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Our goal is to have an effective and interpretable collaborative filtering sys- tem which uses review texts to improve and explicit suggestions. Learning a lan-.
In that sense, we aim at encoding priors on users and items while reading their reviews, using a deep architecture with personalized attention modeling.
Jan 25, 2024 · Collaborative filtering is one of the famous techniques used by the recommender system. Briefly, it can find users with similar item tastes and recommend items ...
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This paper provides a detailed survey of recent works that integrate review texts and also discusses how these review texts are exploited.
Dec 15, 2023 · The current study proposes a collaborative filtering recommendation framework that employs social networks to generate more precise and pertinent ...
Oct 24, 2024 · This paper proposes a deep learning-based recommendation framework leveraging bidirectional gated recurrent units (Bi-GRUs) and an attention-based Recurrent ...
Aug 24, 2020 · It consists of a neural collaborative filtering part that focuses on prediction output, and a text processing part that serves as a regularizer.
Oct 18, 2020 · In this paper, we propose a deep learning framework for explicit recommender systems, named Attention Collaborative Autoencoder (ACAE).
Collaborative Filtering (CF) is the key technique for recommender systems. CF exploits user-item behavior interactions (e.g., clicks) only and hence suffers ...
Oct 22, 2024 · In this paper, we propose a standard framework for developing a recommender system engine based on transformer architecture. We could not ...
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