31 citations · 63 across the 16 of their papers we have counts for
7 papers · 1 filter
IP2: Entity-Guided Interest Probing for Personalized News Recommendation
Youlin Wu, Yuanyuan Sun, Xiaokun Zhang +4
News recommender systems aim to provide personalized news reading experiences for users based on their reading history. Behavioral science studies suggest that screen-based news re…
Rethinking Contrastive Learning in Session-based Recommendation
Xiaokun Zhang, Bo Xu, Fenglong Ma +3
Session-based recommendation aims to predict intents of anonymous users based on limited behaviors. With the ability in alleviating data sparsity, contrastive learning is prevailin…
A Survey on Side Information-driven Session-based Recommendation: From a Data-centric Perspective
Xiaokun Zhang, Bo Xu, Chenliang Li +4
Session-based recommendation is gaining increasing attention due to its practical value in predicting the intents of anonymous users based on limited behaviors. Emerging efforts in…
Don't Click the Bait: Title Debiasing News Recommendation via Cross-Field Contrastive Learning
Yijie Shu, Xiaokun Zhang, Youlin Wu +3
News recommendation emerges as a primary means for users to access content of interest from the vast amount of news. The title clickbait extensively exists in news domain and incre…
FineRec:Exploring Fine-grained Sequential Recommendation
Xiaokun Zhang, Bo Xu, Youlin Wu +3
Sequential recommendation is dedicated to offering items of interest for users based on their history behaviors. The attribute-opinion pairs, expressed by users in their reviews fo…
Disentangling ID and Modality Effects for Session-based Recommendation
Xiaokun Zhang, Bo Xu, Zhaochun Ren +3
Session-based recommendation aims to predict intents of anonymous users based on their limited behaviors. Modeling user behaviors involves two distinct rationales: co-occurrence pa…
Side Information-Driven Session-based Recommendation: A Survey
Xiaokun Zhang, Bo Xu, Chenliang Li +3
The session-based recommendation (SBR) garners increasing attention due to its ability to predict anonymous user intents within limited interactions. Emerging efforts incorporate v…