines. However, there have been very limited efforts to connect the two important disciplines. On the other hand, due to lack of mature techniques in privacy-preserving IR, concerns about information privacy and security have become serious obstacles that prevent valuable user data to be used in IR research such as studies on query logs, social media, tweets, sessions, and medical record retrieval. This privacy-preserving IR workshop aims to spur research that brings together the research fields of IR and privacy/security, and research that mitigates privacy threats in information retrieval by constructing novel algorithms and tools that enable web users to better understand associated privacy risks.

Similarity search is an important problem in many large scale applications such as image and text retrieval. Hashing method has become popular for similarity search due to its fast search speed and low storage cost. Recent research has shown that hashing quality can be dramatically improved by incorporating supervised information, e.g. semantic tags/labels, into hashing function learning. However, most existing supervised hashing methods can be regarded as passive methods, which assume that the labeled data are provided in advance. But in many real world applications, such supervised information may not be available.

Recommender systems usually need to compare a large number of items before users' most preferred ones can be found This process can be very costly if recommendations are frequently made on large scale datasets. In this paper, a novel hashing algorithm, named Preference Preserving Hashing (PPH), is proposed to speed up recommendation. Hashing has been widely utilized in large scale similarity search (e.g. similar image search), and the search speed with binary hashing code is significantly faster than that with real-valued features. However, one challenge of applying hashing to recommendation is that, recommendation concerns users' preferences over items rather than their similarities. To address this challenge, PPH contains two novel components that work with the popular matrix factorization (MF) algorithm. In MF, users' preferences over items are calculated as the inner product between the learned real-valued user/item features. The first component of PPH constrains the learning process, so that users' preferences can be well approximated by user-item similarities. The second component, which is a novel quantization algorithm,generates the binary hashing code from the learned real-valued user/item features. Finally, recommendation can be achieved efficiently via fast hashing code search. Experiments on three real world datasets show that the recommendation speed of the proposed PPH algorithm can be hundreds of times faster than original MF with real-valued features, and the recommendation accuracy is significantly better than previous work of hashing for recommendation.

In many online news services, users often write comments towards news in subjective emotions such as sadness, happiness or anger. Knowing such emotions can help understand the preferences and perspectives of individual users, and therefore may facilitate online publishers to provide more relevant services to users. Although building emotion classifiers is a practical task, it highly depends on sufficient training data that is not easy to be collected directly and the manually labeling work of comments can be quite labor intensive. Also, online news has different domains, which makes the problem even harder as different word distributions of the domains require different classifiers with corresponding distinct training data.

Predicting student performance is an important task for many core problems in intelligent tutoring systems. This paper proposes a set of novel probabilistic latent class models for the task. The most effective probabilistic model utilizes all available information about the educational content and users/students to jointly identify hidden classes of students and educational content that share similar characteristics, and to learn a specialized and fine-grained regression model for each latent educational content and student class. Experiments carried out on large-scale real-world datasets demonstrate the advantages of the proposed probabilistic latent class models.

Tags have been popularly utilized in many applications with image and text data for better managing, organizing and searching for useful information. Tag completion provides missing tag information for a set of existing images or text documents while tag prediction recommends tag information for any new image or text document. Valuable prior research has focused on improving the accuracy of tag completion and prediction, but limited research has been conducted for the efficiency issue in tag completion and prediction, which is a critical problem in many large scale real world applications.

Similarity search, or finding approximate nearest neighbors, is an important technique for many applications. Many recent research demonstrate that hashing methods can achieve promising results for large scale similarity search due to its computational and memory efficiency.

Entity disambiguation is an important step in many information retrieval applications. This paper proposes new research for entity disambiguation with the focus of name disambiguation in digital libraries. In particular, pairwise similarity is first learned for publications that share the same author name string (ANS) and then a novel Hierarchical Agglomerative Clustering approach with Adaptive Stopping Criterion (HACASC) is proposed to adaptively cluster a set of publications that share a same ANS to individual clusters of publications with different author identities. The HACASC approach utilizes a mixture of kernel ridge regressions to intelligently determine the threshold in clustering. This obtains more appropriate clustering granularity than non-adaptive stopping criterion. We conduct a large scale empirical study with a dataset of more than 2 million publication record pairs to demonstrate the advantage of the proposed HACASC approach.

Prior research in resource selection for federated search mainly focused on selecting a small number of information sources that are most relevant to a user query. However, result novelty and diversification are largely unexplored, which does not reflect the various kinds of information needs of users in real world applications.

It is an important research problem to design efficient and effective solutions for large scale similarity search. One popular strategy is to represent data examples as compact binary codes through semantic hashing, which has produced promising results with fast search speed and low storage cost. Many existing semantic hashing methods generate binary codes for documents by modeling document relationships based on similarity in a keyword feature space. Two major limitations in existing methods are: (1) Tag information is often associated with documents in many real world applications, but has not been fully exploited yet; (2) The similarity in keyword feature space does not fully reflect semantic relationships that go beyond keyword matching.

Most of the current recommender systems heavily rely on explicit user feedback such as ratings on items to model users' interests. However, in many applications, it is very hard to collect the explicit feedback, while implicit feedback such as user clicks may be more available. Furthermore, it is often more suitable for many recommender systems to address a ranking problem than a rating predicting problem. This paper proposes a latent pairwise preference learning (LPPL) approach for recommendation with implicit feedback. LPPL directly models user preferences with respect to a set of items rather than the rating scores on individual items, which are modeled with a set of features by analyzing clickthrough data available in many real-world recommender systems. The LPPL approach models both the latent variables of group structure of users and the pairwise preferences simultaneously. We conduct experiments on the testbed from a real-world recommender system and demonstrate that the proposed approach can effectively improve the recommendation performance against several baseline algorithms.

Result merging is an important research problem in federated search for merging documents retrieved from multiple ranked lists of selected information sources into a single list. The state-of-the-art result merging algorithms such as Semi-Supervised Learning (SSL) and Sample-Agglomerate Fitting Estimate (SAFE) try to map document scores retrieved from different sources to comparable scores according to a single centralized retrieval algorithm for ranking those documents. Both SSL and SAFE arbitrarily select a single centralized retrieval algorithm for generating comparable document scores, which is problematic in a heterogeneous federated search environment, since a single centralized algorithm is often suboptimal for different information sources. Based on this observation, this paper proposes a novel approach for result merging by utilizing multiple centralized retrieval algorithms. One simple approach is to learn a set of combination weights for multiple centralized retrieval algorithms (e.g., logistic regression) to compute comparable document scores. The paper shows that this simple approach generates suboptimal results as it is not flexible enough to deal with heterogeneous information sources. A mixture probabilistic model is thus proposed to learn more appropriate combination weights with respect to different types of information sources with some training data. An extensive set of experiments on three datasets have proven the effectiveness of the proposed new approach.

With the rapid growth of online news services, users can actively respond to online news by making comments. Users often express subjective emotions in comments such as sadness, surprise and anger. Such emotions can help understand the preferences and perspectives of individual users, and therefore may facilitate online publishers to provide users with more relevant services. This paper tackles the task of predicting emotions for the comments of online news. To the best of our knowledge, this is the first research work for addressing the task. In particular, this paper proposes a novel Meta classification approach that exploits heterogeneous information sources such as the content of the comments and the emotion tags of news articles generated by users. The experiments on two datasets from online news services demonstrate the effectiveness of the proposed approach.

This paper presents a novel opinion mining research problem, which is called Contrastive Opinion Modeling (COM). Given any query topic and a set of text collections from multiple perspectives, the task of COM is to present the opinions of the individual perspectives on the topic, and furthermore to quantify their difference. This general problem subsumes many interesting applications, including opinion summarization and forecasting, government intelligence and cross-cultural studies. We propose a novel unsupervised topic model for contrastive opinion modeling. It simulates the generative process of how opinion words occur in the documents of different collections. The ad hoc opinion search process can be efficiently accomplished based on the learned parameters in the model. The difference of perspectives can be quantified in a principled way by the Jensen-Shannon divergence among the individual topic-opinion distributions. An extensive set of experiments have been conducted to evaluate the proposed model on two datasets in the political domain: 1) statement records of U.S. senators; 2) world news reports from three representative media in U.S., China and India, respectively. The experimental results with both qualitative and quantitative analysis have shown the effectiveness of the proposed model.

Abstract Rapid increase in global competition demands increased protection of intellectual property rights and underlines the importance of patents as major intellectual property documents. Prior art patent search is the task of identifying related patents for a given patent file, and is an essential step in judging the validity of a patent application. This article proposes an automated query generation and postprocessing method for prior art patent search. The proposed approach first constructs structured queries by combining terms extracted from different fields of a query patent and then reranks the retrieved patents by utilizing the International Patent Classification (IPC) code similarities between the query patent and the retrieved patents along with the retrieval score. An extensive set of empirical results carried out on a large‐scale, real‐world dataset shows that utilizing 20 or 30 query terms extracted from all fields of an original query patent according to their log(tf)idf values helps form a representative search query out of the query patent and is found to be more effective than is using any number of query terms from any single field. It is shown that combining terms extracted from different fields of the query patent by giving higher importance to terms extracted from the abstract, claims, and description fields than to terms extracted from the title field is more effective than treating all extracted terms equally while forming the search query. Finally, utilizing the similarities between the IPC codes of the query patent and retrieved patents is shown to be beneficial to improve the effectiveness of the prior art search.

Transfer learning has been proposed to address the problem of scarcity of labeled data in the target domain by leveraging the data from the source domain. In many real world applications, data is often represented from different perspectives, which correspond to multiple views. For example, a web page can be described by its contents and its associated links. However, most existing transfer learning methods fail to capture the multi-view {nature}, and might not be best suited for such applications.

Most traditional supervised learning methods are developed to learn a model from labeled examples and use this model to classify the unlabeled ones into the same label space predefined by the models. However, in many real world applications, the label spaces for both the labeled/training and unlabeled/testing examples can be different. To solve this problem, this paper proposes a novel notion of Serendipitous Learning (SL), which is defined to address the learning scenarios in which the label space can be enlarged during the testing phase. In particular, a large margin approach is proposed to solve SL. The basic idea is to leverage the knowledge in the labeled examples to help identify novel/unknown classes, and the large margin formulation is proposed to incorporate both the classification loss on the examples within the known categories, as well as the clustering loss on the examples in unknown categories. An efficient optimization algorithm based on CCCP and the bundle method is proposed to solve the optimization problem of the large margin formulation of SL. Moreover, an efficient online learning method is proposed to address the issue of large scale data in online learning scenario, which has been shown to have a guaranteed learning regret. An extensive set of experimental results on two synthetic datasets and two datasets from real world applications demonstrate the advantages of the proposed method over several other baseline algorithms. One limitation of the proposed method is that the number of unknown classes is given in advance. It may be possible to remove this constraint if we model it by using a non-parametric way. We also plan to do experiments on more real world applications in the future.

Display advertising is a multi-billion dollar industry where advertisers promote their products to users by having publishers display their advertisements on popular Web pages. An important problem in online advertising is how to forecast the number of user visits for a Web page during a particular period of time. Prior research addressed the problem by using traditional time-series forecasting techniques on historical data of user visits; (e.g., via a single regression model built for forecasting based on historical data for all Web pages) and did not fully explore the fact that different types of Web pages have different patterns of user visits.

Identifying similar professionals is an important task for many core services in professional social networks. Information about users can be obtained from heterogeneous information sources, and different sources provide different insights on user similarity.

Expert search has made rapid progress in modeling, algorithms and evaluations in the recent years. However, there is very few work on analyzing how users interact with expert search systems. In this paper, we conduct analysis of an expert search query log. The aim is to understand the special characteristics of expert search usage. To the best of our knowledge, this is one of the earliest work on expert search query log analysis. We find that expert search users generally issue shorter queries, more common queries, and use more advanced search features, with fewer queries in a session, than general Web search users do. This study explores a new research direction in expert search by analyzing and exploiting query logs.

Result merging is an important step in federated search to merge the documents returned from multiple source-specific ranked lists for a user query. Previous result merging methods such as Semi-Supervised Learning (SSL) and Sample- Agglomerate Fitting Estimate (SAFE) use regression methods to estimate global document scores from document ranks in individual ranked lists. SSL relies on overlapping documents that exist in both individual ranked lists and a centralized sample database. SAFE goes a step further by using both overlapping documents with accurate rank information and documents with estimated rank information for regression. However, existing methods do not distinguish the accurate rank information from the estimated information. Furthermore, all documents are assigned equal weights in regression while intuitively, documents in the top should carry higher weights. This paper proposes a weighted curve fitting method for result merging in federated search. The new method explicitly models the importance of information from overlapping documents over non-overlapping ones. It also weights documents at different positions differently. Empirically results on two datasets clearly demonstrate the advantage of the proposed algorithm.

In recent years, we have witnessed successful application of machine learning techniques to a wide range of information retrieval problems, including Web search engines, recommendation systems, online advertising, etc. It is thus critical for researchers in the information retrieval community to understand the core machine learning techniques. In order to accommodate audiences with different levels of understanding of machine learning, we divide this tutorial into two sessions: the first session will focus on basic machine learning concepts and tools; in the second session, we will introduce more advanced topics in machine learning, and will present recent developments in machine learning and its application to information retrieval. Each season is self-contained.

Similarity search applications with a large amount of text and image data demands an efficient and effective solution. One useful strategy is to represent the examples in databases as compact binary codes through semantic hashing, which has attracted much attention due to its fast query/search speed and drastically reduced storage requirement. All of the current semantic hashing methods only deal with the case when each example is represented by one type of features. However, examples are often described from several different information sources in many real world applications. For example, the characteristics of a webpage can be derived from both its content part and its associated links.

Document clustering is a popular research topic, which aims to partition documents into groups of similar objects (i.e., clusters), and has been widely used in many applications such as automatic topic extraction, document organization and filtering. As a recently proposed concept, Universum is a collection of "non-examples" that do not belong to any concept/cluster of interest. This paper proposes a novel document clustering technique -- Document Clustering with Universum, which utilizes the Universum examples to improve the clustering performance. The intuition is that the Universum examples can serve as supervised information and help improve the performance of clustering, since they are known not belonging to any meaningful concepts/clusters in the target domain. In particular, a maximum margin clustering method is proposed to model both target examples and Universum examples for clustering. An extensive set of experiments is conducted to demonstrate the effectiveness and efficiency of the proposed algorithm.

Generative models such as statistical language modeling have been widely studied in the task of expert search to model the relationship between experts and their expertise indi-cated in supporting documents. On the other hand, dis-criminative models have received little attention in expert search research, although they have been shown to outper-form generative models in many other information retrieval and machine learning applications. In this paper, we propose a principled relevance-based discriminative learning frame-work for expert search and derive specific discriminative models from the framework. Compared with the state-of-the-art language models for expert search, the proposed re-search can naturally integrate various document evidence and document-candidate associations into a single model without extra modeling assumptions or effort. An extensive set of experiments have been conducted on two TREC En-terprise track corpora (i.e., W3C and CERC) to demonstrate the effectiveness and robustness of the proposed framework.

Resource selection is an important task in Federated Search to select a small number of most relevant information sources. Current resource selection algorithms such as GlOSS, CORI, ReDDE, Geometric Average and the recent classification-based method focus on the evidence of individual information sources to determine the relevance of available sources. Current algorithms do not model the important relationship information among individual sources. For example, an information source tends to be relevant to a user query if it is similar to another source with high probability of being relevant. This paper proposes a joint probabilistic classification model for resource selection. The model estimates the probability of relevance of information sources in a joint manner by considering both the evidence of individual sources and their relationship. An extensive set of experiments have been conducted on several datasets to demonstrate the advantage of the proposed model.

This article presents two probabilistic models for answering ranking in the multilingual question-answering (QA) task, which finds exact answers to a natural language question written in different languages. Although some probabilistic methods have been utilized in traditional monolingual answer-ranking, limited prior research has been conducted for answer-ranking in multilingual question-answering with formal methods. This article first describes a probabilistic model that predicts the probabilities of correctness for individual answers in an independent way. It then proposes a novel probabilistic method to jointly predict the correctness of answers by considering both the correctness of individual answers as well as their correlations. As far as we know, this is the first probabilistic framework that proposes to model the correctness and correlation of answer candidates in multilingual question-answering and provide a novel approach to design a flexible and extensible system architecture for answer selection in multilingual QA. An extensive set of experiments were conducted to show the effectiveness of the proposed probabilistic methods in English-to-Chinese and English-to-Japanese cross-lingual QA, as well as English, Chinese, and Japanese monolingual QA using TREC and NTCIR questions.

Federated text search provides a unified search interface for multiple search engines of distributed text information sources. Resource selection is an important component for federated text search, which selects a small number of information sources that contain the largest number of relevant documents for a user query. Most prior research of resource selection focused on selecting information sources by analyzing static information of available information sources that is sampled in the offline manner. On the other hand, most prior research ignored a large amount of valuable information like the results from past queries. This paper proposes a new resource selection technique (which is called qSim) that utilizes the search results of past queries for estimating the utilities of available information sources for a specific user query. Experiment results demonstrate the effectiveness of the new resource selection algorithm.

Modeling the response time of search engines is an important task for many applications such as resource selection in federated text search. Limited research has been conducted to address this task. Prior research calculated the search response time of all queries in the same way either with the average response time of several sample queries or with a single probability distribution, which is irrelevant to the characteristics of queries. However, the search response time may vary a lot for different types of queries. This paper proposes a novel query-specific and source-specific approach to model search response time. Some training data is acquired by measuring the search response time of some sample queries from a search engine. Then, a query-specific model is estimated with the training data and their corresponding response times by utilizing Ridge Regression. The obtained model can be used to predict search response times for new queries. A set of empirical studies are conducted to show the effectiveness of the proposed method.

Similar document detection plays important roles in many applications, such as file management, copyright protection, and plagiarism prevention. Existing protocols assume that the contents of files stored on a server (or multiple servers) are directly accessible. This assumption limits more practical applications, e.g., detecting plagiarized documents between two conferences, where submissions are confidential. We propose novel protocols to detect similar documents between two entities where documents cannot be openly shared with each other. We also conduct experiments to show the practical value of the proposed protocols.

The approach of using passage-level evidence for document retrieval has shown mixed results when it is applied to a variety of test beds with different characteristics. One main reason of the inconsistent performance is that there exists no unified framework to model the evidence of individual passages within a document. This paper proposes two probabilistic models to formally model the evidence of a set of top ranked passages in a document. The first probabilistic model follows the retrieval criterion that a document is relevant if any passage in the document is relevant, and models each passage independently. The second probabilistic model goes a step further and incorporates the similarity correlations among the passages. Both models are trained in a discriminative manner. Furthermore, we present a combination approach to combine the ranked lists of document retrieval and passage-based retrieval.

Federated search is the task of retrieving relevant documents from different information resources. One of the main research problems in federated search is to combine the results from different sources into a single ranked list. Recent work proposed a regression based method to download some documents from each ranked list of the different sources, calculated comparable scores for the documents and estimated mapping functions that transform source-specific scores into comparable scores. Experiments have shown that downloading more documents improves the accuracy of results merging. However downloading more documents increases the computation and communication costs.

Many information sources contain information that can only be accessed through search-specific search engines. Federated search provides search solutions of this type of hidden information that cannot be searched by conventional search engines. In many scenarios of federated search, such as the search among health care providers or among intelligence agencies, an individual information source does not want to disclose the source of the search results to users or other sources. Therefore, this paper proposes a two-step federated search protocol that protects the privacy of information sources. As far as we know, this is the first attempt to address the research problem of protecting source privacy in federated text search.

Graphical models have been applied to various information retrieval and natural language processing tasks in the recent literature. In this paper, we apply a probabilistic graphical model for answer ranking in question answering. This model estimates the joint probability of correctness of all answer candidates, from which the probability of correctness of an individual candidate can be inferred. The joint prediction model can estimate both the correctness of individual answers as well as their correlations, which enables a list of accurate and comprehensive answers. This model was compared with a logistic regression model which directly estimates the probability of correctness of each individual answer candidate. An extensive set of empirical results based on TREC questions demonstrates the effectiveness of the joint model for answer ranking. Furthermore, we combine the joint model with the logistic regression model to improve the efficiency and accuracy of answer ranking.

Abstract Federated search and distributed information retrieval systems provide a single user interface for searching multiple full‐text search engines. They have been an active area of research for more than a decade, but in spite of their success as a research topic, they are still rare in operational environments. This article discusses a prototype federated search system developed for the U.S. government's FedStats Web portal, and the issues addressed in adapting research solutions to this operational environment. A series of experiments explore how well prior research results, parameter settings, and heuristics apply in the FedStats environment. The article concludes with a set of lessons learned from this technology transfer effort, including observations about search engine quality in the “real world.”

Federated search links multiple search engines into a single, virtual search system. Most prior research of federated search focused on selecting search engines that have the most relevant contents, but ignored the retrieval effectiveness of individual search engines. This omission can cause serious problems when federating search engines of different qualities.This paper proposes a federated search technique that uses utility maximization to model the retrieval effectiveness of each search engine in a federated search environment. The new algorithm ranks the available resources by explicitly estimating the amount of relevant material that each resource can return, instead of the amount of relevant material that each resource contains. An extensive set of experiments demonstrates the effectiveness of the new algorithm.

This paper presents a unified utility framework for resource selection of distributed text information retrieval. This new framework shows an efficient and effective way to infer the probabilities of relevance of all the documents across the text databases. With the estimated relevance information, resource selection can be made by explicitly optimizing the goals of different applications. Specifically, when used for database recommendation, the selection is optimized for the goal of high-recall (include as many relevant documents as possible in the selected databases); when used for distributed document retrieval, the selection targets the high-precision goal (high precision in the final merged list of documents). This new model provides a more solid framework for distributed information retrieval. Empirical studies show that it is at least as effective as other state-of-the-art algorithms.

Collaborative filtering and content-based filtering are two types of information filtering techniques. Combining these two techniques can improve the recommendation effectiveness. The main problem with previous research is that the content information and the rating information are not combined in an integrated way. This paper presents a unified probabilistic framework that allows the mutual interaction between these two types of information. Experiments have shown that the new unified filtering algorithm outperforms a pure collaborative filtering approach, a pure content-based filtering approach and another unified filtering algorithm.

Collaborative filtering identifies information interest of a particular user based on the information provided by other similar users. The memory-based approaches for collaborative filtering (e.g., Pearson correlation coefficient approach) identify the similarity between two users by comparing their ratings on a set of items. In these approaches, different items are weighted either equally or by some predefined functions. The impact of rating discrepancies among different users has not been taken into consideration. For example, an item that is highly favored by most users should have a smaller impact on the user-similarity than an item for which different types of users tend to give different ratings. Even though simple weighting methods such as variance weighting try to address this problem, empirical studies have shown that they are ineffective in improving the performance of collaborative filtering. In this paper, we present an optimization algorithm to automatically compute the weights for different items based on their ratings from training users. More specifically, the new weighting scheme will create a clustered distribution for user vectors in the item space by bringing users of similar interests closer and separating users of different interests more distant. Empirical studies over two datasets have shown that our new weighting scheme substantially improves the performance of the Pearson correlation coefficient method for collaborative filtering.

The goal of collaborative filtering is to make recommendations for a test user by utilizing the rating information of users who share interests similar to the test user. Because ratings are determined not only by user interests but also the rating habits of users, it is important to normalize ratings of different users to the same scale. In this paper, we compare two different normalization strategies for user ratings, namely the Gaussian normalization method and the decoupling normalization method. Particularly, we incorporated these two rating normalization methods into two collaborative filtering algorithms, and evaluated their effectiveness on the EachMovie dataset. The experiment results have shown that the decoupling method for rating normalization is more effective than the Gaussian normalization method in improving the performance of collaborative filtering algorithms.

No abstract available.

In this paper, we describe a new model for collaborative filtering. The motivation of this work comes from the fact that two users with very similar preferences on items may have very different rating schemes. For example, one user may tend to assign a higher rating to all items than another user. Unlike previous models of collaborative filtering, which determine the similarity between two users only based on their rating performance, our model treats the user's preferences on items separately from the user's rating scheme. More specifically, for each user, we build two separate models: a preference model capturing which items are favored by the user and a rating model capturing how the user would rate an item given the preference information. The similarity of two users is computed based on the underlying preference model, instead of the surface ratings. We compare the new model with several representative previous approaches on two data sets. Experiment results show that the new model outperforms all the previous approaches that are tested consistently on both data sets.

Prior research under a variety of conditions has shown the CORI algorithm to be one of the most effective resource selection algorithms, but the range of database sizes studied was not large. This paper shows that the CORI algorithm does not do well in environments with a mix of "small" and "very large" databases. A new resource selection algorithm is proposed that uses information about database sizes as well as database contents. We also show how to acquire database size estimates in uncooperative environments as an extension of the query-based sampling used to acquire resource descriptions. Experiments demonstrate that the database size estimates are more accurate for large databases than estimates produced by a competing method; the new resource ranking algorithm is always at least as effective as the CORI algorithm; and the new algorithm results in better document rankings than the CORI algorithm.

The proliferation of searchable text databases on local area networks and the Internet causes the problem of finding information that may be distributed among many disjoint text databases ( distributed information retrieval ). How to merge the results returned by selected databases is an important subproblem of the distributed information retrieval task. Previous research assumed that either resource providers cooperate to provide normalizing statistics or search clients download all retrieved documents and compute normalized scores without cooperation from resource providers.This article presents a semisupervised learning solution to the result merging problem. The key contribution is the observation that information used to create resource descriptions for resource selection can also be used to create a centralized sample database to guide the normalization of document scores returned by different databases. At retrieval time, the query is sent to the selected databases, which return database-specific document scores, and to a centralized sample database , which returns database-independent document scores. Documents that have both a database-specific score and a database-independent score serve as training data for learning to normalize the scores of other documents. An extensive set of experiments demonstrates that this method is more effective than the well-known CORI result-merging algorithm under a variety of conditions.

Statistical language models have been proposed recently for several information retrieval tasks, including the resource selection task in distributed information retrieval. This paper extends the language modeling approach to integrate resource selection, ad-hoc searching, and merging of results from different text databases into a single probabilistic retrieval model. This new approach is designed primarily for Intranet environments, where it is reasonable to assume that resource providers are relatively homogeneous and can adopt the same kind of search engine. Experiments demonstrate that this new, integrated approach is at least as effective as the prior state-of-the-art in distributed IR.

Information retrieval using meta data can be traced back to the early age of IR where documents are represented by the controlled vocabulary. In this paper, we explore the usage of meta-data information under the framework of language model. We present a new language model that is able to take advantage of the category information for documents to improve the retrieval accuracy. We compare the new language model with the traditional language model over the TREC4 dataset where the collection information for documents is obtained using the k-means clustering method. The new language model outperforms the traditional language model, which verifies our statement.

This paper addresses the problem of merging results obtained from different databases and search engines in a distributed information retrieval environment. The prior research on this problem either assumed the exchange of statistics necessary for normalizing scores (cooperative solutions) or is heuristic. Both approaches have disadvantages. We show that the problem in uncooperative environments is simpler when viewed as a component of a distributed IR system that uses query-based sampling to create resource descriptions. Documents sampled for creating resource descriptions can also be used to create a sample centralized index, and this index is a source of training data for adaptive results merging algorithms. A variety of experiments demonstrate that this new approach is more effective than a well-known alternative, and that it allows query-by-query tuning of the results merging function.

In this paper, we present a new method of using statistical models to estimate readability [1]. Language Model is used to capture the content information. It is combined with linguistic feature model by a linear form. Experiments show that this new method has a better performance than the widely used Flesch-Kincaid readability formula.