CROP adds a conservative reward objective to model-based offline policy optimization that jointly reduces estimation error and random-action rewards to produce robust conservative estimates and mitigate distribution shift.
Deep Learning based Recommender System: A Survey and New Perspectives
3 Pith papers cite this work. Polarity classification is still indexing.
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Pith papers citing it
abstract
With the ever-growing volume of online information, recommender systems have been an effective strategy to overcome such information overload. The utility of recommender systems cannot be overstated, given its widespread adoption in many web applications, along with its potential impact to ameliorate many problems related to over-choice. In recent years, deep learning has garnered considerable interest in many research fields such as computer vision and natural language processing, owing not only to stellar performance but also the attractive property of learning feature representations from scratch. The influence of deep learning is also pervasive, recently demonstrating its effectiveness when applied to information retrieval and recommender systems research. Evidently, the field of deep learning in recommender system is flourishing. This article aims to provide a comprehensive review of recent research efforts on deep learning based recommender systems. More concretely, we provide and devise a taxonomy of deep learning based recommendation models, along with providing a comprehensive summary of the state-of-the-art. Finally, we expand on current trends and provide new perspectives pertaining to this new exciting development of the field.
verdicts
UNVERDICTED 3representative citing papers
NeuCDCF is a wide-and-deep neural architecture for cross-domain collaborative filtering that jointly learns matrix factorization and deep representations, reporting better performance than prior CDCF models on four real-world datasets.
J-NCF jointly optimizes deep feature extraction and non-linear interaction modeling on rating data with a combined loss, reporting up to 15% gains in NDCG@10 over baselines on MovieLens and Amazon datasets.
citing papers explorer
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CROP: Conservative Reward for Model-based Offline Policy Optimization
CROP adds a conservative reward objective to model-based offline policy optimization that jointly reduces estimation error and random-action rewards to produce robust conservative estimates and mitigate distribution shift.
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Neural Cross-Domain Collaborative Filtering with Shared Entities
NeuCDCF is a wide-and-deep neural architecture for cross-domain collaborative filtering that jointly learns matrix factorization and deep representations, reporting better performance than prior CDCF models on four real-world datasets.
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Joint Neural Collaborative Filtering for Recommender Systems
J-NCF jointly optimizes deep feature extraction and non-linear interaction modeling on rating data with a combined loss, reporting up to 15% gains in NDCG@10 over baselines on MovieLens and Amazon datasets.