Improved Recommendations Based on Trust Relationships in Social Networks

Tian, Hao; Liang, Peifeng

doi:10.3390/fi9010009

Cited by 13 publications

(10 citation statements)

References 17 publications

(24 reference statements)

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“…(2) When describing the dynamics of the trust relationship, most of them treat every recommendation equally, and seldom consider or quantify the actual effect of every recommendation on the positive or negative change of the trust relationship. Therefore, in order to overcome these shortcomings, on the basis of our previous research [16,17], we are motivated to consider more influencing factors, redefine the trust relationship, and propose a web service recommendation method based on advanced trust relationships to further improve the performance of service recommendation.…”

Section: Related Workmentioning

confidence: 99%

“…rai and rbi denote the evaluation value on service si by user ua Obviously, there is a difference in the calculation of D between the two types of trust relationships. In our previous study [17], indirect trust degree was calculated by selecting the maximum trust degree path without considering the influence of other trust paths, which may lose part of the trust information. Therefore, we put forward an improved calculation method of trust degree in this paper, which can be described as Equation 2.…”

Section: User Preference Similaritymentioning

confidence: 99%

“…Experiments were conducted to evaluate the performance of the formalization of advanced trust relationships and the algorithm SRATR proposed in this paper, in which three similar methods are selected as comparison objects: IRATR [17], NCut [18], and SRMTC [19]. IRATR method is one of our previous research results and one of the foundations of this study.…”

Section: Experiments and Analysismentioning

confidence: 99%

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A Novel Approach for Web Service Recommendation Based on Advanced Trust Relationships

2019

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Service recommendation is one of the important means of service selection. Aiming at the problems of ignoring the influence of typical data sources such as service information and interaction logs on the similarity calculation of user preferences and insufficient consideration of dynamic trust relationship in traditional trust-based Web service recommendation methods, a novel approach for Web service recommendation based on advanced trust relationships is presented. After considering the influence of indirect trust paths, the improved calculation about indirect trust degree is proposed. By quantifying the popularity of service, the method of calculating user preference similarity is investigated. Furthermore, the dynamic adjustment mechanism of trust is designed by differentiating the effect of each service recommendation. Integrating these efforts, a service recommendation mechanism is introduced, in which a new service recommendation algorithm is described. Experimental results show that, compared with existing methods, the proposed approach not only has higher accuracy of service recommendation, but also can resist attacks from malicious users more effectively.

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Section: Related Workmentioning

confidence: 99%

Section: User Preference Similaritymentioning

confidence: 99%

Section: Experiments and Analysismentioning

confidence: 99%

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A Novel Approach for Web Service Recommendation Based on Advanced Trust Relationships

2019

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“…Moreover, there is a research on improving such recommendation systems using relationships in SNS (i.e. social networks) [5]. Also, there is a system which refers to the features of co-author networks for the formation of research groups [6].…”

Section: Matching Between Usersmentioning

confidence: 99%

Analysis of Speech Dialogue to Detect Active Conversation and Lapse in Conversation toward Development of Conversation Support Robot after Co-occurrence and Mutual Assistance Matching

Gomi

Aizawa

Sato-Shimokawara

et al. 2019

Journal of Signal Processing

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The formation of human relationships is important to form communities. As a supporting system to strengthen the bonding among community members, we firstly tried to connect users by co-occurrence and mutual assistance matching to form relationships. Then, we brought them together. At the time of this experiment, users can easily carry on animated conversations if a common topic is suggested to them. In this study, we utilized a questionnaire to acquire user's personal characteristics for co-occurrence and mutual assistance matching. Next, we conducted a conversation experiment via Skype to investigate activation and inactivation during user's conversations by using speech dialogue and the LF/HF ratio calculated from user's heart rates. During the conversation experiment, matched users made conversations for 10 min on each common topic based on their interests suggested. The result showed that users could easily have animated conversations when a common topic was suggested to them. In addition, we investigated acoustic features, the overlap of utterances, and the number of utterances, backchannel feedbacks, and laughter.

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“…However, issues have emerged when there were problems with the user data being utilized for formulating the recommendations, and consequently, the recommendation accuracy suffered. To overcome these problems, research has been focused on the user who creates the data for assessing the trust of the user data that is available on the OSNs . Therefore, recommendation schemes have been introduced that consider the trust of the user .…”

Section: Introductionmentioning

confidence: 99%

Personalized content recommendation scheme based on trust in online social networks

Bok

Geonsik

Lim

et al. 2019

Concurrency and Computation

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Summary As various social network services have developed, users are creating and sharing a large amount of content. Concurrently, there have been many studies on recommendation schemes for providing users with content that matches their preferences. In this paper, we propose a trust‐based personalized content recommendation scheme using collaborative filtering in online social network services. The user trust is calculated by analyzing social activities, content usages, and social relationships. In addition, the content trust is calculated by analyzing user expertise and reputations. Collaborative filtering is performed on users who are filtered through the user trust, and recommendation priorities are determined according to the content trust. The proposed scheme can improve the performance of collaborative filtering by eliminating untrustworthy users using user trust. It also improves the accuracy of recommendations since it provides recommendations based on content trust. Therefore, the proposed scheme can improve the performance of recommendation services using collaborative filtering in online social network services that share multimedia content. Performance evaluation is performed in terms of MAE and RMSE, which assesses errors in recommended results to demonstrate the superiority of the proposed scheme. Performance evaluations have shown that errors in the proposed scheme are reduced compared to the existing schemes, improving the accuracy of recommendations.

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Improved Recommendations Based on Trust Relationships in Social Networks

Cited by 13 publications

References 17 publications

A Novel Approach for Web Service Recommendation Based on Advanced Trust Relationships

A Novel Approach for Web Service Recommendation Based on Advanced Trust Relationships

Analysis of Speech Dialogue to Detect Active Conversation and Lapse in Conversation toward Development of Conversation Support Robot after Co-occurrence and Mutual Assistance Matching

Personalized content recommendation scheme based on trust in online social networks

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