OpenRaiser/Intern-Atlas huggingface dataset:
Towards Event-level Causal Relation Identification
Authors: Chuang Fan, Daoxing Liu, Libo Qin, Yue Zhang, Ruifeng Xu
Venue: SIGIR
Year: 2022

Existing methods usually identify causal relations between events at the mention-level, which takes each event mention pair as a separate input. As a result, they either suffer from conflicts among causal relations predicted separately or require a set of additional constraints to resolve such conflicts. We propose to study this task in a more realistic setting, where event-level causality identification can be made. The advantage is two folds: 1) with modeling different mentions of an event as a single unit, no more conflicts among predicted results, without any extra constraints; 2) with the use of diverse knowledge sources (e.g., co-occurrence and coreference relations), a rich graph-based event structure can be induced from the document for supporting event-level causal inference. Graph convolutional network is used to encode such structural information, which aims to capture the local and non-local dependencies among nodes. Results show that our model achieves the best performance under both mention- and event-level settings, outperforming a number of strong baselines by at least 2.8% on F1 score.
Analyzing the Support Level for Tips Extracted from Product Reviews
Authors: Miriam Farber, David Carmel, Lital Kuchy, Avihai Mejer
Venue: SIGIR
Year: 2022

Useful tips extracted from product reviews assist customers to take a more informed purchase decision, as well as making a better, easier, and safer usage of the product. In this work we argue that extracted tips should be examined based on the amount of support and opposition they receive from all product reviews. A classifier, developed for this purpose, determines the degree to which a tip is supported or contradicted by a single review sentence. These support-levels are then aggregated over all review sentences, providing a global support score, and a global contradiction score, reflecting the support-level of all reviews to the given tip, thus improving the customer confidence in the tip validity. By analyzing a large set of tips extracted from product reviews, we propose a novel taxonomy for categorizing tips as highly-supported, highly-contradicted, controversial (supported and contradicted), and anecdotal (neither supported nor contradicted).
From Distillation to Hard Negative Sampling: Making Sparse Neural IR Models More Effective
Authors: Thibault Formal, Carlos Lassance, Benjamin Piwowarski, Stéphane Clinchant
Venue: SIGIR
Year: 2022

Neural retrievers based on dense representations combined with Approximate Nearest Neighbors search have recently received a lot of attention, owing their success to distillation and/or better sampling of examples for training -- while still relying on the same backbone architecture. In the meantime, sparse representation learning fueled by traditional inverted indexing techniques has seen a growing interest, inheriting from desirable IR priors such as explicit lexical matching. While some architectural variants have been proposed, a lesser effort has been put in the training of such models. In this work, we build on SPLADE -- a sparse expansion-based retriever -- and show to which extent it is able to benefit from the same training improvements as dense models, by studying the effect of distillation, hard-negative mining as well as the Pre-trained Language Model initialization. We furthermore study the link between effectiveness and efficiency, on in-domain and zero-shot settings, leading to state-of-the-art results in both scenarios for sufficiently expressive models.
SpaceQA: Answering Questions about the Design of Space Missions and Space Craft Concepts
Authors: Andrés García-Silva, Cristian Berrio, José Manuél Gómez-Pérez, José Antonio Martínez Heras, Alessandro Donati, Ilaria Roma
Venue: SIGIR
Year: 2022

We present SpaceQA, to the best of our knowledge the first open-domain QA system in Space mission design. SpaceQA is part of an initiative by the European Space Agency (ESA) to facilitate the access, sharing and reuse of information about Space mission design within the agency and with the public. We adopt a state-of-the-art architecture consisting of a dense retriever and a neural reader and opt for an approach based on transfer learning rather than fine-tuning due to the lack of domain-specific annotated data. Our evaluation on a test set produced by ESA is largely consistent with the results originally reported by the evaluated retrievers and confirms the need of fine tuning for reading comprehension. As of writing this paper, ESA is piloting SpaceQA internally.
ZeroMatcher: A Cost-Off Entity Matching System
Authors: Congcong Ge, Xiaocan Zeng, Lu Chen, Yunjun Gao
Venue: SIGIR
Year: 2022

Entity Matching (EM) aims to find data instances from different sources that refer to the same real-world entity. The existing EM techniques can be either costly or tailored for a specific data type. We present ZeroMatcher, a cost-off entity matching system, which supports (i) handling EM tasks with different data types, including relational tables and knowledge graphs; (ii) keeping its EM performance always competitive by enabling the sub-modules to be updated in a lightweight manner, thus reducing development costs; and (iii) performing EM without human annotations to further slash the labor costs. First, ZeroMatcher automatically suggests users a set of appropriate modules for EM according to the data types of the input datasets. Users could specify the modules for the subsequent EM process according to their preferences. Alternatively, users are able to customize the modules of ZeroMatcher. Then, the system proceeds to the EM task, where users can track the entire EM process and monitor the memory usage changes in real-time. When the EM process is completed, ZeroMatcher visualizes the EM results from different aspects to ease the understanding for users. Finally, ZeroMatcher provides EM results evaluation, enabling users to compare the effectiveness among different parameter settings.
A Study of Cross-Session Cross-Device Search Within an Academic Digital Library
Authors: Sebastian Gomes, Miriam L. Boon, Orland Hoeber
Venue: SIGIR
Year: 2022

Information seeking in an academic digital library is complex in nature, often spanning multiple search sessions. Resuming academic search tasks requires significant cognitive effort as searchers must re-acquaint themselves with previous search session activities and previously discovered documents before resuming their search. Further, some academic searchers may find it convenient to initiate such searches on their mobile devices during short gaps in time (e.g., between classes), and resume them later in a desktop environment when they can use the extra screen space and more convenient document storage capabilities of their computers. To support such searching, we have developed an academic digital library search interface that assists searchers in managing cross-session search tasks even when moving between mobile and desktop environments. Using a controlled laboratory study we compared our approach (Dilex) to a standard academic digital library search interface. We found increased user engagement in both the initial (mobile) and resumed (desktop) search activities, and that participants spent more time on the search results pages and had an increased degree of interaction with information and personalization features during the resumed tasks. These results provide evidence that the participants were able to make effective use of the visualization features in Dilex, which enabled them to readily resume their search tasks and stay engaged in the search activities. This work represents an example of how semi-automatic search task/session management and visualization features can support cross-session search, and how designing for both mobile and desktop use can support cross-device search.
Personalized Fashion Compatibility Modeling via Metapath-guided Heterogeneous Graph Learning
Authors: Weili Guan, Fangkai Jiao, Xuemeng Song, Haokun Wen, Chung-Hsing Yeh, Xiaojun Chang
Venue: SIGIR
Year: 2022

Fashion Compatibility Modeling (FCM) is a new yet challenging task, which aims to automatically access the matching degree among a set of complementary items. Most of existing methods evaluate the fashion compatibility from the common perspective, but overlook the user's personal preference. Inspired by this, a few pioneers study the Personalized Fashion Compatibility Modeling (PFCM). Despite their significance, these PFCM methods mainly concentrate on the user and item entities, as well as their interactions, but ignore the attribute entities, which contain rich semantics. To address this problem, we propose to fully explore the related entities and their relations involved in PFCM to boost the PFCM performance. This is, however, non-trivial due to the heterogeneous contents of different entities, embeddings for new users, and various high-order relations. Towards these ends, we present a novel metapath-guided personalized fashion compatibility modeling, dubbed as MG-PFCM. In particular, we creatively build a heterogeneous graph to unify the three types of entities (i.e., users, items, and attributes) and their relations (i.e., user-item interactions, item-item matching relations, and item-attribute association relations). Thereafter, we design a multi-modal content-oriented user embedding module to learn user representations by inheriting the contents of their interacted items. Meanwhile, we define the user-oriented and item-oriented metapaths, and perform the metapath-guided heterogeneous graph learning to enhance the user and item embeddings. In addition, we introduce the contrastive regularization to improve the model performance. We conduct extensive experiments on the real-world benchmark dataset, which verifies the superiority of our proposed scheme over several cutting-edge baselines. As a byproduct, we have released our source codes to benefit other researchers.
Deployable and Continuable Meta-learning-Based Recommender System with Fast User-Incremental Updates
Authors: Renchu Guan, Haoyu Pang, Fausto Giunchiglia, Ximing Li, Xuefeng Yang, Xiaoyue Feng
Venue: SIGIR
Year: 2022

User cold-start is a major challenge in building personalized recommender systems. Due to the lack of sufficient interactions, it is difficult to effectively model new users. One of the main solutions is to obtain an initial model through meta-learning (mainly gradient-based methods) and adapt it to new users with a few steps of gradient descent. Although these methods have achieved remarkable performance, they are still far from being usable in real-world applications due to their high-demand data processing, heavy computational burden, and inability to perform effective user-incremental update. In this paper, we propose a d eployable and c ontinuable m eta-learning-based r ecommendation (DCMR) approach, which can achieve fast user-incremental updating with task replay and first-order gradient descent. Specifically, we introduce a dual-constrained task sampler, distillation-based loss functions, and an adaptive controller in this framework to balance the trade-off between stability and plasticity in updating. In summary, DCMR can be updated while serving new users; in other words, it learns continuously and rapidly from a sequential user stream and is able to make recommendations at any time. The extensive experiments conducted on three benchmark datasets illustrate the superiority of our model.
Webformer: Pre-training with Web Pages for Information Retrieval
Authors: Yu Guo, Zhengyi Ma, Jiaxin Mao, Hongjin Qian, Xinyu Zhang, Hao Jiang, Zhao Cao, Zhicheng Dou
Venue: SIGIR
Year: 2022

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.
DH-HGCN: Dual Homogeneity Hypergraph Convolutional Network for Multiple Social Recommendations
Authors: Jiadi Han, Qian Tao, Yufei Tang, Yuhan Xia
Venue: SIGIR
Year: 2022

Social relations are often used as auxiliary information to improve recommendations. In the real-world, social relations among users are complex and diverse. However, most existing recommendation methods assume only single social relation (i.e., exploit pairwise relations to mine user preferences), ignoring the impact of multifaceted social relations on user preferences (i.e., high order complexity of user relations). Moreover, an observing fact is that similar items always have similar attractiveness when exposed to users, indicating a potential connection among the static attributes of items. Here, we advocate modeling the dual homogeneity from social relations and item connections by hypergraph convolution networks, named DH-HGCN, to obtain high-order correlations among users and items. Specifically, we use sentiment analysis to extract comment relation and use the k-means clustering to construct item-item correlations, and we then optimize those heterogeneous graphs in a unified framework. Extensive experiments on two real-world datasets demonstrate the effectiveness of our model.
A Dataset for Sentence Retrieval for Open-Ended Dialogues
Authors: Itay Harel, Hagai Taitelbaum, Idan Szpektor, Oren Kurland
Venue: SIGIR
Year: 2022

We address the task of sentence retrieval for open-ended dialogues. The goal is to retrieve sentences from a document corpus that contain information useful for generating the next turn in a given dialogue. Prior work on dialogue-based retrieval focused on specific types of dialogues: either conversational QA or conversational search. To address a broader scope of this task where any type of dialogue can be used, we constructed a dataset that includes open-ended dialogues from Reddit, candidate sentences from Wikipedia for each dialogue and human annotations for the sentences. We report the performance of several retrieval baselines, including neural retrieval models, over the dataset. To adapt neural models to the types of dialogues in the dataset, we explored an approach to induce a large-scale weakly supervised training data from Reddit. Using this training set significantly improved the performance over training on the MS MARCO dataset.
Rethinking Correlation-based Item-Item Similarities for Recommender Systems
Authors: Katsuhiko Hayashi
Venue: SIGIR
Year: 2022

This paper studies correlation-based item-item similarity measures for recommendation systems. While current research on recommender systems is directed toward deep learning-based approaches, nearest neighbor methods have been still used extensively in commercial recommender systems due to their simplicity. A crucial step in item-based nearest neighbor methods is to compute similarities between items, which are generally estimated through correlation measures like Pearson. The purpose of this paper is to re-investigate the effectiveness of correlation-based nearest neighbor methods on several benchmark datasets that have been used for recommendation evaluation in recent years. This paper also provides a more effective estimation method for correlation measures than the classical Pearson correlation coefficient and shows that this leads to significant improvements in recommendation performance.
Unified Dialog Model Pre-training for Task-Oriented Dialog Understanding and Generation
Authors: Wanwei He, Yinpei Dai, Min Yang, Jian Sun, Fei Huang, Luo Si, Yongbin Li
Venue: SIGIR
Year: 2022

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.
IWILDS'22 - Third International Workshop on Investigating Learning During Web Search
Authors: Anett Hoppe, Ran Yu, Jiqun Liu
Venue: SIGIR
Year: 2022

Since its inception, the World Wide Web has become a major information source, consulted for a diversity of informational tasks. With an abundance of information available online, Web search engines have been a main entry point, supporting users in finding suitable Web content for ever more complex information needs. The IWILDS workshop series invites research on complex search activities related to human learning. It provides an interdisciplinary platform for the presentation and discussion of recent research on human learning on the Web, welcoming perspectives from computer & information science, education and psychology.
Learning to Infer User Implicit Preference in Conversational Recommendation
Authors: Chenhao Hu, Shuhua Huang, Yansen Zhang, Yubao Liu
Venue: SIGIR
Year: 2022

Conversational recommender systems (CRS) enable traditional recommender systems to interact with users by asking questions about attributes and recommending items. The attribute-level and item-level feedback of users can be utilized to estimate users' preferences. However, existing works do not fully exploit the advantage of explicit item feedback --- they only use the item feedback in rather implicit ways such as updating the latent user and item representation. Since CRS has multiple chances to interact with users, leveraging the context in the conversation may help infer users' implicit feedback (e.g., some specific attributes) when recommendations get rejected. To address the limitations of existing methods, we propose a new CRS framework called Conversational Recommender with Implicit Feedback (CRIF). CRIF formulates the conversational recommendation scheme as a four-phase process consisting of offline representation learning, tracking, decision, and inference. In the inference module, by fully utilizing the relation between users' attribute-level and item-level feedback, our method can explicitly deduce users' implicit preferences. Therefore, CRIF is able to achieve more accurate user preference estimation. Besides, in the decision module, to better utilize the attribute-level and item-level feedback, we adopt inverse reinforcement learning to learn a flexible decision strategy that selects the suitable action at each conversation turn. Through extensive experiments on four benchmark CRS datasets, we validate the effectiveness of our approach, which significantly outperforms the state-of-the-art CRS methods.
Empowering Next POI Recommendation with Multi-Relational Modeling
Authors: Zheng Huang, Jing Ma, Yushun Dong, Natasha Zhang Foutz, Jundong Li
Venue: SIGIR
Year: 2022

With the wide adoption of mobile devices and web applications, location-based social networks (LBSNs) offer large-scale individual-level location-related activities and experiences. Next point-of-interest (POI) recommendation is one of the most important tasks in LBSNs, aiming to make personalized recommendations of next suitable locations to users by discovering preferences from users' historical activities. Noticeably, LBSNs have offered unparalleled access to abundant heterogeneous relational information about users and POIs (including user-user social relations, such as families or colleagues; and user-POI visiting relations). Such relational information holds great potential to facilitate the next POI recommendation. However, most existing methods either focus on merely the user-POI visits, or handle different relations based on over-simplified assumptions while neglecting relational heterogeneities. To fill these critical voids, we propose a novel framework, MEMO, which effectively utilizes the heterogeneous relations with a multi-network representation learning module, and explicitly incorporates the inter-temporal user-POI mutual influence with the coupled recurrent neural networks. Extensive experiments on real-world LBSN data validate the superiority of our framework over the state-of-the-art next POI recommendation methods.
Self-Supervised Learning for Recommender System
Authors: Chao Huang, Xiang Wang, Xiangnan He, Dawei Yin
Venue: SIGIR
Year: 2022

Recommender systems have become key components for a wide spectrum of web applications (e.g., E-commerce sites, video sharing platforms, lifestyle applications, etc), so as to alleviate the information overload and suggest items for users. However, most existing recommendation models follow a supervised learning manner, which notably limits their representation ability with the ubiquitous sparse and noisy data in practical applications. Recently, self-supervised learning (SSL) has become a promising learning paradigm to distill informative knowledge from unlabeled data, without the heavy reliance on sufficient supervision signals. Inspired by the effectiveness of self-supervised learning, recent efforts bring SSL's superiority into various recommendation representation learning scenarios with augmented auxiliary learning tasks. In this tutorial, we aim to provide a systemic review of existing self-supervised learning frameworks and analyze the corresponding challenges for various recommendation scenarios, such as general collaborative filtering paradigm, social recommendation, sequential recommendation, and multi-behavior recommendation. We then raise discussions and future directions of this area. With the introduction of this emerging and promising topic, we expect the audience to have a deep understanding of this domain. We also seek to promote more ideas and discussions, which facilitates the development of self-supervised learning recommendation techniques.