most citedModeling the Heterogeneous Duration of User Interest in Time-Dependent Recommendation: A Hidden Semi-Markov Approach

48 citations · 186 across the 6 of their papers we have counts for

collaborators

6 papers

cs.SI2024★ 45 cited

A multi-theoretical kernel-based approach to social network-based recommendation

Xin Li, Mengyue Wang, T. -P. Liang

Recommender systems are a critical component of e-commercewebsites. The rapid development of online social networking services provides an opportunity to explore social networks to…

cs.IR2024★ 48 cited

Modeling the Heterogeneous Duration of User Interest in Time-Dependent Recommendation: A Hidden Semi-Markov Approach

Haidong Zhang, Wancheng Ni, Xin Li +1

Recommender systems are widely used for suggesting books, education materials, and products to users by exploring their behaviors. In reality, users' preferences often change over…

cs.CL2024★ 26 cited

Feature engineering vs. deep learning for paper section identification: Toward applications in Chinese medical literature

Sijia Zhou, Xin Li

Section identification is an important task for library science, especially knowledge management. Identifying the sections of a paper would help filter noise in entity and relation…

q-bio.MN2022★ 18 cited

Global Mapping of Gene/Protein Interactions in PubMed Abstracts: A Framework and an Experiment with P53 Interactions

Xin Li, Hsinchun Chen, Zan Huang +2

Gene/protein interactions provide critical information for a thorough understanding of cellular processes. Recently, considerable interest and effort has been focused on the constr…

q-bio.MN2022★ 30 cited

Gene Function Prediction with Gene Interaction Networks: A Context Graph Kernel Approach

Xin Li, Hsinchun Chen, Jiexun Li +1

Predicting gene functions is a challenge for biologists in the post genomic era. Interactions among genes and their products compose networks that can be used to infer gene functio…

cs.AI2022★ 19 cited

Aggregate effects of advertising decisions: a complex systems look at search engine advertising via an experimental study

Yanwu Yang, Xin Li, Bernard J. Jansen +1

Purpose: We model group advertising decisions, which are the collective decisions of every single advertiser within the set of advertisers who are competing in the same auction or…