Identifying Core Users Based on Trust Relationships and Interest Similarity in Recommender System

Cao, Gaofeng; Kuang, Li

doi:10.1109/icws.2016.44

Cited by 21 publications

(9 citation statements)

References 23 publications

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“…More real datasets would be considered, which would have improved the accuracy of the proposed system. Cao Gaofeng, and Li Kuang [16] conducted experiments and the results of the experiments demonstrated the usefulness of extraction of core users and proved that ~20% core users permit recommender systems to attain >90% accuracy of top-N recommendation. However, the authors were unable to determine more approaches to define core users from different aspects (e.g., fusing similar relationships and trust relationships).…”

Section: Review Of Literaturementioning

confidence: 94%

Analysing the Structure, Dynamics and Contents of Social Networks

Lokhande¹

2019

IJRASET

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Social media permits its users to have numerous accounts in diverse social networking podiums. Popular social networking sites have billions of users that possess similar interests. The aim of this paper is to determine the identity of users and map those identities with those having similar interests. The developed system can be utilized for business intelligence that would help in creating a database of all users that possess specific interests. In this study, a system is developed wherein a user's core interest is recognized in well-known social networking sites (i.e., Twitter and Plurk) by means of latent Dirichlet allocation (LDA) algorithm and Kullback-Leibler divergence method. In addition, in order to identify users across the abovementioned social networking sites, LDA will cogitate social network structure and article content of users. LDA initially establishes a user's core interest. Next, it calculates similarity between target users. The results in this study prove to be quite promising, which reveal that LDA is an effective algorithm to map users across various platforms and efficient when compared with other methods.

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Section: Review Of Literaturementioning

confidence: 94%

Analysing the Structure, Dynamics and Contents of Social Networks

Lokhande¹

2019

IJRASET

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“…(1) Random: this approach randomly selects a user member from the social network as the prospective friends or neighbors of user u # . (2) Core-user [19]: this approach first searches for the core users (i.e., key users) with maximal social influences from user candidates and then finds out the friends of user u # based on the core users. (3) Exact-match: this approach exactly compares the feedback records left by user members and u # ; if their feedback records are exactly the same, then they can be regarded as similar friends or neighbors.…”

Section: Experimental Settingsmentioning

confidence: 99%

Privacy-aware friend finding in social network based on thumbs-up data

Sun

Kou

Huang

2019

J Wireless Com Network

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Social network has provided a promising way for massive users to share their ideas and communicate with each other. A key issue in social network is to find out the prospective friends of users so as to extend the users' social cycles. Fortunately, users' thumbs-up data on web news or blogs have become an important evaluation basis in friend finding. Typically, through analyzing the thumbs-up data from different users, we can find out the friends or neighbors of a user. However, the thumbs-up data are often sensitive to users as they can disclose the private information of users, which violate the civil privacy-protection laws enacted by governments. In view of this challenge, we introduce the Simhash technique in information retrieval domain into social network and further bring forth a privacy-aware prospective friend-finding solution in social network based on the sensitive thumbs-up data. At last, we conduct a range of experiments based on well-known Movielens dataset. Experimental data demonstrate the advantages of our solution.

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“…We compare our Ser Rec S BT +CF approach with other three ones, i.e., WSRec [9], Rec S BT +CF [13] and coreusers-NBI [11]. Concretely, in WSRec, r * (user target ) (i.e., user target 's average rating over all his/her invoked services) and r * (ws j ) (i.e., ws j 's average rating from all the users who invoked ws j ) are considered.…”

Section: Experiments Dataset and Deploymentmentioning

confidence: 99%

Data-Sparsity Tolerant Web Service Recommendation Approach Based on Improved Collaborative Filtering

Zhou

et al. 2017

IEICE Trans. Inf. & Syst.

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SUMMARYWith the ever-increasing number of web services registered in service communities, many users are apt to find their interested web services through various recommendation techniques, e.g., Collaborative Filtering (i.e., CF)-based recommendation. Generally, CF-based recommendation approaches can work well, when a target user has similar friends or the target services (i.e., services preferred by the target user) have similar services. However, when the available user-service rating data is very sparse, it is possible that a target user has no similar friends and the target services have no similar services; in this situation, traditional CF-based recommendation approaches fail to generate a satisfying recommendation result. In view of this challenge, we combine Social Balance Theory (abbreviated as SBT; e.g., "enemy's enemy is a friend" rule) and CF to put forward a novel data-sparsity tolerant recommendation approach Ser Rec S BT +CF . During the recommendation process, a pruning strategy is adopted to decrease the searching space and improve the recommendation efficiency. Finally, through a set of experiments deployed on a real web service quality dataset WS-DREAM, we validate the feasibility of our proposal in terms of recommendation accuracy, recall and efficiency. The experiment results show that our proposed Ser Rec S BT +CF approach outperforms other up-to-date approaches.

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Identifying Core Users Based on Trust Relationships and Interest Similarity in Recommender System

Cited by 21 publications

References 23 publications

Analysing the Structure, Dynamics and Contents of Social Networks

Analysing the Structure, Dynamics and Contents of Social Networks

Privacy-aware friend finding in social network based on thumbs-up data

Data-Sparsity Tolerant Web Service Recommendation Approach Based on Improved Collaborative Filtering

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