579 citations · 3k across the 98 of their papers we have counts for
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Causal Distillation for Alleviating Performance Heterogeneity in Recommender Systems
Shengyu Zhang, Ziqi Jiang, Jiangchao Yao +7
Recommendation performance usually exhibits a long-tail distribution over users -- a small portion of head users enjoy much more accurate recommendation services than the others. W…
Temporally and Distributionally Robust Optimization for Cold-Start Recommendation
Xinyu Lin, Wenjie Wang, Jujia Zhao +3
Collaborative Filtering (CF) recommender models highly depend on user-item interactions to learn CF representations, thus falling short of recommending cold-start items. To address…
Bridging Items and Language: A Transition Paradigm for Large Language Model-Based Recommendation
Xinyu Lin, Wenjie Wang, Yongqi Li +3
Harnessing Large Language Models (LLMs) for recommendation is rapidly emerging, which relies on two fundamental steps to bridge the recommendation item space and the language space…
Context-aware Event Forecasting via Graph Disentanglement
Yunshan Ma, Chenchen Ye, Zijian Wu +3
Event forecasting has been a demanding and challenging task throughout the entire human history. It plays a pivotal role in crisis alarming and disaster prevention in various aspec…
Generative Recommendation: Towards Next-generation Recommender Paradigm
Wenjie Wang, Xinyu Lin, Fuli Feng +2
Recommender systems typically retrieve items from an item corpus for personalized recommendations. However, such a retrieval-based recommender paradigm faces two limitations: 1) th…
Causal Disentangled Recommendation Against User Preference Shifts
Wenjie Wang, Xinyu Lin, Liuhui Wang +3
Recommender systems easily face the issue of user preference shifts. User representations will become out-of-date and lead to inappropriate recommendations if user preference has s…
Causal Inference for Knowledge Graph based Recommendation
Yinwei Wei, Xiang Wang, Liqiang Nie +3
Knowledge Graph (KG), as a side-information, tends to be utilized to supplement the collaborative filtering (CF) based recommendation model. By mapping items with the entities in K…
Incorporating Bias-aware Margins into Contrastive Loss for Collaborative Filtering
An Zhang, Wenchang Ma, Xiang Wang +1
Collaborative filtering (CF) models easily suffer from popularity bias, which makes recommendation deviate from users' actual preferences. However, most current debiasing strategie…
CCL4Rec: Contrast over Contrastive Learning for Micro-video Recommendation
Shengyu Zhang, Bofang Li, Dong Yao +7
Micro-video recommender systems suffer from the ubiquitous noises in users' behaviors, which might render the learned user representation indiscriminating, and lead to trivial reco…
Re4: Learning to Re-contrast, Re-attend, Re-construct for Multi-interest Recommendation
Shengyu Zhang, Lingxiao Yang, Dong Yao +5
Effectively representing users lie at the core of modern recommender systems. Since users' interests naturally exhibit multiple aspects, it is of increasing interest to develop mul…
Intelligent Request Strategy Design in Recommender System
Xufeng Qian, Yue Xu, Fuyu Lv +6
Waterfall Recommender System (RS), a popular form of RS in mobile applications, is a stream of recommended items consisting of successive pages that can be browsed by scrolling. In…
CrossCBR: Cross-view Contrastive Learning for Bundle Recommendation
Yunshan Ma, Yingzhi He, An Zhang +2
Bundle recommendation aims to recommend a bundle of related items to users, which can satisfy the users' various needs with one-stop convenience. Recent methods usually take advant…
User-controllable Recommendation Against Filter Bubbles
Wenjie Wang, Fuli Feng, Liqiang Nie +1
Recommender systems usually face the issue of filter bubbles: overrecommending homogeneous items based on user features and historical interactions. Filter bubbles will grow along…
A Unified Multi-task Learning Framework for Multi-goal Conversational Recommender Systems
Yang Deng, Wenxuan Zhang, Weiwen Xu +3
Recent years witnessed several advances in developing multi-goal conversational recommender systems (MG-CRS) that can proactively attract users' interests and naturally lead user-e…
Interacting with Non-Cooperative User: A New Paradigm for Proactive Dialogue Policy
Wenqiang Lei, Yao Zhang, Feifan Song +5
Proactive dialogue system is able to lead the conversation to a goal topic and has advantaged potential in bargain, persuasion and negotiation. Current corpus-based learning manner…
KuaiRec: A Fully-observed Dataset and Insights for Evaluating Recommender Systems
Chongming Gao, Shijun Li, Wenqiang Lei +6
The progress of recommender systems is hampered mainly by evaluation as it requires real-time interactions between humans and systems, which is too laborious and expensive. This is…
Learning Robust Recommender from Noisy Implicit Feedback
Wenjie Wang, Fuli Feng, Xiangnan He +2
The ubiquity of implicit feedback makes it indispensable for building recommender systems. However, it does not actually reflect the actual satisfaction of users. For example, in E…
Contrastive Learning for Cold-Start Recommendation
Yinwei Wei, Xiang Wang, Qi Li +4
Recommending cold-start items is a long-standing and fundamental challenge in recommender systems. Without any historical interaction on cold-start items, CF scheme fails to use co…
Leveraging Two Types of Global Graph for Sequential Fashion Recommendation
Yujuan Ding, Yunshan Ma, Wai Keung Wong +1
Sequential fashion recommendation is of great significance in online fashion shopping, which accounts for an increasing portion of either fashion retailing or online e-commerce. Th…
Deconfounded Recommendation for Alleviating Bias Amplification
Wenjie Wang, Fuli Feng, Xiangnan He +2
Recommender systems usually amplify the biases in the data. The model learned from historical interactions with imbalanced item distribution will amplify the imbalance by over-reco…
Learning Intents behind Interactions with Knowledge Graph for Recommendation
Xiang Wang, Tinglin Huang, Dingxian Wang +4
Knowledge graph (KG) plays an increasingly important role in recommender systems. A recent technical trend is to develop end-to-end models founded on graph neural networks (GNNs).…
Advances and Challenges in Conversational Recommender Systems: A Survey
Chongming Gao, Wenqiang Lei, Xiangnan He +2
Recommender systems exploit interaction history to estimate user preference, having been heavily used in a wide range of industry applications. However, static recommendation model…
Clicks can be Cheating: Counterfactual Recommendation for Mitigating Clickbait Issue
Wenjie Wang, Fuli Feng, Xiangnan He +2
Recommendation is a prevalent and critical service in information systems. To provide personalized suggestions to users, industry players embrace machine learning, more specificall…
Explainable Reasoning over Knowledge Graphs for Recommendation
Xiang Wang, Dingxian Wang, Canran Xu +3
Incorporating knowledge graph into recommender systems has attracted increasing attention in recent years. By exploring the interlinks within a knowledge graph, the connectivity be…