Please use this identifier to cite or link to this item: https://idr.nitk.ac.in/jspui/handle/123456789/7229
Title: Alleviating data sparsity and cold start in recommender systems using social behaviour
Authors: Reshma, R.
Ambikesh, G.
Santhi Thilagam, P.
Issue Date: 2016
Citation: 2016 International Conference on Recent Trends in Information Technology, ICRTIT 2016, 2016, Vol., , pp.-
Abstract: Recommender systems are used to find preferences of people or to predict the ratings with the help of information available from other users. The most widely used collaborative filtering recommender system by the e-commerce sites suffers from both the sparsity and cold-start problem due to insufficient data. Most of the existing systems consider only the ratings of the similar users and they do not give any preferences to the social behavior of users which shall aid the recommendations made to the user to a great extent. In this paper, instead of finding similarity from rating information, we propose a new approach which predicts the ratings of items by considering directed and transitive trust with timestamps and profile similarity from the social network along with the user-rated information. In cases where the trust and the rating details of users from the system is absent, we still make use of the social data of the users like the products liked by the user, user's social profile-education status, location etc.To make recommendation. Experimental analysis proves that our approach can improve the user recommendations at the extreme levels of sparsity in user-rating data. We also show that our approach works considerably well for cold-start users under the circumstances where collaborative filtering approach fails. � 2016 IEEE.
URI: https://idr.nitk.ac.in/jspui/handle/123456789/7229
Appears in Collections:2. Conference Papers

Files in This Item:
There are no files associated with this item.


Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.