Heterogeneous trust-aware recommender systems in social network

Wang, Na; Chen, Zhaonan; Li, Xia

doi:10.1109/icbda.2017.8078741

Cited by 3 publications

(3 citation statements)

References 12 publications

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“…The last one adds some of the CB characteristics inside of CF and then CF generates the recommendation [29]. In [30] the authors proposed a hybrid novel approach based on CF and context-aware using the trust relationship to capture multifaceted and asymmetry trust relationships known as haTrust. This work improves the performance rating prediction and more robust to the cold-start problem.…”

Section: Fig 1 Traditional Recommendation System Approachesmentioning

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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Section: Fig 1 Traditional Recommendation System Approachesmentioning

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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“…The social regularization constrains the user's latent factor to be similar his/her rating average or his/her trustees rating average. Wang et al [32] assume that the topics or categories of a user determines whether she/he trusts someone else or not. Based on this assumption, their method incorporates the multi-faceted trust relationships between users into rating prediction.…”

Section: Related Workmentioning

confidence: 99%

Data imputation using a trust network for recommendation via matrix factorization

Hwang

Kim

et al. 2018

COMSIS J

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Existing recommendation methods suffer from the data sparsity problem which means that most of users have rated only a very small number of items, often resulting in low recommendation accuracy. In addition, for cold start users evaluating only few items, rating predictions with the methods also produce low accuracy. To address these problems, we propose a novel data imputation method that effectively substitutes missing ratings with probable values (i.e., imputed values). Our method successfully improves accuracy of recommendation methods from the following three aspects: (1) exploiting a trust network, (2) imputing only a part of missing ratings, and (3) applying them to any recommendation methods. Our method employs a bidirectional connection structure within a distance level for finding reliable users in exploiting a trust network as useful information. In addition, our method imputes only some missing ratings, called fillable ratings, whose imputed values are expected to be accurate with a sufficient level of confidence. Moreover, our imputation method is independent of, thus applicable to, any recommendation methods that may include application-specific ones and the most accurate one in each domain. We conduct experiments on three real-life datasets which arise from Epinions and Ciao. Our experimental results demonstrate that our method has recommendation accuracy better than existing recommendation methods equipped with imputation methods or trust networks, especially for cold start users.

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“…Jian [24] proposed a media recommendation solution in heterogeneous social network called GCCR to tackle false information by a user-centric strategy. Na [25] built a trust-aware recommender system with enhanced accuracy which reliably estimates users multi-faceted and asymmetry trust strengths.…”

Section: Related Workmentioning

confidence: 99%

UISTD: A Trust-Aware Model for Diverse Item Personalization in Social Sensing with Lower Privacy Intrusion

Zhang

2018

Sensors

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Privacy intrusion has become a major bottleneck for current trust-aware social sensing, since online social media allows anybody to largely disclose their personal information due to the proliferation of the Internet of Things (IoT). State-of-the-art social sensing still suffers from severe privacy threats since it collects users’ personal data and disclosure behaviors, which could raise user privacy concerns due to data integration for personalization. In this paper, we propose a trust-aware model, called the User and Item Similarity Model with Trust in Diverse Kinds (UISTD), to enhance the personalization of social sensing while reducing users’ privacy concerns. UISTD utilizes user-to-user similarities and item-to-item similarities to generate multiple kinds of personalized items with common tags. UISTD also applies a modified k-means clustering algorithm to select the core users among trust relationships, and the core users’ preferences and disclosure behaviors will be regarded as the predicted disclosure pattern. The experimental results on three real-world data sets demonstrate that target users are more likely to: (1) follow the core users’ interests on diverse kinds of items and disclosure behaviors, thereby outperforming the compared methods; and (2) disclose more information with lower intrusion awareness and privacy concern.

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Heterogeneous trust-aware recommender systems in social network

Cited by 3 publications

References 12 publications

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

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

Data imputation using a trust network for recommendation via matrix factorization

UISTD: A Trust-Aware Model for Diverse Item Personalization in Social Sensing with Lower Privacy Intrusion

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