scholarly journals New Graph Based Trust Similarity Measure

2015 ◽  
Vol 37 ◽  
pp. 339 ◽  
Author(s):  
Saeed Garmsiri ◽  
Ali Hamzeh
Keyword(s):  
Social Networks ◽  
Similarity Measure ◽  
Similarity Measures ◽  
Nearest Neighbors ◽  
Trust Network ◽  
K Nearest Neighbors ◽  
Trust Value ◽  
Empirical Results ◽  
As Graph

Trust network in social networks can be considered as graph which trustors and trustees are graph vertices and edges present trust between them with measured values. To evaluate trust between trustors and trustees there is some similarity measures to measure similarity between trustors together or trustees together and then by using evaluated values predict trust value between them. Similarity measure has important effect on final accuracy. In this paper we propose graph based similarity measure. Similarity between two users is computed by connection between them on graph then this computed similarity used with k- nearest neighbors method to evaluate(predict) trust between users. To the best of our knowledge this is the first work introduces graph based similarity measure, empirical results on two real datasets show accuracy of predicted trust using proposed similarity measure outperforms accuracy of method without it.

scholarly journals Privacy-Enhancing k-Nearest Neighbors Search over Mobile Social Networks

Sensors ◽  
2021 ◽  
Vol 21 (12) ◽  
pp. 3994
Author(s):  
Yuxi Li ◽  
Fucai Zhou ◽  
Yue Ge ◽  
Zifeng Xu
Keyword(s):  
Social Networks ◽  
Access Control ◽  
Location Privacy ◽  
Nearest Neighbors ◽  
Search Pattern ◽  
Broadcast Encryption ◽  
Mobile Social Networks ◽  
K Nearest Neighbors ◽  
Fine Grained ◽  
Server Architecture

Focusing on the diversified demands of location privacy in mobile social networks (MSNs), we propose a privacy-enhancing k-nearest neighbors search scheme over MSNs. First, we construct a dual-server architecture that incorporates location privacy and fine-grained access control. Under the above architecture, we design a lightweight location encryption algorithm to achieve a minimal cost to the user. We also propose a location re-encryption protocol and an encrypted location search protocol based on secure multi-party computation and homomorphic encryption mechanism, which achieve accurate and secure k-nearest friends retrieval. Moreover, to satisfy fine-grained access control requirements, we propose a dynamic friends management mechanism based on public-key broadcast encryption. It enables users to grant/revoke others’ search right without updating their friends’ keys, realizing constant-time authentication. Security analysis shows that the proposed scheme satisfies adaptive L-semantic security and revocation security under a random oracle model. In terms of performance, compared with the related works with single server architecture, the proposed scheme reduces the leakage of the location information, search pattern and the user–server communication cost. Our results show that a decentralized and end-to-end encrypted k-nearest neighbors search over MSNs is not only possible in theory, but also feasible in real-world MSNs collaboration deployment with resource-constrained mobile devices and highly iterative location update demands.

Building Recommendation Service with Social Networks and Semantic Databases

2014 ◽  
pp. 83-92
Author(s):  
Sašo Karakatič ◽  
Vili Podgorelec ◽  
Marjan Heričko
Keyword(s):  
Social Networks ◽  
Social Network ◽  
Semantic Information ◽  
Additional Data ◽  
Nearest Neighbors ◽  
K Nearest Neighbors ◽  
Web Based ◽  
Semantic Database

In this chapter, it is shown how useful user services can be created through the integration of social networks and semantic databases. The authors developed a recommendation service in a form of a Web-based application, where a user's interests are imported from social network Facebook and linked with additional data from open semantic database Freebase. Based on a custom implementation of k-nearest neighbors algorithm, the developed method is able to find recommendations based on users’ interests enriched with semantic information. The resulting list of found recommendations is then shown to the user in some basic categories like movies, music, games, books, and others.

A collaborative filtering similarity measure based on potential field

Kybernetes ◽  
2016 ◽  
Vol 45 (3) ◽  
pp. 434-445 ◽  
Author(s):  
Yajun Leng ◽  
Qing Lu ◽  
Changyong Liang
Keyword(s):  
Recommender Systems ◽  
Similarity Measure ◽  
Potential Field ◽  
Similarity Measures ◽  
Nearest Neighbors ◽  
Dense Matrix ◽  
Content Type ◽  
Systems Research ◽  
Practical Engineering ◽  
Potential Matrix

Purpose – Collaborative recommender systems play a crucial role in providing personalized services to online consumers. Most online shopping sites and many other applications now use the collaborative recommender systems. The measurement of the similarity plays a fundamental role in collaborative recommender systems. Some of the most well-known similarity measures are: Pearson’s correlation coefficient, cosine similarity and mean squared differences. However, due to data sparsity, accuracy of the above similarity measures decreases, which makes the formation of inaccurate neighborhood, thereby resulting in poor recommendations. The purpose of this paper is to propose a novel similarity measure based on potential field. Design/methodology/approach – The proposed approach constructs a dense matrix: user-user potential matrix, and uses this matrix to compute potential similarities between users. Then the potential similarities are modified based on users’ preliminary neighborhoods, and k users with the highest modified similarity values are selected as the active user’s nearest neighbors. Compared to the rating matrix, the potential matrix is much denser. Thus, the sparsity problem can be efficiently alleviated. The similarity modification scheme considers the number of common neighbors of two users, which can further improve the accuracy of similarity computation. Findings – Experimental results show that the proposed approach is superior to the traditional similarity measures. Originality/value – The research highlights of this paper are as follows: the authors construct a dense matrix: user-user potential matrix, and use this matrix to compute potential similarities between users; the potential similarities are modified based on users’ preliminary neighborhoods, and k users with the highest modified similarity values are selected as the active user’s nearest neighbors; and the proposed approach performs better than the traditional similarity measures. The manuscript will be of particular interests to the scientists interested in recommender systems research as well as to readers interested in solution of related complex practical engineering problems.

scholarly journals A Novel Similarity Measure of Link Prediction in Multi-Layer Social Networks Based on Reliable Paths

2021 ◽  
Author(s):  
Amin Rezaeipanah
Keyword(s):  
Social Networks ◽  
Similarity Measure ◽  
Network Structure ◽  
Online Social Networks ◽  
Link Prediction ◽  
Similarity Measures ◽  
Single Layer ◽  
Superior Performance ◽  
Local Similarity ◽  
Topological Features

Abstract Online social networks are an integral element of modern societies and significantly influence the formation and consolidation of social relationships. In fact, these networks are multi-layered so that there may be multiple links between a user’ on different social networks. In this paper, the link prediction problem for the same user in a two-layer social network is examined, where we consider Twitter and Foursquare networks. Here, information related to the two-layer communication is used to predict links in the Foursquare network. Link prediction aims to discover spurious links or predict the emergence of future links from the current network structure. There are many algorithms for link prediction in unweighted networks, however only a few have been developed for weighted networks. Based on the extraction of topological features from the network structure and the use of reliable paths between users, we developed a novel similarity measure for link prediction. Reliable paths have been proposed to develop unweight local similarity measures to weighted measures. Using these measures, both the existence of links and their weight can be predicted. Empirical analysis shows that the proposed similarity measure achieves superior performance to existing approaches and can more accurately predict future relationships. In addition, the proposed method has better results compared to single-layer networks. Experiments show that the proposed similarity measure has an advantage precision of 1.8% over the Katz and FriendLink measures.

scholarly journals A Weighted Similarity Measure for k-Nearest Neighbors Algorithm

2019 ◽  
Vol 15 (4) ◽  
pp. 393-400
Author(s):  
Bergen Karabulut ◽  
Güvenç Arslan ◽  
Halil Murat ÜNVER
Keyword(s):  
Similarity Measure ◽  
Nearest Neighbors ◽  
K Nearest Neighbors

Modeling Tag-Aware Recommendations Based on User Preferences

2015 ◽  
Vol 14 (05) ◽  
pp. 947-970 ◽  
Author(s):  
Jiajin Hunag ◽  
Xi Yuan ◽  
Ning Zhong ◽  
Yiyu Yao
Keyword(s):  
Recommender Systems ◽  
Similarity Measures ◽  
Nearest Neighbors ◽  
Adaptive Method ◽  
User Preferences ◽  
K Nearest Neighbors ◽  
Qualitative And Quantitative ◽  
Item Properties ◽  
The One

A recommender system aims at recommending items that users might be interested in. With an increasing popularity of social tagging systems, it becomes urgent to model recommendations on users, items, and tags in a unified way. In this paper, we propose a framework for studying recommender systems by modeling user preferences as a relation on (user, item, tag) triples. We discuss tag-aware recommender systems from two aspects. On the one hand, we compute associations between users and items related to tags by using an adaptive method and recommend tags to users or predict item properties for users. On the other hand, by taking the similarity-based recommendation as a case study, we discuss similarity measures from both qualitative and quantitative perspectives and k-nearest neighbors and reverse k-nearest neighbors for recommendations.

scholarly journals Item Genre-Based Users Similarity Measure for Recommender Systems

2021 ◽  
Vol 11 (13) ◽  
pp. 6108
Author(s):  
Jehan Al-Safi ◽  
Cihan Kaleli
Keyword(s):  
Collaborative Filtering ◽  
Similarity Measure ◽  
Evaluation Criteria ◽  
Similarity Measures ◽  
Nearest Neighbors ◽  
Active User ◽  
Recommendation Accuracy ◽  
User Ratings ◽  
The Relationship ◽  
Better Than

A technique employed by recommendation systems is collaborative filtering,,which predicts the item ratings and recommends the items that may be interesting to the user. Naturally, users have diverse opinions, and only trusting user ratings of products may produce inaccurate recommendations. Therefore, it is essential to offer a new similarity measure that enhances recommendation accuracy, even for customers who only leave a few ratings. Thus, this article proposes an algorithm for user similarity measures that exploit item genre information to make more accurate recommendations. This algorithm measures the relationship between users using item genre information, discovers the active user’s nearest neighbors in each genre, and finds the final nearest neighbors list who can share with them the same preference in a genre. Finally, it predicts the active-user rating of items using a definite prediction procedure. To measure the accuracy, we propose new evaluation criteria: the rating level and reliability among users, according to rating level. We implement the proposed method on real datasets. The empirical results clarify that the proposed algorithm produces a predicted rating accuracy, rating level, and reliability between users, which are better than many existing collaborative filtering algorithms.

MATHURA (MBI) - A NOVEL IMPUTATION MEASURE FOR IMPUTATION OF MISSING VALUES IN MEDICAL DATASETS

2019 ◽  
Vol 13 ◽  
Author(s):  
B. Mathura Bai ◽  
N. Mangathayaru ◽  
B. Padmaja Rani ◽  
Shadi Aljawarneh
Keyword(s):  
Similarity Measure ◽  
Medical Records ◽  
Missing Values ◽  
Similarity Measures ◽  
Common Problems ◽  
Experiment Analysis

: Missing attribute values in medical datasets are one of the most common problems faced when mining medical datasets. Estimation of missing values is a major challenging task in pre-processing of datasets. Any wrong estimate of missing attribute values can lead to inefficient and improper classification thus resulting in lower classifier accuracies. Similarity measures play a key role during the imputation process. The use of an appropriate and better similarity measure can help to achieve better imputation and improved classification accuracies. This paper proposes a novel imputation measure for finding similarity between missing and non-missing instances in medical datasets. Experiments are carried by applying both the proposed imputation technique and popular benchmark existing imputation techniques. Classification is carried using KNN, J48, SMO and RBFN classifiers. Experiment analysis proved that after imputation of medical records using proposed imputation technique, the resulting classification accuracies reported by the classifiers KNN, J48 and SMO have improved when compared to other existing benchmark imputation techniques.

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