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Found 3 papers, 1 papers with code
Maximizing Cumulative User Engagement in Sequential Recommendation: An Online Optimization Perspective
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).
Asymmetric Transitivity Preserving Graph Embedding
In particular, we develop a novel graph embedding algorithm, High-Order Proximity preserved Embedding (HOPE for short), which is scalable to preserve high-order proximities of large scale graphs and capable of capturing the asymmetric transitivity.
Multinomial Logit Bandit with Linear Utility Functions
In each round, the player selects a $K$-cardinality subset from $N$ candidate items, and receives a reward which is governed by a {\it multinomial logit} (MNL) choice model considering both item utility and substitution property among items.
