Publication Type

Conference Proceeding Article

Version

publishedVersion

Publication Date

7-2020

Abstract

Social collaborative platforms such as GitHub and Stack Overflow have been increasingly used to improve work productivity via collaborative efforts. To improve user experiences in these platforms, it is desirable to have a recommender system that can suggest not only items (e.g., a GitHub repository) to a user, but also activities to be performed on the suggested items (e.g., forking a repository). To this end, we propose a new approach dubbed Keen2Act, which decomposes the recommendation problem into two stages: the Keen and Act steps. The Keen step identifies, for a given user, a (sub)set of items in which he/she is likely to be interested. The Act step then recommends to the user which activities to perform on the identified set of items. This decomposition provides a practical approach to tackling complex activity recommendation tasks while producing higher recommendation quality. We evaluate our proposed approach using two real-world datasets and obtain promising results whereby Keen2Act outperforms several baseline models.

Keywords

activity recommendation, factorization machine, GitHub, social collaborative platform, stack overflow

Discipline

Databases and Information Systems | Software Engineering

Research Areas

Software and Cyber-Physical Systems

Publication

UMAP '20: Proceedings of the 28th ACM Conference on User Modeling, Adaptation and Personalization: July 12-18, Genoa, Virtual

First Page

308

Last Page

312

ISBN

9781450368612

Identifier

10.1145/3340631.3394884

Publisher

ACM

City or Country

New York

Copyright Owner and License

Publisher

Additional URL

https://doi.org/10.1145/3340631.3394884

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