A hybrid movie recommender system based on neural networks

Christakou, C.; Stafylopatis, Andreas

doi:10.1109/isda.2005.9

Cited by 47 publications

(21 citation statements)

References 9 publications

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“…Hsu et al [37] used ANN to construct a TV recommender system, using the back-propagation neural network method to train a three-layered neural network. A hybrid recommender system combining CB and CF was proposed by Christakou et al [38] to generate precise recommendations for movies. The content filtering part of the system is based on a trained ANN representing individual user preferences.…”

Section: Computational Intelligence-based Recommendation Techniquesmentioning

confidence: 99%

Recommender system application developments: A survey

Mao

et al. 2015

Decision Support Systems

1,357

546

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A recommender system aims to provide users with personalized online product or service recommendations to handle the increasing online information overload problem and improve customer relationship management. Various recommender system techniques have been proposed since the mid-1990s, and many sorts of recommender system software have been developed recently for a variety of applications. Researchers and managers recognize that recommender systems offer great opportunities and challenges for business, government, education, and other domains, with more recent successful developments of recommender systems for real-world applications becoming apparent. It is thus vital that a high quality, instructive review of current trends should be conducted, not only of the theoretical research results but more importantly of the practical developments in recommender systems. This paper therefore reviews up-to-date application developments of recommender systems, clusters their applications into eight main categories: e-government, e-business, e-commerce/e-shopping, e-library, e-learning, e-tourism, e-resource services and e-group activities, and summarizes the related recommendation techniques used in each category. It systematically examines the reported recommender systems through four dimensions: recommendation methods (such as CF), recommender systems software (such as BizSeeker), real-world application domains (such as e-business) and application platforms (such as mobilebased platforms). Some significant new topics are identified and listed as new directions. By providing a stateof-the-art knowledge, this survey will directly support researchers and practical professionals in their understanding of developments in recommender system applications.

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Section: Computational Intelligence-based Recommendation Techniquesmentioning

confidence: 99%

Recommender system application developments: A survey

Mao

et al. 2015

Decision Support Systems

1,357

546

View full text Add to dashboard Cite

show abstract

“…Since 1990s, recommender systems hav in many product domains, i.e., movies [6] pages [3] with the objective of recommendin to users' profiles [24]. In recent years, much have been developed in recommender sys derive better performance [8,10,22].…”

Section: B Recommender Systemsmentioning

confidence: 99%

Augmenting Chinese Online Video Recommendations by Using Virtual Ratings Predicted by Review Sentiment Classification

Zhang

Ding

Chen

et al. 2010

2010 IEEE International Conference on Data Mining Workshops

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Abstract-In this paper we aim to resolve the recommendation problem by using the virtual ratings in online environments when user rating information is not available. As a matter of fact, in most of current websites especially the Chinese video-sharing ones, the traditional pure rating based collaborative filtering recommender methods are not fully qualified due to the sparsity of rating data. Motivated by our prior work on the investigation of user reviews that broadly appear in such sites, we hence propose a new recommender algorithm by fusing a self-supervised emoticon-integrated sentiment classification approach, by which the missing UserItem Rating Matrix can be substituted by the virtual ratings which are predicted by decomposing user reviews as given to the items. To test the algorithm's practical value, we have first identified the self-supervised sentiment classification's higher performance by comparing it with a supervised approach. Moreover, we conducted a statistic evaluation method to show the effectiveness of our recommender system on improving Chinese online video recommendations' accuracy.

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“…Christakou et al [31] proposed the use of fuzzy aggregation operators, specifically Ordered Weighted Averaging operators (OWA), as a way for constructing hybrid recommender systems by combining the output of two recommendation components: a neural network-based content filtering, and a collaborative filtering component.…”

Section: Proposalsmentioning

confidence: 99%

Fuzzy Tools in Recommender Systems: A Survey

Toledo¹,

Martínez²

2017

IJCIS

152

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Recommender systems are currently successful solutions for facilitating access for online users to the information that fits their preferences and needs in overloaded search spaces. In the last years several methodologies have been developed to improve their performance. This paper is focused on developing a review on the use of fuzzy tools in recommender systems, for detecting the more common research topics and also the research gaps, in order to suggest future research lines for boosting the current developments in fuzzy-based recommender systems. Specifically, it is developed an analysis of the papers focused at such aim, indexed in Thomson Reuters Web of Science database, in terms of they key features, evaluation strategies, datasets employed, and application areas.

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A hybrid movie recommender system based on neural networks

Cited by 47 publications

References 9 publications

Recommender system application developments: A survey

Recommender system application developments: A survey

Augmenting Chinese Online Video Recommendations by Using Virtual Ratings Predicted by Review Sentiment Classification

Fuzzy Tools in Recommender Systems: A Survey

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