In time series visualization, sampling is used to reduce the number of points while retaining the visual features of the raw time series. Area-based Largest Triangle Sampling (LTS) excels at preserving perceptually critical points. However, the heuristic solution to LTS by sequentially sampling points with the locally largest triangle area (a.k.a. Largest-Triangle-Three-Buckets, LTTB) suffers from suboptimal solution and query inefficiency. We address the shortcomings by contributing a novel Iterative Largest Triangle Sampling (ILTS) algorithm with convex hull acceleration. It refines the sampling results iteratively, capturing a broader perspective by integrating more points in each iteration. Remarkably, we prove that the largest triangle can always be found in the precomputed convex hulls, making the iterative sampling still efficient. Experiments demonstrate increased visual quality over state-of-the-art baselines and significant speedups over the brute force approach.

Recently, generative retrieval has emerged as a promising alternative to the traditional retrieval paradigms. It assigns each document a unique identifier, known as the DocID, and employs a generative model to directly generate the relevant DocID for the input query. A common choice for the DocID is one or several natural language sequences, e.g. the title, synthetic queries, or n-grams, so that the pre-trained knowledge of the generative model can be effectively utilized. However, a sequence is generated token by token, where only the most likely candidates are kept and the rest are pruned at each decoding step, thus, retrieval fails if any token within the relevant DocID is falsely pruned. What's worse, during decoding, the model can only perceive preceding tokens in the DocID while being blind to subsequent ones, hence is prone to make such errors. To address this problem, we present a novel framework for generative retrieval, dubbed Term-Set Generation (TSGen). Instead of sequences, we use a set of terms as the DocID. The terms are selected based on learned weights from relevance signals, so that they concisely summarize the document's semantics and distinguish it from others. On top of the term-set DocID, we propose a permutation-invariant decoding algorithm, with which the term set can be generated in any permutation yet will always lead to the corresponding document. Remarkably, TSGen perceives all valid terms rather than only the preceding ones at each decoding step. Given the constant decoding space, it can make more reliable decisions due to the broader perspective. TSGen is also resilient to errors: the relevant DocID will not be falsely pruned as long as the decoded term belongs to it. Moreover, TSGen can explore the optimal decoding permutation of the term set on its own, which further improves the likelihood of generating the relevant DocID. Lastly, we design an iterative optimization procedure to incentivize the model to generate the relevant term set in its favorable permutation. We conduct extensive experiments on popular benchmarks of generative retrieval, which validate the effectiveness, the generalizability, the scalability, and the efficiency of TSGen.

While previous video to text models have achieved remarkable successes, they mostly focus on how to understand the video contents in a general sense, but fail to capture the human personalized preferences, which is highly demanded for an engaging multimodal chatbots. Different from user modeling in collaborative filtering, there is no other user behaviors in inference as a real-time video stream is coming. In this paper, we formally define the task of personalized video commenting task and design an end-to-end personalized framework for solving this task. In specific, we argue that the personalization for video comment generation can be reflected in two aspects, that is, (1) for the same video, different users may comment on different clips, and (2) for the same clip, different people may also express various opinions with diverse commentary styles. Motivated by these considerations, we design our framework based on two components. The first one is a clip selector, which is responsible for predicting the clips that the user may comment in the video. The second one is a text generator, which aims to produce the comment based on the above predicted clips and the user's preference. In our framework, these two components are optimized in an end-to-end manner to mutually enhance each other, where we design confidence-aware scheduled sampling and iterative inference strategies to solve the problem that the ground truth clips are absent in the inference phase. As the absence of personalized video to text dataset, we collect and release a new dataset for studying this problem. We conduct extensive experiments to demonstrate the effectiveness of our model.

With the development of pre-trained language models, the dense retrieval models have become promising alternatives to the traditional retrieval models that rely on exact match and sparse bag-of-words representations. Different from most dense retrieval models using a bi-encoder to encode each query or document into a dense vector, the recently proposed late-interaction multi-vector models (i.e., ColBERT and COIL) achieve state-of-the-art retrieval effectiveness by using all token embeddings to represent documents and queries and modeling their relevance with a sum-of-max operation. However, these fine-grained representations may cause unacceptable storage overhead for practical search systems. In this study, we systematically analyze the matching mechanism of these late-interaction models and show that the sum-of-max operation heavily relies on the co-occurrence signals and some important words in the document. Based on these findings, we then propose several simple document pruning methods to reduce the storage overhead and compare the effectiveness of different pruning methods on different late-interaction models. We also leverage query pruning methods to further reduce the retrieval latency. We conduct extensive experiments on both in-domain and out-domain datasets and show that some of the used pruning methods can significantly improve the efficiency of these late-interaction models without substantially hurting their retrieval effectiveness.

Conversational recommender systems~(CRSs) aim to provide recommendation services via natural language conversations. Although a number of approaches have been proposed for developing capable CRSs, they typically rely on sufficient training data for training. Since it is difficult to annotate recommendation-oriented dialogue datasets, existing CRS approaches often suffer from the issue of insufficient training due to the scarcity of training data.

Search clarification in conversational search systems exhibits a clarification pane composed of several candidate aspect items and a clarifying question. To generate a pane, existing studies usually rely on unstructured document texts. However, important structured information in search results is not effectively considered, making the generated panes inaccurate in some cases. In this paper, we emphasize the importance of structured information in search results for improving search clarification. We propose enhancing unstructured documents with two kinds of structured information: one is "In-List'' relation obtained from HTML list structures, which helps extract groups of high-quality items with abundant parallel information. Another is "Is-A'' relation extracted from knowledge bases, which is helpful to generate good questions with explicit prompts. To avoid introducing excessive noises, we design a relation selection process to filter out ineffective relations. We further design a BART-based model for generating clarification panes. The experimental results show that the structured information is good supplement for generating high-quality clarification panes.

Conversational recommender systems (CRS) aim to provide the recommendation service via natural language conversations. To develop an effective CRS, high-quality CRS datasets are very crucial. However, existing CRS datasets suffer from the long-tail issue, i.e., a large proportion of items are rarely (or even never) mentioned in the conversations, which are called long-tail items. As a result, the CRSs trained on these datasets tend to recommend frequent items, and the diversity of the recommended items would be largely reduced, making users easier to get bored.

Recent studies have shown that Dense Retrieval (DR) techniques can significantly improve the performance of first-stage retrieval in IR systems. Despite its empirical effectiveness, the application of DR is still limited. In contrast to statistic retrieval models that rely on highly efficient inverted index solutions, DR models build dense embeddings that are difficult to be pre-processed with most existing search indexing systems. To avoid the expensive cost of brute-force search, the Approximate Nearest Neighbor (ANN) algorithm and corresponding indexes are widely applied to speed up the inference process of DR models. Unfortunately, while ANN can improve the efficiency of DR models, it usually comes with a significant price on retrieval performance.

Vector quantization is one of the critical techniques which enables dense retrieval for realtime applications. The recent study shows that vanilla vector quantization methods, like those implemented by FAISS [8], are lossy and prone to limited retrieval performances when large acceleration ratios are needed [14, 16, 18]. Besides, there have also been multiple algorithms which make the retriever and VQ better collaborated to alleviate such a loss. On top of these progresses, we develop LibVQ, which optimizes vector quantization for efficient dense retrieval. Our toolkit is highlighted for three advantages. 1. Effectiveness. The retrieval quality can be substantially improved over the vanilla implementations of VQ. 2. Simplicity. The optimization can be conducted in a lowcode fashion, and the optimization results can be easily loaded to ANN indexes to support downstream applications. 3. Universality. The optimization is agnostic to the embedding's learning process, and may accommodate different input conditions and ANN back-ends with little modification of the workflow. LibVQ may also support rich applications beyond dense retrieval, e.g., embedding compression, topic modeling, and de-duplication. In this demo, we provide comprehensive hand-on examples and evaluations for LibVQ. The toolkit is publicly released at: https://github.com/staoxiao/LibVQ/tree/demo.

Learning effective high-order feature interactions is very crucial in the CTR prediction task. However, it is very time-consuming to calculate high-order feature interactions with massive features in online e-commerce platforms. Most existing methods manually design a maximal order and further filter out the useless interactions from them. Although they reduce the high computational costs caused by the exponential growth of high-order feature combinations, they still suffer from the degradation of model capability due to the suboptimal learning of the restricted feature orders. The solution to maintain the model capability and meanwhile keep it efficient is a technical challenge, which has not been adequately addressed. To address this issue, we propose an adaptive feature interaction learning model, named as EulerNet, in which the feature interactions are learned in a complex vector space by conducting space mapping according to Euler's formula. EulerNet converts the exponential powers of feature interactions into simple linear combinations of the modulus and phase of the complex features, making it possible to adaptively learn the high-order feature interactions in an efficient way. Furthermore, EulerNet incorporates the implicit and explicit feature interactions into a unified architecture, which achieves the mutual enhancement and largely boosts the model capabilities. Such a network can be fully learned from data, with no need of pre-designed form or order for feature interactions. Extensive experiments conducted on three public datasets have demonstrated the effectiveness and efficiency of our approach. Our code is available at: https://github.com/RUCAIBox/EulerNet.

Conversational search supports multi-turn user-system interactions to solve complex information needs. Compared with the traditional single-turn ad-hoc search, conversational search faces a more complex search intent understanding problem because a conversational search session is much longer and contains many noisy tokens. However, existing conversational dense retrieval solutions simply fine-tune the pre-trained ad-hoc query encoder on limited conversational search data, which are hard to achieve satisfactory performance in such a complex conversational search scenario. Meanwhile, the learned latent representation also lacks interpretability that people cannot perceive how the model understands the session. To tackle the above drawbacks, we propose a sparse Lexical-based Conversational REtriever (LeCoRE), which extends the SPLADE model with two well-matched multi-level denoising methods uniformly based on knowledge distillation and external query rewrites to generate denoised and interpretable lexical session representation. Extensive experiments on four public conversational search datasets in both normal and zero-shot evaluation settings demonstrate the strong performance of LeCoRE towards more effective and interpretable conversational search.

Users' search tasks have become increasingly complicated, requiring multiple queries and interactions with the results. Recent studies have demonstrated that modeling the historical user behaviors in a session can help understand the current search intent. Existing context-aware ranking models primarily encode the current session sequence (from the first behavior to the current query) and compute the ranking score using the high-level representations. However, there is usually some noise in the current session sequence (useless behaviors for inferring the search intent) that may affect the quality of the encoded representations. To help the encoding of the current user behavior sequence, we propose to use a decoder and the information of future sequences and a supplemental query. Specifically, we design three generative tasks that can help the encoder to infer the actual search intent: (1) predicting future queries, (2) predicting future clicked documents, and (3) predicting a supplemental query. We jointly learn the ranking task with these generative tasks using an encoder-decoder structured approach. Extensive experiments on two public search logs demonstrate that our model outperforms all existing baselines, and the designed generative tasks can actually help the ranking task. Besides, additional experiments also show that our approach can be easily applied to various Transformer-based encoder-decoder models and improve their performance.

Cross-modal hashing (CMH) has been widely used in multimedia retrieval applications for its low storage cost and fast indexing speed. Thanks to the success of deep learning, cross-modal hashing has made significant progress with high-quality deep features. However, the modal gap is still a crucial bottleneck for existing cross-modal hashing methods: the commonly used convolutional neural network and bag-of-words encoders are customized for single modal prior, limiting the models to learn semantics representation in a cross-modal space. To overcome modality heterogeneity, we propose a shared transformer encoder (UniHash) to unify the cross-modal hashing into the same semantic space. A contrastive label correlation learning (CLC) loss using the category labels as modality bridge is designed together to improve the representation quality. Moreover, we take advantage of the multi-hot label space and propose a negative label generation (NegLG) strategy to get richer and uniformly distributed negative labels for contrast. Extensive experiments on three benchmarks verify the advantage of our proposed method. Besides, the proposed UniHash outperforms state-of-the-art cross-modal hashing methods significantly, establishing a new important baseline for the cross-modal hashing research. Codes are released github.com/idealwhite/Unihash.

Nowadays, multi-hop question answer (QA) problem is challenging and not well solved in the QA community. The dominant bottleneck of the multi-hop QA problem is the need for a reasoning retriever to fetch a document path from an open-domain corpus (e.g., Wikipedia). A reasoning retriever aims to collect an evidence document from large corpora at one hop retrieval and aggregate the evidence for subsequent hop retrieval, which yields a document path after multi-hop retrieval. There exist two challenges, (1) to fetch the evidence document in an efficient and explainable way at one hop retrieval and (2) to update the question information by aggregating the evidence from the retrieved document after each hop retrieval. To address these two challenges, we propose a triple-fact-based retrieval model to effectively retrieve a related document path in an explainable way for each question. We extract a structured representation from the unstructured document and utilize the knowledge of pre-trained language model (PLM) to do the semantic-level matching between the question and document. We evaluate the proposed Triple-fact Retriever model on the recently proposed open-domain multi-hop QA dataset, HotpotQA, and a cross-document multi-step Reading Comprehension dataset, Wikihop. The results11The source code is available on our website: https://github.com/Rebaccamin/triple_retriever. demonstrate that the Triple-fact retriever outperforms the existing baseline retrieval works.

Pre-trained language models (PLMs) have achieved great success in the area of Information Retrieval. Studies show that applying these models to ad-hoc document ranking can achieve better retrieval effectiveness. However, on the Web, most information is organized in the form of HTML web pages. In addition to the pure text content, the structure of the content organized by HTML tags is also an important part of the information delivered on a web page. Currently, such structured information is totally ignored by pre-trained models which are trained solely based on text content. In this paper, we propose to leverage large-scale web pages and their DOM (Document Object Model) tree structures to pre-train models for information retrieval. We argue that using the hierarchical structure contained in web pages, we can get richer contextual information for training better language models. To exploit this kind of information, we devise four pre-training objectives based on the structure of web pages, then pre-train a Transformer model towards these tasks jointly with traditional masked language model objective. Experimental results on two authoritative ad-hoc retrieval datasets prove that our model can significantly improve ranking performance compared to existing pre-trained models.

The configuration of 512 window size prevents transformers from being directly applicable to document ranking that requires larger context. Hence, recent works propose to estimate document relevance with fine-grained passage-level relevance signals. A limitation of such models, however, is that scoring each passage independently falls short in modeling inter-passage interactions and leads to unsatisfactory results. In this paper, we propose a Multiview inter-passage Interaction based Ranking model (MIR), to combine intra-passage interactions and inter-passage interactions in a complementary manner. The former captures local semantic relations inside each passage, whereas the latter draws global dependencies between different passages. Moreover, we represent inter-passage relationships via multi-view attention patterns, allowing information propagation at token, sentence, and passage-level. The representations at different levels of granularity, being aware of global context, are then aggregated into a document-level representation for ranking. Experimental results on two benchmarks show that modeling inter-passage interactions brings substantial improvements over existing passage-level methods.

Designing pre-training objectives that more closely resemble the downstream tasks for pre-trained language models can lead to better performance at the fine-tuning stage, especially in the ad-hoc retrieval area. Existing pre-training approaches tailored for IR tried to incorporate weak supervised signals, such as query-likelihood based sampling, to construct pseudo query-document pairs from the raw textual corpus. However, these signals rely heavily on the sampling method. For example, the query likelihood model may lead to much noise in the constructed pre-training data. In this paper, we propose to leverage the large-scale hyperlinks and anchor texts to pre-train the language model for ad-hoc retrieval. Since the anchor texts are created by webmasters and can usually summarize the target document, it can help to build more accurate and reliable pre-training samples than a specific algorithm. Considering different views of the downstream ad-hoc retrieval, we devise four pre-training tasks based on the hyperlinks. We then pre-train the Transformer model to predict the pair-wise preference, jointly with the Masked Language Model objective. Experimental results on two large-scale ad-hoc retrieval datasets show the significant improvement of our model compared with the existing methods.

Currently, the most popular method for open-domain Question Answering (QA) adopts "Retriever and Reader" pipeline, where the retriever extracts a list of candidate documents from a large set of documents followed by a ranker to rank the most relevant documents and the reader extracts answer from the candidates. Existing studies take the greedy strategy in the sense that they only use samples for ranking at the current hop, and ignore the global information across the whole documents. In this paper, we propose a purely rank-based framework Thinking Path Re-Ranker (TPRR), which is comprised of Thinking Path Ranker (TPR) for generating document sequences called "a path" and External Path Reranker (EPR) for selecting the best path from candidate paths generated by TPR. Specifically, TPR leverages the scores of a dense model and conditional probabilities to score the full paths. Moreover, to further enhance the performance of the dense ranker in the iterative training, we propose a "thinking" negatives selection method that the top-K candidates treated as negatives in the current hop are adjusted dynamically through supervised signals. After achieving multiple supporting paths through TPR, the EPR component which integrates several fine-grained training tasks for QA is used to select the best path for answer extraction. We have tested our proposed solution on the multi-hop dataset "HotpotQA" with a full wiki set ting, and the results show that TPRR significantly outperforms the existing state-of-the-art models. Moreover, our method has won the first place in the HotpotQA official leaderboard since Feb 1, 2021 under the Fullwiki setting. Code is available at https://gitee.com/mindspore/mindspore/ tree/master/model_zoo/research/nlp/tprr.

A popular programming paradigm in the cloud, MapReduce is ex- tensively considered and used for big analysis. Unfortu- nately, a great many big applications require capabilities be- yond those originally intended by MapReduce, often burdening de- velopers to write unnatural non-obvious MapReduce programs so as to twist the underlying system to meet the requirements. In this paper, we focus on a class of big applications that in addi- tion to MapReduce's main data source, require selective access to one or many data sources, e.g., various kinds of indices, knowledge bases, external cloud services. We propose to extend MapReduce with EFind, an Efficient and Flexible index access solution, to better support this class of ap- plications. EFind introduces a standard index access interface to MapReduce so that (i) developers can easily and flexibly express index access operations without unnatural code, and (ii) the EFind enhanced MapReduce system can automatically optimize the in- dex access operations. We propose and analyze a number of in- dex access strategies that utilize caching, re-partitioning, and index locality to reduce redundant index accesses. EFind collects index statistics and performs cost-based adaptive optimization to improve index access efficiency. Our experimental results, using both real- world and synthetic data sets, show that EFind chooses execution plans that are optimal or close to optimal, and achieves a factor of 2x-8x improvements compared to an approach that accesses in- dices without optimization.

MapReduce based data-intensive computing solutions are increasingly deployed as production systems. Unlike Internet companies who invent and adopt the technology from the very beginning, traditional enterprises demand easy-to-use software due to the limited capabilities of administrators. Automatic job optimization software for MapReduce is a promising technique to satisfy such requirements. In this paper, we introduce a toolkit from IBM, called MRTuner , to enable holistic optimization for MapReduce jobs. In particular, we propose a novel Producer-Transporter-Consumer (PTC) model, which characterizes the tradeoffs in the parallel execution among tasks. We also carefully investigate the complicated relations among about twenty parameters, which have significant impact on the job performance. We design an efficient search algorithm to find the optimal execution plan. Finally, we conduct a thorough experimental evaluation on two different types of clusters using the HiBench suite which covers various Hadoop workloads from GB to TB size levels. The results show that the search latency of MRTuner is a few orders of magnitude faster than that of the state-of-the-art cost-based optimizer, and the effectiveness of the optimized execution plan is also significantly improved.

Despite its promise, RFID technology presents numerous challenges, including incomplete data, lack of location and containment information, and very high volumes. In this work, we present a novel data inference and compression substrate over RFID streams to address these challenges. Our substrate employs a time-varying graph model to efficiently capture possible object locations and interobject relationships such as containment from raw RFID streams. It then employs a probabilistic algorithm to estimate the most likely location and containment for each object. By performing such online inference, it enables online compression that recognizes and removes redundant information from the output stream of this substrate. We have implemented a prototype of our inference and compression substrate and evaluated it using both real traces from a laboratory warehouse setup and synthetic traces emulating enterprise supply chains. Results of a detailed performance study show that our data inference techniques provide high accuracy while retaining efficiency over RFID data streams, and our compression algorithm yields significant reduction in output data volume.

In this paper, we present the design of a scalable, distributed stream processing system for RFID tracking and monitoring. Since RFID data lacks containment and location information that is key to query processing, we propose to combine location and containment inference with stream query processing in a single architecture, with inference as an enabling mechanism for high-level query processing. We further consider challenges in instantiating such a system in large distributed settings and design techniques for distributed inference and query processing. Our experimental results, using both real-world data and large synthetic traces, demonstrate the accuracy, efficiency, and scalability of our proposed techniques.