Enhancing memory-based collaborative filtering for group recommender systems

Ghazarian, Sarik; Nematbakhsh, Mohammad Ali

doi:10.1016/j.eswa.2014.11.042

Cited by 101 publications

(48 citation statements)

References 41 publications

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“…A collaborative filtering based recommendation scheme recommends items and similar groups to a group through associative relationship between users and items that they use [26,32,34,41]. For example, a new group recommendation based on collaborative filtering was proposed [26].…”

Section: Introductionmentioning

confidence: 99%

Social group recommendation based on dynamic profiles and collaborative filtering

Bok

Lim

Yang³

et al. 2016

Neurocomputing

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Section: Introductionmentioning

confidence: 99%

Social group recommendation based on dynamic profiles and collaborative filtering

Bok

Lim

Yang³

et al. 2016

Neurocomputing

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“…This action influences the step on finding similar user to the active use which leads to inaccurate` prediction. Filling in all missing ratings with constant values is considered to be the major drawback to these types of researches [14]. Our proposed method solve this problem by filtering the users according to the neighbors who influence the process of prediction.…”

Section: Related Workmentioning

confidence: 99%

A Novel Imputation-Boosted Technique to Overcome the Unrated Items Issue and Improving the Performance of Collaborative Filtering

2018

Dec. 14-15, 2017 Kuala Lumpur (Malaysia)

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“…But, this approach leads to high time complexity due to the similarity computations on large data [15]. After that, Ghazarian et al in [16] used SVM regression in order to train a model to compute similarities on the item's features. The results achieved from item similarity calculation were then used to make predictions on the missing values of the matrix.…”

Section: Related Workmentioning

confidence: 99%

DBT Recommender: Improved Trustworthiness of Ratings through De-Biasing Tendency of Users

Mukkamula¹

2018

IJIES

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Recent advancements in business strategies marked the significance of e-commerce in marketing any service of the organization. Moreover, users are quiet dependent on the average ratings of the products showcased in the marketing interface in turn these average ratings made remarkable impact on sales phenomena of the product. The average rating of a product is the aggregation of individual users ratings biased with the tendency of the user towards publishing the opinion. The optimistic user tends to give a slight high rating than a neutral judgement and vice versa with a pessimistic user. However, these biased ratings produce an aggregate value that is degraded with its trustworthiness. This paper proposed a novel approach named DBT (De-biased Tendency) Recommender to analyze the bias in product rating which recalculates the average ratings of the products by making user tendencies as part of the process. The solution implemented on a big data environment on demand of high computation complexity involved in the process. Experimental results had shown a significant improvement in the trustworthiness of the product ratings with the proposed approach.

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Enhancing memory-based collaborative filtering for group recommender systems

Cited by 101 publications

References 41 publications

Social group recommendation based on dynamic profiles and collaborative filtering

Social group recommendation based on dynamic profiles and collaborative filtering

A Novel Imputation-Boosted Technique to Overcome the Unrated Items Issue and Improving the Performance of Collaborative Filtering

DBT Recommender: Improved Trustworthiness of Ratings through De-Biasing Tendency of Users

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