Social knowledge-based recommender system. Application to the movies domain
Walter Carrer-Neto,Maria Luisa Hernández-Alcaraz,Rafael Valencia-García,Francisco García-Sánchez +3 more
TLDR
A hybrid recommender system based on knowledge and social networks is presented and its evaluation in the cinematographic domain yields very promising results compared to state-of-the-art solutions.Abstract:
With the advent of the Social Web and the growing popularity of Web 2.0 applications, recommender systems are gaining momentum. The recommendations generated by these systems aim to provide end users with suggestions about information items, social elements, products or services that are likely to be of their interest. The traditional syntactic-based recommender systems suffer from a number of shortcomings that hamper their effectiveness. As semantic technologies mature, they provide a consistent and reliable basis for dealing with data at the knowledge level. Adding semantically empowered techniques to recommender systems can significantly improve the overall quality of recommendations. In this work, a hybrid recommender system based on knowledge and social networks is presented. Its evaluation in the cinematographic domain yields very promising results compared to state-of-the-art solutions.read more
Citations
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Journal ArticleDOI
Recommender systems survey
TL;DR: An overview of recommender systems as well as collaborative filtering methods and algorithms is provided, which explains their evolution, provides an original classification for these systems, identifies areas of future implementation and develops certain areas selected for past, present or future importance.
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Collaborative filtering and deep learning based recommendation system for cold start items
TL;DR: Two recommendation models to solve the CCS and ICS problems for new items are proposed, which are based on a framework of tightly coupled CF approach and deep learning neural network.
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Knowledge-based recommendation: a review of ontology-based recommender systems for e-learning
TL;DR: This study shows that use of ontology for knowledge representation in e-learning recommender systems can improve the quality of recommendations and hybridization of knowledge-based recommendation with other recommendation techniques can enhance the effectiveness of e- learning recommenders.
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Dealing with the new user cold-start problem in recommender systems: A comparative review
TL;DR: A classification that divides the relevant studies addressing the new user cold-start problem into three major groups and summarize their advantages and disadvantages in a tabular format is presented and NHSM achieves better accuracy and computational time than the relevant methods.
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Tag-aware recommender systems: a state-of-the-art survey
TL;DR: This article summarizes recent progress about tag-aware recommender systems, emphasizing on the contributions from three mainstream perspectives and approaches: network-based methods, tensor- based methods, and the topic-based Methods.
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