Enhancing Session-Based Recommendations with GRU4Rec and ReChorus
Keywords:
Evolution, GRU4Rec, Recommender Systems, Session-based Algorithms, , Recurrent Neural Networks (RNNs), Training Framework, ReChorus, PyTorch, Top-K Recommendation, Implicit Feedback, State-of-the-Art Metrics, NDCG, Hit Rate, Empirical Evaluations.Abstract
Recommender systems have evolved from basic item-to-item recommendations to sophisticated, session-based algorithms. A pivotal model in this transition is GRU4Rec, which employs Recurrent Neural Networks (RNNS) for session-based recommendations. While GRU4Rec has shown marked improvements over traditional methods, its effective deployment necessitates a robust training frame- work. This paper leverages ReChorus, a PyTorch frame work designed for top-K recommendation with implicit feedback, to train the GRU4Rec model. ReChorus offers a streamlined model design process, high efficiency, and flexibility, making it well-suited for achieving state-of-the art metrics, specifically NDCG and Hit Rate. Empirical evaluations across multiple datasets confirm that this approach successfully matches existing state-of- the-art metrics in the field of Recommender Systems.
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