Unifying user similarity and social trust to generate powerful recommendations for smart cities using collaborating filtering-based recommender systems

2019 ◽  
Vol 24 (15) ◽  
pp. 11071-11094 ◽  
Author(s):  
Mubbashir Ayub ◽  
Mustansar Ali Ghazanfar ◽  
Zahid Mehmood ◽  
Khaled H. Alyoubi ◽  
Ahmed S. Alfakeeh
Keyword(s):  
Recommender Systems ◽  
Social Trust ◽  
Smart Cities ◽  
User Similarity ◽  
Collaborating Filtering

Collaborative filtering-based recommendation of online social voting

2018 ◽  
Vol 7 (3) ◽  
pp. 1504 ◽  
Author(s):  
Dr Mohammed Ismail ◽  
Dr K. Bhanu Prakash ◽  
Dr M. Nagabhushana Rao
Keyword(s):  
Recommender Systems ◽  
Matrix Factorization ◽  
Recommendation System ◽  
Nearest Neighbor ◽  
Social Trust ◽  
Social Recommendation ◽  
Peer Reviewers ◽  
Hybrid Recommender System ◽  
Hybrid Recommender ◽  
Cold Start Problem

Social voting is becoming the new reason behind social recommendation these days. It helps in providing accurate recommendations with the help of factors like social trust etc. Here we propose Matrix factorization (MF) and nearest neighbor-based recommender systems accommodating the factors of user activities and also compared them with the peer reviewers, to provide a accurate recommendation. Through experiments we realized that the affiliation factors are very much needed for improving the accuracy of the recommender systems. This information helps us to overcome the cold start problem of the recommendation system and also y the analysis this information was much useful to cold users than to heavy users. In our experiments simple neighborhood model outperform the computerized matrix factorization models in the hot voting and non hot voting recommendation. We also proposed a hybrid recommender system producing a top-k recommendation inculcating different single approaches.  

Soft Sensing in Smart Cities: Handling 3Vs Using Recommender Systems, Machine Intelligence, and Data Analytics

2018 ◽  
Vol 56 (2) ◽  
pp. 78-86 ◽  
Author(s):  
Hadi Habibzadeh ◽  
Andrew Boggio-Dandry ◽  
Zhou Qin ◽  
Tolga Soyata ◽  
Burak Kantarci ◽  
...  
Keyword(s):  
Recommender Systems ◽  
Data Analytics ◽  
Smart Cities ◽  
Machine Intelligence ◽  
Soft Sensing

STRESS

2015 ◽  
Vol 7 (3) ◽  
pp. 40-58
Author(s):  
Naziha Abderrahim ◽  
Sidi Mohamed Benslimane
Keyword(s):  
Social Sciences ◽  
Web Services ◽  
Web Service ◽  
Recommender Systems ◽  
Social Trust ◽  
Social Computing ◽  
Design And Development ◽  
Innovative Solutions ◽  
Run Time

Recommender systems help users find relevant Web service based on peers' previous experiences dealing with Web services (WSs). However, with the proliferation of WSs, recommendation has become “questionable”. Social computing seems offering innovative solutions to improve the quality of recommendations. Social computing is at the crossroad of computer sciences and social sciences disciplines by looking into ways of improving application design and development using elements that people encounter daily such as collegiality, friendship and trust. In this paper, the authors propose a social trust-aware system for recommending WS based on social qualities of WSs that they exhibit towards peers at run-time, and trustworthiness of the users who provide feedback on their overall experience using WSs. A set of experiments to assess the fairness and accuracy of the proposed system are reported in the paper, showing promising results.

scholarly journals UARR: A Novel Similarity Measure for Collaborative Filtering Recommendation

2013 ◽  
Vol 13 (Special-Issue) ◽  
pp. 122-130
Author(s):  
Yue Huang ◽  
Xuedong Gao ◽  
Shujuan Gu
Keyword(s):  
Collaborative Filtering ◽  
Recommender Systems ◽  
Similarity Measure ◽  
Accurate Prediction ◽  
Similarity Measurement ◽  
Data Set ◽  
Rating Data ◽  
User Similarity ◽  
Level Difference

Abstract User similarity measurement plays a key role in collaborative filtering recommendation which is the most widely applied technique in recommender systems. Traditional user-based collaborative filtering recommendation methods focus on absolute rating difference of common rated items while neglecting the relative rating level difference to the same items. In order to overcome this drawback, we propose a novel user similarity measure which takes into account the degree of rating the level gap that users could accept. The results of collaborative filtering recommendation based on User Acceptable Rating Radius (UARR) on a real movie rating data set, the MovieLens data set, prove to generate more accurate prediction results compared to the traditional similarity methods.

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