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Not All Videos Become Outdated: Short-Video Recommendation by Learning to Deconfound Release Interval Bias

Published: 08 October 2024 Publication History

Abstract

Short-video recommender systems often exhibit a biased preference to recently released videos. However, not all videos become outdated; certain classic videos can still attract user’s attention. Such bias along temporal dimension can be further aggravated by the matching model between users and videos, because the model learns from preexisting interactions. From real data, we observe that different videos have varying sensitivities to recency in attracting users’ attention. Our analysis, based on a causal graph modeling short-video recommendation, suggests that the release interval serves as a confounder, establishing a backdoor path between users and videos. To address this confounding effect, we propose a model-agnostic causal architecture called Learning to Deconfound the Release Interval Bias (LDRI). LDRI enables jointly learning of the matching model and the video recency sensitivity perceptron. In the inference stage, we apply a backdoor adjustment, effectively blocking the backdoor path by intervening on each video. Extensive experiments on two benchmarks demonstrate that LDRI consistently outperforms backbone models and exhibits superior performance against state-of-the-art models. Additional comprehensive analyses confirm the deconfounding capability of LDRI.

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    cover image ACM Conferences
    RecSys '24: Proceedings of the 18th ACM Conference on Recommender Systems
    October 2024
    1438 pages
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    Published: 08 October 2024

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    Author Tags

    1. Causal Inference
    2. Recommender System
    3. Release Interval Bias

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    • Chen Guang project supported by Shanghai Municipal Education Commission and Shanghai Education Development Foundation
    • National Natural Science Foundation of China
    • Shanghai Pujiang Program
    • Fundamental Research Funds for the Central Universities, China
    • 2024 Innovation Evaluation Open Fund, Fudan University
    • Shanghai Planning Office of Philosophy and Social Science Youth Project

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