Maximizing Cumulative User Engagement in Sequential Recommendation: An Online Optimization Perspective - CrossMinds.ai
Maximizing Cumulative User Engagement in Sequential Recommendation: An Online Optimization Perspective
Aug 13, 202023 views
Yifei Zhao
To maximize cumulative user engagement (e.g. cumulative clicks),in sequential recommendation, it is often needed to tradeoff two,potentially conflicting objectives, that is, pursuing higher immediate user engagement (e.g., click-through rate) and encouraging,user browsing (i.e., more items exposured). Existing works often,study these two tasks separately, thus tend to result in sub-optimal,results. In this paper, we study this problem from an online optimization perspective, and propose a flexible and practical framework to,explicitly tradeoff longer user browsing length and high immediate user engagement. Specifically, by considering items as actions,,user’s requests as states and user leaving as an absorbing state, we,formulate each user’s behavior as a personalized,Markov decision,process,(MDP), and the problem of maximizing cumulative user,engagement is reduced to a,stochastic shortest path,(SSP) problem.,Meanwhile, with immediate user engagement and quit probability,estimation, it is shown that the SSP problem can be efficiently solved,via dynamic programming. Experiments on real-world datasets,demonstrate the effectiveness of the proposed approach. Moreover,,this approach is deployed at a large E-commerce platform, achieved,over,7%,improvement of cumulative clicks.
SIGKDD_2020
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