OpenRaiser/Intern-Atlas huggingface dataset:
Bilateral Self-unbiased Learning from Biased Implicit Feedback
Authors: Jae-woong Lee, Seongmin Park, Joonseok Lee, Jongwuk Lee
Venue: SIGIR
Year: 2022

Implicit feedback has been widely used to build commercial recommender systems. Because observed feedback represents users' click logs, there is a semantic gap between true relevance and observed feedback. More importantly, observed feedback is usually biased towards popular items, thereby overestimating the actual relevance of popular items. Although existing studies have developed unbiased learning methods using inverse propensity weighting (IPW) or causal reasoning, they solely focus on eliminating the popularity bias of items. In this paper, we propose a novel unbiased recommender learning model, namely BIlateral SElf-unbiased Recommender (BISER), to eliminate the exposure bias of items caused by recommender models. Specifically, BISER consists of two key components: (i) self-inverse propensity weighting (SIPW) to gradually mitigate the bias of items without incurring high computational costs; and (ii) bilateral unbiased learning (BU) to bridge the gap between two complementary models in model predictions, i.e., user- and item-based autoencoders, alleviating the high variance of SIPW. Extensive experiments show that BISER consistently outperforms state-of-the-art unbiased recommender models over several datasets, including Coat, Yahoo! R3, MovieLens, and CiteULike.
ViQuAE, a Dataset for Knowledge-based Visual Question Answering about Named Entities
Authors: Paul Lerner, Olivier Ferret, Camille Guinaudeau, Hervé Le Borgne, Romaric Besançon, José G. Moreno, Jesús Lovón-Melgarejo
Venue: SIGIR
Year: 2022

Whether to retrieve, answer, translate, or reason, multimodality opens up new challenges and perspectives. In this context, we are interested in answering questions about named entities grounded in a visual context using a Knowledge Base (KB). To benchmark this task, called KVQAE (Knowledge-based Visual Question Answering about named Entities), we provide ViQuAE, a dataset of 3.7K questions paired with images. This is the first KVQAE dataset to cover a wide range of entity types (e.g. persons, landmarks, and products). The dataset is annotated using a semi-automatic method. We also propose a KB composed of 1.5M Wikipedia articles paired with images. To set a baseline on the benchmark, we address KVQAE as a two-stage problem: Information Retrieval and Reading Comprehension, with both zero- and few-shot learning methods. The experiments empirically demonstrate the difficulty of the task, especially when questions are not about persons. This work paves the way for better multimodal entity representations and question answering. The dataset, KB, code, and semi-automatic annotation pipeline are freely available at https://github.com/PaulLerner/ViQuAE.
Multi-Agent RL-based Information Selection Model for Sequential Recommendation
Authors: Kaiyuan Li, Pengfei Wang, Chenliang Li
Venue: SIGIR
Year: 2022

For sequential recommender, the coarse-grained yet sparse sequential signals mined from massive user-item interactions have become the bottleneck to further improve the recommendation performance. To alleviate the spareness problem, exploiting auxiliary semantic features (\eg textual descriptions, visual images and knowledge graph) to enrich contextual information then turns into a mainstream methodology. Though effective, we argue that these different heterogeneous features certainly include much noise which may overwhelm the valuable sequential signals, and therefore easily reach the phenomenon of negative collaboration (ie 1 + 1 > 2). How to design a flexible strategy to select proper auxiliary information and alleviate the negative collaboration towards a better recommendation is still an interesting and open question. Unfortunately, few works have addressed this challenge in sequential recommendation. In this paper, we introduce a Multi-Agent RL-based Information S election Model (named MARIS) to explore an effective collaboration between different kinds of auxiliary information and sequential signals in an automatic way. Specifically, MARIS formalizes the auxiliary feature selection as a cooperative Multi-agent Markov Decision Process. For each auxiliary feature type, MARIS resorts to using an agent to determine whether a specific kind of auxiliary feature should be imported to achieve a positive collaboration. In between, a QMIX network is utilized to cooperate their joint selection actions and produce an episode corresponding an effective combination of different auxiliary features for the whole historical sequence. Considering the lack of supervised selection signals, we further devise a novel reward-guided sampling strategy to leverage exploitation and exploration scheme for episode sampling. By preserving them in a replay buffer, MARIS learns the action-value function and the reward alternatively for optimization. Extensive experiments on four real-world datasets demonstrate that our model obtains significant performance improvement over up-to-date state-of-the-art recommendation models.
BSAL: A Framework of Bi-component Structure and Attribute Learning for Link Prediction
Authors: Bisheng Li, Min Zhou, Shengzhong Zhang, Menglin Yang, Defu Lian, Zengfeng Huang
Venue: SIGIR
Year: 2022

Given the ubiquitous existence of graph-structured data, learning the representations of nodes for the downstream tasks ranging from node classification, link prediction to graph classification is of crucial importance. Regarding missing link inference of diverse networks, we revisit the link prediction techniques and identify the importance of both the structural and attribute information. However, the available techniques either heavily count on the network topology which is spurious in practice, or cannot integrate graph topology and features properly. To bridge the gap, we propose a bicomponent structural and attribute learning framework (BSAL) that is designed to adaptively leverage information from topology and feature spaces. Specifically, BSAL constructs a semantic topology via the node attributes and then gets the embeddings regarding the semantic view, which provides a flexible and easy-to-implement solution to adaptively incorporate the information carried by the node attributes. Then the semantic embedding together with topology embedding are fused together using attention mechanism for the final prediction. Extensive experiments show the superior performance of our proposal and it significantly outperforms baselines on diverse research benchmarks.
Transform Cold-Start Users into Warm via Fused Behaviors in Large-Scale Recommendation
Authors: Pengyang Li, Rong Chen, Quan Liu, Jian Xu, Bo Zheng
Venue: SIGIR
Year: 2022

Recommendation for cold-start users who have very limited data is a canonical challenge in recommender systems. Existing deep recommender systems utilize user content features and behaviors to produce personalized recommendations, yet often face significant performance degradation on cold-start users compared to existing ones due to the following challenges: (1) Cold-start users may have a quite different distribution of features from existing users. (2) The few behaviors of cold-start users are hard to be exploited. In this paper, we propose a recommender system called Cold-Transformer to alleviate these problems. Specifically, we design context-based Embedding Adaption to offset the differences in feature distribution. It transforms the embedding of cold-start users into a warm state that is more like existing ones to represent corresponding user preferences. Furthermore, to exploit the few behaviors of cold-start users and characterize the user context, we propose Label Encoding that models Fused Behaviors of positive and negative feedback simultaneously, which are relatively more sufficient. Last, to perform large-scale industrial recommendations, we keep the two-tower architecture that de-couples user and target item. Extensive experiments on public and industrial datasets show that Cold-Transformer significantly outperforms state-of-the-art methods, including those that are deep coupled and less scalable.
BERT-based Dense Intra-ranking and Contextualized Late Interaction via Multi-task Learning for Long Document Retrieval
Authors: Minghan Li, Éric Gaussier
Venue: SIGIR
Year: 2022

Combining query tokens and document tokens and inputting them to pre-trained transformer models like BERT, an approach known as interaction-based, has shown state-of-the-art effectiveness for information retrieval. However, the computational complexity of this approach is high due to the online self-attention computation. In contrast, dense retrieval methods in representation-based approaches are known to be efficient, however less effective. A tradeoff between the two is reached with late interaction methods like ColBERT, which attempt to benefit from both approaches: contextualized token embeddings can be pre-calculated over BERT for fine-grained effective interaction while preserving efficiency. However, despite its success in passage retrieval, it's not straightforward to use this approach for long document retrieval. In this paper, we propose a cascaded late interaction approach using a single model for long document retrieval. Fast intra-ranking by dot product is used to select relevant passages, then fine-grained interaction of pre-stored token embeddings is used to generate passage scores which are aggregated to the final document score. Multi-task learning is used to train a BERT model to optimize both a dot product and a fine-grained interaction loss functions. Our experiments reveal that the proposed approach obtains near state-of-the-art level effectiveness while being efficient on such collections as TREC 2019.
Enhancing Hypergraph Neural Networks with Intent Disentanglement for Session-based Recommendation
Authors: Yinfeng Li, Chen Gao, Hengliang Luo, Depeng Jin, Yong Li
Venue: SIGIR
Year: 2022

Session-based recommendation (SBR) aims at the next-item prediction with a short behavior session. Existing solutions fail to address two main challenges: 1) user interests are shown as dynamically coupled intents, and 2) sessions always contain noisy signals. To address them, in this paper, we propose a hypergraph-based solution, HIDE. Specifically, HIDE first constructs a hypergraph for each session to model the possible interest transitions from distinct perspectives. HIDE then disentangles the intents under each item click in micro and macro manners. In the micro-disentanglement, we perform intent-aware embedding propagation on session hypergraph to adaptively activate disentangled intents from noisy data. In the macro-disentanglement, we introduce an auxiliary intent-classification task to encourage the independence of different intents. Finally, we generate the intent-specific representations for the given session to make the final recommendation. Benchmark evaluations demonstrate the significant performance gain of our HIDE over the state-of-the-art methods.
Multi-label Masked Language Modeling on Zero-shot Code-switched Sentiment Analysis
Authors: Zhi Li, Xing Gao, Ji Zhang, Yin Zhang
Venue: SIGIR
Year: 2022

In multilingual communities, code-switching is a common phenomenon and code-switched tasks have become a crucial area of research in natural language processing (NLP) applications. Existing approaches mainly focus on supervised learning. However, it is expensive to annotate a sufficient amount of code-switched data. In this paper, we consider zero-shot setting and improve model performance on code-switched tasks via monolingual language datasets, unlabeled code-switched datasets, and semantic dictionaries. Inspired by the mechanism of code-switching itself, we propose multi-label masked language modeling and predict both the masked word and its synonyms in other languages. Experimental results show that compared with baselines, our method can further improve the pretrained multilingual model's performance on code-switched sentiment analysis datasets.
Contrastive Learning with Hard Negative Entities for Entity Set Expansion
Authors: Yinghui Li, Yangning Li, Yuxin He, Tianyu Yu, Ying Shen, Hai-Tao Zheng
Venue: SIGIR
Year: 2022

Entity Set Expansion (ESE) is a promising task which aims to expand entities of the target semantic class described by a small seed entity set. Various NLP and IR applications will benefit from ESE due to its ability to discover knowledge. Although previous ESE methods have achieved great progress, most of them still lack the ability to handle hard negative entities (i.e., entities that are difficult to distinguish from the target entities), since two entities may or may not belong to the same semantic class based on different granularity levels we analyze on. To address this challenge, we devise an entity-level masked language model with contrastive learning to refine the representation of entities. In addition, we propose the ProbExpan, a novel probabilistic ESE framework utilizing the entity representation obtained by the aforementioned language model to expand entities. Extensive experiments and detailed analyses on three datasets show that our method outperforms previous state-of-the-art methods.
Adversarial Filtering Modeling on Long-term User Behavior Sequences for Click-Through Rate Prediction
Authors: Xiaochen Li, Jian Liang, Xialong Liu, Yu Zhang
Venue: SIGIR
Year: 2022

Rich user behavior information is of great importance for capturing and understanding user interest in click-through rate (CTR) prediction. To improve the richness, collecting long-term behaviors becomes a typical approach in academy and industry but at the cost of increasing online storage and latency. Recently, researchers have proposed several approaches to shorten long-term behavior sequence and then model user interests. These approaches reduce online cost efficiently but do not well handle the noisy information in long-term user behavior, which may deteriorate the performance of CTR prediction significantly. To obtain better cost/performance trade-off, we propose a novel Adversarial Filtering Model (ADFM) to model long-term user behavior. ADFM uses a hierarchical aggregation representation to compress raw behavior sequence and then learns to remove useless behavior information with an adversarial filtering mechanism. The selected user behaviors are fed into interest extraction module for CTR prediction. Experimental results on public datasets and industrial dataset demonstrate that our method achieves significant improvements over state-of-the-art models.
How Does Feedback Signal Quality Impact Effectiveness of Pseudo Relevance Feedback for Passage Retrieval
Authors: Hang Li, Ahmed Mourad, Bevan Koopman, Guido Zuccon
Venue: SIGIR
Year: 2022

Pseudo-Relevance Feedback (PRF) assumes that the top results retrieved by a first-stage ranker are relevant to the original query and uses them to improve the query representation for a second round of retrieval. This assumption however is often not correct: some or even all of the feedback documents may be irrelevant. Indeed, the effectiveness of PRF methods may well depend on the quality of the feedback signal and thus on the effectiveness of the first-stage ranker. This aspect however has received little attention before. In this paper we control the quality of the feedback signal and measure its impact on a range of PRF methods, including traditional bag-of-words methods (Rocchio), and dense vector-based methods (learnt and not learnt). Our results show the important role the quality of the feedback signal plays on the effectiveness of PRF methods. Importantly, and surprisingly, our analysis reveals that not all PRF methods are the same when dealing with feedback signals of varying quality. These findings are critical to gain a better understanding of the PRF methods and of which and when they should be used, depending on the feedback signal quality, and set the basis for future research in this area.
Unsupervised Belief Representation Learning with Information-Theoretic Variational Graph Auto-Encoders
Authors: Jinning Li, Huajie Shao, Dachun Sun, Ruijie Wang, Yuchen Yan, Jinyang Li, Shengzhong Liu, Hanghang Tong, Tarek F. Abdelzaher
Venue: SIGIR
Year: 2022

This paper develops a novel unsupervised algorithm for belief representation learning in polarized networks that (i) uncovers the latent dimensions of the underlying belief space and (ii) jointly embeds users and content items (that they interact with) into that space in a manner that facilitates a number of downstream tasks, such as stance detection, stance prediction, and ideology mapping. Inspired by total correlation in information theory, we propose the Information-Theoretic Variational Graph Auto-Encoder (InfoVGAE) that learns to project both users and content items (e.g., posts that represent user views) into an appropriate disentangled latent space. To better disentangle latent variables in that space, we develop a total correlation regularization module, a Proportional-Integral (PI) control module, and adopt rectified Gaussian distribution to ensure the orthogonality. The latent representation of users and content can then be used to quantify their ideological leaning and detect/predict their stances on issues. We evaluate the performance of the proposed InfoVGAE on three real-world datasets, of which two are collected from Twitter and one from U.S. Congress voting records. The evaluation results show that our model outperforms state-of-the-art unsupervised models by reducing 10.5% user clustering errors and achieving 12.1% higher F1 scores for stance separation of content items. In addition, InfoVGAE produces a comparable result with supervised models. We also discuss its performance on stance prediction and user ranking within ideological groups.
Training Entire-Space Models for Target-oriented Opinion Words Extraction
Authors: Yuncong Li, Fang Wang, Sheng-Hua Zhong
Venue: SIGIR
Year: 2022

Target-oriented opinion words extraction (TOWE) is a subtask of aspect-based sentiment analysis (ABSA). Given a sentence and an aspect term occurring in the sentence, TOWE extracts the corresponding opinion words for the aspect term. TOWE has two types of instance. In the first type, aspect terms are associated with at least one opinion word, while in the second type, aspect terms do not have corresponding opinion words. However, previous researches trained and evaluated their models with only the first type of instance, resulting in a sample selection bias problem. Specifically, TOWE models were trained with only the first type of instance, while these models would be utilized to make inference on the entire space with both the first type of instance and the second type of instance. Thus, the generalization performance will be hurt. Moreover, the performance of these models on the first type of instance cannot reflect their performance on entire space. To validate the sample selection bias problem, four popular TOWE datasets containing only aspect terms associated with at least one opinion word are extended and additionally include aspect terms without corresponding opinion words. Experimental results on these datasets show that training TOWE models on entire space will significantly improve model performance and evaluating TOWE models only on the first type of instance will overestimate model performance.
Dual Pseudo Supervision for Semi-Supervised Text Classification with a Reliable Teacher
Authors: Shujie Li, Min Yang, Chengming Li, Ruifeng Xu
Venue: SIGIR
Year: 2022

In this paper, we study the semi-supervised text classification (SSTC) by exploring both labeled and extra unlabeled data. One of the most popular SSTC techniques is pseudo-labeling which assigns pseudo labels for unlabeled data via a teacher classifier trained on labeled data. These pseudo labeled data is then applied to train a student classifier. However, when the pseudo labels are inaccurate, the student classifier will learn from inaccurate data and get even worse performance than the teacher. To mitigate this issue, we propose a simple yet efficient pseudo-labeling framework called Dual Pseudo Supervision (DPS), which exploits the feedback signal from the student to guide the teacher to generate better pseudo labels. In particular, we alternately update the student based on the pseudo labeled data annotated by the teacher and optimize the teacher based on the student's performance via meta learning. In addition, we also design a consistency regularization term to further improve the stability of the teacher. With the above two strategies, the learned reliable teacher can provide more accurate pseudo-labels to the student and thus improve the overall performance of text classification. We conduct extensive experiments on three benchmark datasets (i.e., AG News, Yelp and Yahoo) to verify the effectiveness of our DPS method. Experimental results show that our approach achieves substantially better performance than the strong competitors. For reproducibility, we will release our code and data of this paper publicly at https://github.com/GRIT621/DPS.
Deep Page-Level Interest Network in Reinforcement Learning for Ads Allocation
Authors: Guogang Liao, Xiaowen Shi, Ze Wang, Xiaoxu Wu, Chuheng Zhang, Yongkang Wang, Xingxing Wang, Dong Wang
Venue: SIGIR
Year: 2022

A mixed list of ads and organic items is usually displayed in feed and how to allocate the limited slots to maximize the overall revenue is a key problem. Meanwhile, user behavior modeling is essential in recommendation and advertising (e.g., CTR prediction and ads allocation). Most previous works only model point-level positive feedback (i.e., click), which neglect the page-level information of feedback and other types of feedback. To this end, we propose Deep Page-level Interest Network (DPIN) to model the page-level user preference and exploit multiple types of feedback. Specifically, we introduce four different types of page-level feedback, and capture user preference for item arrangement under different receptive fields through the multi-channel interaction module. Through extensive offline and online experiments on Meituan food delivery platform, we demonstrate that DPIN can effectively model the page-level user preference and increase the revenue.
Hierarchical Multi-Task Graph Recurrent Network for Next POI Recommendation
Authors: Nicholas Lim, Bryan Hooi, See-Kiong Ng, Yong Liang Goh, Renrong Weng, Rui Tan
Venue: SIGIR
Year: 2022

Learning which Point-of-Interest (POI) a user will visit next is a challenging task for personalized recommender systems due to the large search space of possible POIs in the region. A recurring problem among existing works that makes it difficult to learn and perform well is the sparsity of the User-POI matrix. In this paper, we propose our Hierarchical Multi-Task Graph Recurrent Network (HMT-GRN) approach, which alleviates the data sparsity problem by learning different User-Region matrices of lower sparsities in a multi-task setting. We then perform a Hierarchical Beam Search (HBS) on the different region and POI distributions to hierarchically reduce the search space with increasing spatial granularity and predict the next POI. Our HBS provides efficiency gains by reducing the search space, resulting in speedups of 5 to 7 times over an exhaustive approach. In addition, we also propose a novel selectivity layer to predict if the next POI has been visited before by the user to balance between personalization and exploration. Experimental results on two real-world Location-Based Social Network (LBSN) datasets show that our model significantly outperforms baseline and the state-of-the-art methods.