Intra-session Context-aware Feed Recommendation in Live Systems
- URL: http://arxiv.org/abs/2210.07815v1
- Date: Fri, 30 Sep 2022 04:21:36 GMT
- Title: Intra-session Context-aware Feed Recommendation in Live Systems
- Authors: Luo Ji and Gao Liu and Mingyang Yin and Hongxia Yang
- Abstract summary: We propose a novel intra-session Context-aware Feed Recommendation framework to maximize the total views and total clicks simultaneously.
Our method sheds some lights on feed recommendation studies which aim to optimize session-level click and view metrics.
- Score: 35.84926743736469
- License: http://creativecommons.org/licenses/by/4.0/
- 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 on Alipay with all key
business benchmarks improved. Our method sheds some lights on feed
recommendation studies which aim to optimize session-level click and view
metrics.
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