Collaborative filtering-based recommender systems by effective trust

Faridani, Vahid; Jalali, Mehrdad; Jahan, Majid Vafaei

doi:10.1007/s41060-017-0049-y

Cited by 16 publications

(7 citation statements)

References 23 publications

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“…Also, hybrid techniques need to be considered that can handle the sparsity issue of RS and build a model that can provide accurate and fast recommendations. Recent studies show that integrating the hierarchy of user or item preferences [17], group recommender systems [19] and interactive recommender systems [6] can also increase the performance of online recommender systems which can also be taken into consideration.…”

Section: Discussionmentioning

confidence: 99%

An Efficient Recommender System Technique in Social Networks Based on Association Rule Based Mining

Nandi¹

2019

IJITEE

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A recommender system is an information filtering system that has become a buzzword in various areas of marketing and research such as movies, music, books, products and research articles. The main role of recommender systems is to guide users on a personal level to provide an optimum set of suggestions based on the users’ taste, explicit rating of items, his/her demographic and other related valuable information. In the past decade, several approaches have been discussed for recommendation of items to online users keeping in mind the accuracy of prediction, the cold-start problem and the problem of sparsity. However, most of these existing recommender system techniques have failed to define a proper recommendation model that can be well suited for any online social network and can consider majority of the limitations when modeling real-market recommendations. In this paper, we present a novel efficient recommender system technique RecGyp and many other standard commonly used existing prediction techniques and also perform experimental evaluations to make a comparative analysis among each technique. The experiments carried out on the MovieLens 100K and the Yahoo! Webscope Movie datasets demonstrate the superior nature of the proposed RecGyp technique in solving the scalability issue and accuracy of results for recommending items to users of a social network. In addition to the traditional similarity measurements evaluation, results are also provided for three evaluation metrics, Precision, Recall and ROC, to evaluate the accuracy of all the recommender system techniques

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Section: Discussionmentioning

confidence: 99%

An Efficient Recommender System Technique in Social Networks Based on Association Rule Based Mining

Nandi¹

2019

IJITEE

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“…Such as, [58] the authors proposed an algorithm based on a trust model to enhance the quality of ratings provided by a mobile ad hoc network (MANET). In [59] the authors propose a novel trust approach known as Effective Trust, which is a combination of trust neighbours and classical CF techniques to overcome the data sparsity limitation by using MoleTrust algorithm. • Cold Start: Cold start is one of the common limitations of the recommendation system which mostly appears with data sparsity.…”

Section: B Limitations Of Recommendation Systemmentioning

confidence: 99%

The Role of Trust to Enhance the Recommendation System Based on Social Network

Tharwat

Jacob

Fudzee

et al. 2020

Int. J. Adv. Sci. Eng. Inf. Technol.

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Recommendation systems or recommender system (RSs) is one of the hottest topics nowadays, which is widely utilized to predict an item to the end-user based on his/her preferences primary. Recommendation systems applied in many areas mainly in commercial applications. This work aims to collect evidence of utilizing social network information between users to enhance the quality of traditional recommendation system. It provides an overview of traditional and modern approaches used by RSs such as collaborative filter (CF) approach, content-based (CB) approach, and hybrid filter approach. CF is one of the most famous traditional approaches in RSs, which is facing many limitations due to the lack of information available during a performance such as Cold start, Sparsity and Shilling attack. Additionally, this content focused on the role of incorporating a trust relationship from the social network to enhance the weaknesses of CF and achieve better quality in the recommendation process. Trust-aware Recommendation Systems (TaRSs) is a modern approach proposed to overcome the limitations of CF recommendation system in a social network. The trust relationship between users can boost and enhance CF limitations. Many researchers are focusing on trust in the recommendation system but fewer works are highlighting the role of trust in the recommendation system. In the end, limitations, and open issues of the current picture of the recommendation system come across.

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“…To achieve the best recommendation accuracy, an optimal scaling parameter is determined for the proposed method. In Faridani et al [42], a trust-based recommendation method is proposed based on aggregating the trusted neighbours of the target users to alleviate data sparsity problem. To this end, the MoleTrust algorithm is used to provide more similar users into the recommendation process.…”

Section: Related Workmentioning

confidence: 99%

An effective social recommendation method based on user reputation model and rating profile enhancement

Ahmadian

Afsharchi

Meghdadi

2018

Journal of Information Science

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Trust-aware recommender systems are advanced approaches which have been developed based on social information to provide relevant suggestions to users. These systems can alleviate cold start and data sparsity problems in recommendation methods through trust relations. However, the lack of sufficient trust information can reduce the efficiency of these methods. Moreover, diversity and novelty are important measures for providing more attractive suggestions to users. In this article, a reputation-based approach is proposed to improve trust-aware recommender systems by enhancing rating profiles of the users who have insufficient ratings and trust information. In particular, we use a user reliability measure to determine the effectiveness of the rating profiles and trust networks of users in predicting unseen items. Then, a novel user reputation model is introduced based on the combination of the rating profiles and trust networks. The main idea of the proposed method is to enhance the rating profiles of the users who have low user reliability measure by adding a number of virtual ratings. To this end, the proposed user reputation model is used to predict the virtual ratings. In addition, the diversity, novelty and reliability measures of items are considered in the proposed rating profile enhancement mechanism. Therefore, the proposed method can improve the recommender systems about the cold start and data sparsity problems and also the diversity, novelty and reliability measures. Experimental results based on three real-world datasets show that the proposed method achieves higher performance than other recommendation methods.

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Collaborative filtering-based recommender systems by effective trust

Cited by 16 publications

References 23 publications

An Efficient Recommender System Technique in Social Networks Based on Association Rule Based Mining

An Efficient Recommender System Technique in Social Networks Based on Association Rule Based Mining

The Role of Trust to Enhance the Recommendation System Based on Social Network

An effective social recommendation method based on user reputation model and rating profile enhancement

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