Signed Distance-based Deep Memory Recommender

Abstract

Personalized recommendation algorithms learn a user's preference for an item by measuring a distance/similarity between them. However, some of the existing recommendation models (e.g., matrix factorization) assume a linear relationship between the user and item. This approach limits the capacity of recommender systems, since the interactions between users and items in real-world applications are much more complex than the linear relationship. To overcome this limitation, in this paper, we design and propose a deep learning framework called Signed Distance-based Deep Memory Recommender, which captures non-linear relationships between users and items explicitly and implicitly, and work well in both general recommendation task and shopping basket-based recommendation task. Through an extensive empirical study on six real-world datasets in the two recommendation tasks, our proposed approach achieved significant improvement over ten state-of-the-art recommendation models.

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Cited by in corpus (5)

• DaisyRec 2.0: Benchmarking Recommendation for Rigorous Evaluation
• Quaternion-Based Self-Attentive Long Short-Term User Preference Encoding for Recommendation
• Memory Augmented Graph Neural Networks for Sequential Recommendation
• Explainable Recommendations via Attentive Multi-Persona Collaborative Filtering
• Sequential Recommender via Time-aware Attentive Memory Network