Neighbor Based Enhancement for the Long-Tail Ranking Problem in Video Rank Models

• URL: http://arxiv.org/abs/2302.08128v1
• Date: Thu, 16 Feb 2023 07:38:51 GMT
• Title: Neighbor Based Enhancement for the Long-Tail Ranking Problem in Video Rank Models
• Authors: Xuanji Xiao, Ziyu He
• Abstract summary: We propose a novel neighbor enhancement structure to help train the representation of the target user or item. Experiments on the well-known public dataset MovieLens 1M demonstrate the efficiency of the method.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Rank models play a key role in industrial recommender systems, advertising, and search engines. Existing works utilize semantic tags and user-item interaction behaviors, e.g., clicks, views, etc., to predict the user interest and the item hidden representation for estimating the user-item preference score. However, these behavior-tag-based models encounter great challenges and reduced effectiveness when user-item interaction activities are insufficient, which we called "the long-tail ranking problem". Existing rank models ignore this problem, but its common and important because any user or item can be long-tailed once they are not consistently active for a short period. In this paper, we propose a novel neighbor enhancement structure to help train the representation of the target user or item. It takes advantage of similar neighbors (static or dynamic similarity) with multi-level attention operations balancing the weights of different neighbors. Experiments on the well-known public dataset MovieLens 1M demonstrate the efficiency of the method over the baseline behavior-tag-based model with an absolute CTR AUC gain of 0.0259 on the long-tail user dataset.

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