Generalized Delayed Feedback Model with Post-Click Information in Recommender Systems

• URL: http://arxiv.org/abs/2206.00407v1
• Date: Wed, 1 Jun 2022 11:17:01 GMT
• Title: Generalized Delayed Feedback Model with Post-Click Information in Recommender Systems
• Authors: Jia-Qi Yang, De-Chuan Zhan
• Abstract summary: We show that post-click user behaviors are also informative to conversion rate prediction and can be used to improve timeliness. We propose a generalized delayed feedback model (GDFM) that unifies both post-click behaviors and early conversions as post-click information.
• Score: 37.72697954740977
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Predicting conversion rate (e.g., the probability that a user will purchase an item) is a fundamental problem in machine learning based recommender systems. However, accurate conversion labels are revealed after a long delay, which harms the timeliness of recommender systems. Previous literature concentrates on utilizing early conversions to mitigate such a delayed feedback problem. In this paper, we show that post-click user behaviors are also informative to conversion rate prediction and can be used to improve timeliness. We propose a generalized delayed feedback model (GDFM) that unifies both post-click behaviors and early conversions as stochastic post-click information, which could be utilized to train GDFM in a streaming manner efficiently. Based on GDFM, we further establish a novel perspective that the performance gap introduced by delayed feedback can be attributed to a temporal gap and a sampling gap. Inspired by our analysis, we propose to measure the quality of post-click information with a combination of temporal distance and sample complexity. The training objective is re-weighted accordingly to highlight informative and timely signals. We validate our analysis on public datasets, and experimental performance confirms the effectiveness of our method.

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