AskMe: Joint Individual-level and Community-level Behavior Interaction for Question Recommendation
Nuo Li, Bin Guo, Yan Liu, Lina Yao, Jiaqi Liu, Zhiwen, Yu

TL;DR
AskMe improves question recommendation in community Q&A platforms by jointly modeling individual user behaviors and community-level interactions, effectively addressing data sparsity issues.
Contribution
The paper introduces a novel hybrid behavior interaction model that combines individual and community-level data for enhanced question recommendation.
Findings
Achieves superior performance over baseline methods.
Effective in scenarios with limited historical answering data.
Leverages rich behavioral correlations for better recommendations.
Abstract
Questions in Community Question Answering (CQA) sites are recommended to users, mainly based on users' interest extracted from questions that users have answered or have asked. However, there is a general phenomenon that users answer fewer questions while pay more attention to follow questions and vote answers. This can impact the performance when recommending questions to users (for obtaining their answers) by using their historical answering behaviors on existing studies. To address the data sparsity issue, we propose AskMe, which aims to leverage the rich, hybrid behavior interactions in CQA to improve the question recommendation performance. On the one hand, we model the rich correlations between the users' diverse behaviors (e.g., answer, follow, vote) to obtain the individual-level behavior interaction. On the other hand, we model the sophisticated behavioral associations between…
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Taxonomy
TopicsExpert finding and Q&A systems · Topic Modeling · Recommender Systems and Techniques
