TY - JOUR
T1 - AskMe
T2 - joint individual-level and community-level behavior interaction for question recommendation
AU - Li, Nuo
AU - Guo, Bin
AU - Liu, Yan
AU - Yao, Lina
AU - Liu, Jiaqi
AU - Yu, Zhiwen
N1 - Publisher Copyright:
© 2021, The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.
PY - 2022/1
Y1 - 2022/1
N2 - 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 user’s 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.
AB - 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 user’s 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.
KW - Community question answering
KW - hybrid behaviors interaction
KW - Question recommendation
KW - Sparse data
UR - http://www.scopus.com/inward/record.url?scp=85118512526&partnerID=8YFLogxK
U2 - 10.1007/s11280-021-00964-6
DO - 10.1007/s11280-021-00964-6
M3 - 文章
AN - SCOPUS:85118512526
SN - 1386-145X
VL - 25
SP - 49
EP - 72
JO - World Wide Web
JF - World Wide Web
IS - 1
ER -