TruGRC: Trust-Aware Group Recommendation with Virtual Coordinators

Wang, Ximeng; Liu, Yun; Lu, Jie; Xiong, Fei; Zhang, Guangquan

doi:10.1016/j.future.2018.11.030

Cited by 42 publications

(16 citation statements)

References 54 publications

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“…In addition, the coordinator-based approach predicts preferences by constructing networks and interacting with each group member. In [29], the coordinator integrates virtual users through various profile aggregation strategies to generate a trust-aware group. In [30], the coordinator assigns synthetic coordinates to accurately estimate the distance between the user and item.…”

Section: B Recommendation Systemmentioning

confidence: 99%

Tree-Based Real-Time Advertisement Recommendation System in Online Broadcasting

2020

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The viewing time of media content per week through TV is still dominant. Users are exposed to numerous advertisements, such as commercials, electronic home shopping, product placement (PPL), and T-Commerce while watching TV programs. Most of the advertisement systems provide a good overview of products. However, traditional advertising services do not consider user preferences, meaning it is difficult to expect anything more than mere exposure to them. We can adopt a recommendation system to predict the preference. However, existing recommendation systems find it difficult to satisfy the realtime requirements of online broadcasting because of the large overhead incurred in preference prediction processes. In this paper, we propose a real-time recommendation system to provide personalized advertisements. The proposed system generates tree models based on user historical data. To reduce the overhead of preference prediction, we introduce a sorted HashMap that enables fast tree searches. For sophisticated preference prediction, the proposed system normalizes the users' preferences by considering the characteristics of their tree model. Finally, we conduct experiments to evaluate the performance of the proposed tree-based recommendation system.

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Section: B Recommendation Systemmentioning

confidence: 99%

Tree-Based Real-Time Advertisement Recommendation System in Online Broadcasting

2020

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“…To simulate the uncertainty environment in the group, the interval trust function was constructed based on the common recognition and harmony degree between members [29]. Ximeng Wang [30] proposed a virtual GR algorithm, which integrates trust aggregation and file configuration at the same time and builds a user virtual coordinator to solve the preference conflict. Chen [31] analysed the similarity between other users under the same trusted user and minimized the difference between similar users by CosRA+T.…”

Section: Trust Metricmentioning

confidence: 99%

Group recommendation based on hybrid trust metric

2020

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Group recommendation is a special service type which has the ability to satisfy a group's common interest and find the preferred items for group users. Deep mining of trust relationship between group members can contribute to the improvement of accuracy during group recommendation. Most of the existing trust-based group recommendation methods pay little attention to the diversity of trust sources, resulting in poor recommendation accuracy. To address the problem above, this paper proposes a group recommendation method based on a hybrid trust metric (GR-HTM). Firstly, GR-HTM creates an attribute trust matrix and a social trust matrix based on user attributes and social relationships, respectively. Secondly, GR-HTM accomplishes a hybrid trust matrix based on the integration of these two matrices with the employment of the Tanimoto coefficient. Finally, GR-HTM calculates weights for each item in the hybrid trust matrix based on weighted-meanlist and proceeds to group recommendation with a given trust threshold. Simulation experiments demonstrate that the proposed GR-HTM has better performance for group recommendation in accuracy and effectiveness. ARTICLE HISTORY

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“…On the other side, in item-item TBRS, the reliance of items is measured by applying users' feedback on the items [56] or studying users' activity with these items [28,57,58].However, according to the methodology of trust integration, TBRS can be categorized as memory-based [30,38,59] and model-based [20,38,60,61] approaches. Further, the TBRS can be classified based on the trust definition as either explicit [19,38,52,62] or implicit [12,13,17,18] or hybrid trust-based recommender system [35,63,64]. Figure 3 shows the classification of TBRS in a row.…”

Section: Classification Of Trust-based Recommender Systemmentioning

confidence: 99%

A Comparative Analysis of Different Trust Metrics in User-User Trust-Based Recommender System

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2020

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Information overload is the biggest challenges nowadays for any website, especially the e-commerce website. However, this challenge arises for the fast growth of information on the web (WWW) with easy access to the internet. Collaborative filtering based recommender system is the most useful application to solve the information overload problem by filtering relevant information for the users according to their interests. But, the existing system faces some significant limitations like as data sparsity, low accuracy, cold-start and malicious attacks. To alleviate the mentioned issues, the relationship of trust incorporates in the system where it can be between the users or items, and such system is known as the trust-based recommender system (TBRS). From the user perspective, the motive of the TBRS is to utilize the reliability between the users to generate more accurate and trusted recommendations. However, the aim of the paper is to present a comparative analysis of different trust metrics in the context of the type of trust definition of TBRS. Also, the study accomplishes on twenty-four trust metrics in terms of the methodology, trust properties &amp; measurement, validation approaches, and the experimented dataset.

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TruGRC: Trust-Aware Group Recommendation with Virtual Coordinators

Cited by 42 publications

References 54 publications

Tree-Based Real-Time Advertisement Recommendation System in Online Broadcasting

Tree-Based Real-Time Advertisement Recommendation System in Online Broadcasting

Group recommendation based on hybrid trust metric

A Comparative Analysis of Different Trust Metrics in User-User Trust-Based Recommender System

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