A new similarity measure using Bhattacharyya coefficient for collaborative filtering in sparse data

Patra, Binoy Krishna; Launonen, Raimo; Ollikainen, Ville; Nandi, Sukumar

doi:10.1016/j.knosys.2015.03.001

Cited by 227 publications

(113 citation statements)

References 38 publications

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“…This dataset exists in three different sizes (100 KB, 1 MB, and 10 MB ratings). In this study, 1 MB dataset is used for testing [24,39]. MovieLens dataset 1 M has 1,000,209 ratings on 3883 films by 6040 users.…”

Section: Implementation and Evaluation Of The Resultsmentioning

confidence: 99%

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A Fast Recommender System for Cold User Using Categorized Items

Jazayeriy

Mohammadi

Shamshirband

2018

MCA

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Abstract:In recent years, recommender systems (RS) provide a considerable progress to users. RSs reduce the cost of a user's time in order to reach to desired results faster. The main issue of RSs is the presence of cold users which are less active and their preferences are more difficult to detect. The aim of this study is to provide a new way to improve recall and precision in recommender systems for cold users. According to the available categories of items, prioritization of the proposed items is improved and then presented to the cold user. The obtained results show that in addition to increased speed of processing, recall and precision have an acceptable improvement.

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Section: Implementation and Evaluation Of The Resultsmentioning

confidence: 99%

“…Cold starts are a challenging problem in collaborative filtering [15,[24][25][26]. It arises in two cases: "cold user" and "cold item" cases.…”

Section: Introductionmentioning

confidence: 99%

A Fast Recommender System for Cold User Using Categorized Items

Jazayeriy

Mohammadi

Shamshirband

2018

MCA

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“…Moreover, two users or items can be similar even if they do not have overlaps. Patra et al [27] propose a new similarity measure that does not depend on co-rated items unlike other measures and it uses the Bhattacharyya measure that has been already used in signal processing, image processing and pattern recognition. The Bhattacharyya measure is used to measure the divergence of two probability distributions.…”

Section: Union-based Similaritymentioning

confidence: 99%

Measures of Similarity in Memory-Based Collaborative Filtering Recommender System

Stephen¹,

Xie²,

Rai³

2017

Proceedings of the 4th Multidisciplinary International Social Networks Conference

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Collaborative filtering (CF) technique in recommender systems (RS) is a well-known and popular technique that exploits relationships between users or items to make product recommendations to an active user. The effectiveness of existing memory based algorithms depend on the similarity measure that is used to identify nearest neighbours. However, similarity measures utilize only the ratings of co-rated items while computing the similarity between a pair of users or items. In most of the ecommerce applications, the rating matrix is too sparse since even active users of an online system tend to rate only a few items of the entire set of items. Therefore, co-rated items among users are even sparser. Moreover, the ratings a user gives an individual item tells us nothing about his comprehensive interest without which the generated recommendations may not be satisfactory to a user. In order to be able to address these issues, a comprehensive study is made of the various existing measures of similarity in a collaborative filtering recommender system (CFRS) and a hierarchical categorization of products has been proposed to understand the interest of a user in a wider scope so as to provide better recommendations as well as to alleviate data sparsity. CCS ConceptsHuman-centered computing~Empirical studies in collaborative and social computing • Applied computing~Online shopping

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“…Liu [9] proposes algorithm of collaborative filtering based on user interest, by building interest intensity model and discovering interest correlation among different items through that model. Patra et al [10] propose a new similarity measure using Bhattacharyya coefficient for collaborative filtering in sparse data. Cheng et al [11] propose a new collaborative filtering recommendation method based on users' interest sequences (ISCF).…”

Section: Related Workmentioning

confidence: 99%

A User Interest Recommendation Based on Collaborative Filtering

Wu¹,

Wang²,

Liu³

et al. 2016

Proceedings of the 2016 2nd International Conference on Artificial Intelligence and Industrial Engineering (AIIE 2016)

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Abstract-The traditional collaborative filtering algorithm cannot response user interest with time and is lack of time effectiveness. These problems lead to poor recommendation quality. On the basis of the neighbor-based collaborative filtering, a fused method of improved similarity and user interest is proposed. To begin with, we compute similarity from global perspectives obtained with Jaccard similarity, local perspectives obtained with Bhattacharyya Coefficient. Furthermore, we adopt the forgetting curve to represent the user interest preference, adding the interest weight to the new similarity method to update user interest. Finally, we make recommendation prediction by calculating similarity using the method. Experimental results on the Movielens datasets demonstrate that our approach has advantages over state-of-the-art methods in terms of both the discovery of user interest preference and providing highly accuracy recommendations.

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A new similarity measure using Bhattacharyya coefficient for collaborative filtering in sparse data

Cited by 227 publications

References 38 publications

A Fast Recommender System for Cold User Using Categorized Items

A Fast Recommender System for Cold User Using Categorized Items

Measures of Similarity in Memory-Based Collaborative Filtering Recommender System

A User Interest Recommendation Based on Collaborative Filtering

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