Journal ArticleDOI
Semantic-enhanced neural collaborative filtering models in recommender systems
TLDR
In this article , the authors proposed a novel semantic-enhanced neural collaborative filtering (NCF) model for movie rating prediction and movie recommendation based on ontology-like modeling and deep learning.Abstract:
Recommendation systems or recommender systems (RSs) are very popular in entertainment websites. With the combination of neural networks and collaborative filtering, Neural Collaborative Filtering (NCF) recommendation methods have shown their outperformance in making item suggestions. However, the lack of semantic relationships between objects makes the NCF unable to capture the complex user-item interactions. Moreover, traditional NCF is unable to capture the dynamic user preference over time. To address these issues, in this paper, we propose novel semantic-enhanced NCF models which are applied to movie rating prediction and movie recommendation. Therefore, MovieLens and IMDB datasets are taken into account as case studies. The proposed models are the integration of ontology-like modeling and deep learning for recommendation tasks into two parts:(1) building the semantic knowledge base for movies and (2) building the user behavior analytic model that has semantic knowledge inference on the knowledge base combined with the sequential preference learned from user sessions, input into the NCF module for making predictions or recommendations. Several experiments have been conducted to show their better recommendation performance than the traditional NCF model. • Building the semantic knowledge base for enhancing deep learning models. • Building the user behavior analytic model that has semantic knowledge inference on the knowledge base combined with the sequential preference learned from sequential data, input into the Neural Collaborative Filtering module for making predictions and recommendations. read more
Citations
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Journal ArticleDOI
E-Learning Course Recommender System Using Collaborative Filtering Models
Kalyan Kumar Jena,Sourav Kumar Bhoi,Tushar Kanta Malik,Kshira Sagar Sahoo,N.Z. Jhanjhi,Sajal Bhatia,Fathi Amsaad +6 more
TL;DR: In this paper , a recommender system is proposed using the collaborative filtering mechanism for e-Learning course recommendation, which makes recommendations for users in selecting the desired option based on their preferences.
Journal ArticleDOI
RDERL: Reliable deep ensemble reinforcement learning-based recommender system
TL;DR: In this paper , a reliable recommendation method is developed, which employs deep neural networks and reinforcement learning, and a recommendation strategy is developed based on the integration of the predicted ratings and their reliability values.
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