Integrating Users’ Contextual Engagements with Their General Preferences: An Interpretable Followee Recommendation Method

Yaxuan Ran (), Jiani Liu () and Yishi Zhang ()
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Yaxuan Ran: School of Business Administration, Zhongnan Universty of Economics and Law, Wuhan 430073, China
Jiani Liu: Faculty of Business and Economics, The University of Hong Kong, Pokfulam, Hong Kong
Yishi Zhang: School of Management, Wuhan University of Technology, Wuhan 430070, China; Research Institute of Digital Governance and Management Decision Innovation, Wuhan University of Technology, Wuhan 430070, China

INFORMS Journal on Computing, 2023, vol. 35, issue 3, 614-632

Abstract: Users’ contextual engagements can affect their decisions about who to follow on online social networks because engaged (versus disengaged) users tend to seek more information about the interested topic and are more likely to follow relevant accounts successively. However, existing followee recommendation methods neglect to consider contextual engagement by only relying on users’ general preferences. In the light of the chronological characteristic of the user’s following behavior, we draw on the engagement theory and propose an interpretable algorithm, namely preference-engagement latent Dirichlet allocation (PE-LDA), which integrates users’ contextual engagements with their general preferences for followee recommendation. Specifically, we suggest that if engaged in the current interest, a user will be more likely to select a followee relevant to that interest. If not, the user tends to select a followee according to their general preference. To implement this framework, we extend the original LDA by (1) introducing an indicator to represent whether the user is engaged in the current interest or not and (2) allowing a potential dependency between a user’s successive interests to describe the condition of contextual engagement. We conduct extensive experiments using a real-world Twitter data set. Results demonstrate the superior performance of PE-LDA compared with several existing methods.

Keywords: followee recommendation; contextual engagement; successive behavior; latent Dirichlet allocation; representation learning (search for similar items in EconPapers)
Date: 2023
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