Re-ranking With Constraints on Diversified Exposures for Homepage Recommender System

• URL: http://arxiv.org/abs/2112.07621v1
• Date: Sun, 12 Dec 2021 09:34:50 GMT
• Title: Re-ranking With Constraints on Diversified Exposures for Homepage Recommender System
• Authors: Qi Hao, Tianze Luo, Guangda Huzhang
• Abstract summary: We propose a two-stage architecture of the homepage recommendation system. In the first stage, we develop efficient algorithms for recommending items to proper channels. In the second stage, we provide an ordered list of items in each channel.
• Score: 7.618705258302672
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The homepage recommendation on most E-commerce applications places items in a hierarchical manner, where different channels display items in different styles. Existing algorithms usually optimize the performance of a single channel. So designing the model to achieve the optimal recommendation list which maximize the Click-Through Rate (CTR) of whole homepage is a challenge problem. Other than the accuracy objective, display diversity on the homepage is also important since homogeneous display usually hurts user experience. In this paper, we propose a two-stage architecture of the homepage recommendation system. In the first stage, we develop efficient algorithms for recommending items to proper channels while maintaining diversity. The two methods can be combined: user-channel-item predictive model with diversity constraint. In the second stage, we provide an ordered list of items in each channel. Existing re-ranking models are hard to describe the mutual influence between items in both intra-channel and inter-channel. Therefore, we propose a Deep \& Hierarchical Attention Network Re-ranking (DHANR) model for homepage recommender systems. The Hierarchical Attention Network consists of an item encoder, an item-level attention layer, a channel encoder and a channel-level attention layer. Our method achieves a significant improvement in terms of precision, intra-list average distance(ILAD) and channel-wise Precision@k in offline experiments and in terms of CTR and ILAD in our online systems.

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