Long-Tail Recommendation Model Utilizing GRU Dual-Branch Information Collaboration Enhancement

Abstract

The long tail phenomenon has long been present in sequential recommender systems, including both long-tail users and long-tail items. Although there have been many studies aiming at alleviating the long tail issue in sequential recom-mender systems, most of them only focus on either long-tail users or long-tail items unilaterally. However, the issues of both long-tail users and long-tail items frequently coexist. Neglecting one aspect will lead to poor recommendation performance in the other. Additionally, the problem of information scarcity for long-tail users and long-tail items has not been adequately addressed. This paper proposes a long-tail recommendation model utilizing gate recurrent unit dual-branch information collaboration enhancement to jointly alleviate the long-tail issues from the perspectives of both users and items, while enriching long-tail information via information collaboration enhancement. The model consists of two branches, the one for long-tail users and the other for long-tail items. Each branch is responsible for processing its respective information and is trained together to enrich the information of the other branch. Furthermore, the model introduces a preference mechanism that dynamically adjusts user preference and item popularity by calculating the influence factors for users and items, which further alleviate the problem of insufficient information in long-tail recommendation. Experiments are conducted on 6 real datasets from the Amazon series and the proposed model (LT-GRU) is compared with 6 classical models. Compared with the best results achieved by existing long-tail recommendation models, LT-GRU achieves an average improvement of 2.49% and 3.80% in terms of HR and NDCG metrics, respectively. This indicates that this work effectively mitigates the issues of the long-tail user and long-tail item without sacrificing the recommendation performance for mainstream users and popular items.

Keywords

• recommender system; long-tail recommendation; information collaboration enhancement; gated recurrent unit (gru)