Intra-session Context-aware Feed Recommendation in Live Systems
Luo Ji, Gao Liu, Mingyang Yin, Hongxia Yang

TL;DR
This paper introduces INSCAFER, a novel framework that models intra-session context to improve feed recommendation by jointly optimizing total views and clicks, addressing exposure bias in live systems.
Contribution
It proposes a new intra-session context-aware recommendation model that explicitly accounts for user browsing decisions and exposure bias, enhancing session-level performance.
Findings
Model improves key business metrics online
Joint learning of clicks and browsing decisions
Addresses exposure bias in feed recommendation
Abstract
Feed recommendation allows users to constantly browse items until feel uninterested and leave the session, which differs from traditional recommendation scenarios. Within a session, user's decision to continue browsing or not substantially affects occurrences of later clicks. However, such type of exposure bias is generally ignored or not explicitly modeled in most feed recommendation studies. In this paper, we model this effect as part of intra-session context, and propose a novel intra-session Context-aware Feed Recommendation (INSCAFER) framework to maximize the total views and total clicks simultaneously. User click and browsing decisions are jointly learned by a multi-task setting, and the intra-session context is encoded by the session-wise exposed item sequence. We deploy our model online with all key business benchmarks improved. Our method sheds some lights on feed…
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Taxonomy
TopicsRecommender Systems and Techniques · Image and Video Quality Assessment · Mind wandering and attention
