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BSL: Understanding and Improving Softmax Loss for Recommendation
Junkang Wu, Jiawei Chen, Jiancan Wu +3
Loss functions steer the optimization direction of recommendation models and are critical to model performance, but have received relatively little attention in recent recommendati…
LLaRA: Large Language-Recommendation Assistant
Jiayi Liao, Sihang Li, Zhengyi Yang +4
Sequential recommendation aims to predict users' next interaction with items based on their past engagement sequence. Recently, the advent of Large Language Models (LLMs) has spark…
Large Language Model Can Interpret Latent Space of Sequential Recommender
Zhengyi Yang, Jiancan Wu, Yanchen Luo +5
Sequential recommendation is to predict the next item of interest for a user, based on her/his interaction history with previous items. In conventional sequential recommenders, a c…
Generate What You Prefer: Reshaping Sequential Recommendation via Guided Diffusion
Zhengyi Yang, Jiancan Wu, Zhicai Wang +3
Sequential recommendation aims to recommend the next item that matches a user's interest, based on the sequence of items he/she interacted with before. Scrutinizing previous studie…
Model-enhanced Contrastive Reinforcement Learning for Sequential Recommendation
Chengpeng Li, Zhengyi Yang, Jizhi Zhang +4
Reinforcement learning (RL) has been widely applied in recommendation systems due to its potential in optimizing the long-term engagement of users. From the perspective of RL, reco…
Understanding Contrastive Learning via Distributionally Robust Optimization
Junkang Wu, Jiawei Chen, Jiancan Wu +3
This study reveals the inherent tolerance of contrastive learning (CL) towards sampling bias, wherein negative samples may encompass similar semantics (\eg labels). However, existi…
MuggleMath: Assessing the Impact of Query and Response Augmentation on Math Reasoning
Chengpeng Li, Zheng Yuan, Hongyi Yuan +6
In math reasoning with large language models (LLMs), fine-tuning data augmentation by query evolution and diverse reasoning paths is empirically verified effective, profoundly narr…
Recommendation Unlearning via Influence Function
Yang Zhang, Zhiyu Hu, Yimeng Bai +3
Recommendation unlearning is an emerging task to serve users for erasing unusable data (e.g., some historical behaviors) from a well-trained recommender model. Existing methods pro…
How Graph Convolutions Amplify Popularity Bias for Recommendation?
Jiajia Chen, Jiancan Wu, Jiawei Chen +3
Graph convolutional networks (GCNs) have become prevalent in recommender system (RS) due to their superiority in modeling collaborative patterns. Although improving the overall acc…
GIF: A General Graph Unlearning Strategy via Influence Function
Jiancan Wu, Yi Yang, Yuchun Qian +3
With the greater emphasis on privacy and security in our society, the problem of graph unlearning -- revoking the influence of specific data on the trained GNN model, is drawing in…
Adap-: Adaptively Modulating Embedding Magnitude for Recommendation
Jiawei Chen, Junkang Wu, Jiancan Wu +3
Recent years have witnessed the great successes of embedding-based methods in recommender systems. Despite their decent performance, we argue one potential limitation of these meth…