AskMe: Joint Individual-level and Community-level Behavior Interaction for Question Recommendation

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 similar users to obtain the community-level behavior interaction. Finally, we propose the way of element-level fusion to mix these two kinds of interactions together to predict the ranking scores. A dataset collected from Zhihu (1126 users, 219434 questions) is utilized to evaluate the performance of the proposed model, and the experimental results show that our model has gained the best performance compared to baseline methods, especially when the historical answering behaviors data is scarce.