  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.
