A Hybrid Recommender System Using Weighted Ensemble Similarity Metrics and Digital Filters

Laveti, Ramesh Naidu; Ch, Janaki; Pal, Supriya N.; Babu, Nithin

doi:10.1109/hipcw.2016.013

Cited by 9 publications

(2 citation statements)

References 26 publications

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“…This metric is superior to PIP in terms of performance and computation time. Laveti et al [33] proposed a weighted hybrid ensemble similarity metric combining two or more conventional approaches that demonstrates an improvement in recommendation accuracy; however, it relies on a high number of rating samples and neighbors to achieve high performance. Guo et al [27] defined a Bayesian similarity measure based on the Dirichlet distribution that considers the direction and length of the rating vectors while also considering the rating semantics of all rating pairs.…”

Section: B Similarity Measuresmentioning

confidence: 99%

Switching Hybrid Method Based on User Similarity and Global Statistics for Collaborative Filtering

Ortal

Edahiro

2020

IEEE Access

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Collaborative filtering (CF) is a technique used in recommender systems to provide meaningful suggestions based on known feedback obtained from like-minded users. The measure of similarity plays a critical role in the performance of neighborhood-based CF methods. However, conventional similarity measures suffer from limitations because they only consider the direction of the rating vectors. We propose a novel similarity measure that considers the semantic nuances of the ratings; in particular, it weights the contributions of ratings in proportion to the users' degree of indifference towards the items. Additionally, to address the sparsity problem that affects the performance of CF techniques, we propose a switching hybrid method that predicts user ratings based on either our custom similarity measure or through user and item biases. We evaluated the proposed method on six different datasets and compared it with other CF methods. The results show that the proposed recommender consistently outperforms those using conventional similarity measures when the sparsity of the dataset is high.

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Section: B Similarity Measuresmentioning

confidence: 99%

Switching Hybrid Method Based on User Similarity and Global Statistics for Collaborative Filtering

Ortal

Edahiro

2020

IEEE Access

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“…The rating toward item is the most used criterion for item selection [12,13]. However, most studies use only a comprehensive score, which results in a serious loss of information [14,15]. In recent years, with the development of text analysis technology, several researches have combined ratings and online reviews to select items [16,17].…”

Section: Introductionmentioning

confidence: 99%

A comprehensive mechanism for hotel recommendation to achieve personalized search engine

Huang

Zhang

Wang

2018

IFS

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Search engines are as important as recommender systems for hotel selections. However, the recommended lists of search engines are usually non-personalized and low accuracy. In order to deal with these issues in search engines, a comprehensive mechanism for hotel recommendation is proposed. In this mechanism, we consider users' personalized preferences by identifying users' attributes about interest, trust and consumption capacity. Meanwhile, the quantification method for each attribute is presented by using fuzzy theory. Moreover, this paper improves the method to evaluate the hotel, which respects to the criteria price, rating, and online review by using fuzzy theory. In addition, this proposed approach uses TOPSIS, a classical multi-criteria decision making method, to improve the accuracy further. Finally, a case study is conducted based on Tripadvisor.com to illustrate the validity of the proposed method for hotel recommendation in search engines. The results of the case study indicate that it not only solves the problem of non-personalization, but also improves the accuracy in search engine.

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Emotion and Collaborative-Based Music Recommendation System

Aparna¹,

Chandana²,

Jayashree³

et al. 2022

Intelligent Data Communication Technologies and Internet of Things

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A Hybrid Recommender System Using Weighted Ensemble Similarity Metrics and Digital Filters

Cited by 9 publications

References 26 publications

Switching Hybrid Method Based on User Similarity and Global Statistics for Collaborative Filtering

Switching Hybrid Method Based on User Similarity and Global Statistics for Collaborative Filtering

A comprehensive mechanism for hotel recommendation to achieve personalized search engine

Emotion and Collaborative-Based Music Recommendation System

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