A Survey on Alleviating Cold – Start Problem in Lars* Using Hybrid System - Mohan, Mili; Robert, S (International Journal of Computer Sciences and Engineering, 2015)
Number of people who uses internet and websites for various purposes is increasing at an astonishing rate. More and more people rely on online sites for purchasing rented movies, songs, apparels, books etc. The competition between numbers of sites forced the web site owners to provide personalized services to their customers. So the recommender systems came into existence. LARS* is a location-aware recommender system that uses location based ratings to produce recommendations. LARS* supports a taxonomy of three novel classes of location-based ratings, namely, spatial ratings for non-spatial items, non-spatial ratings for spatial items, and spatial ratings for spatial items. The item based collaborative filtering used for generating recommendations in LARS* suffers from cold start problem. In cold start problem, the recommenders cannot draw inferences for users who are new to the system (new user problem) and for items which does not have sufficient ratings (new item problem). New user cold start problem can be resolved by utilizing the demographic data explicitly given by a user. Also the content based filtering does not suffer from new item cold start problem. From the survey carried out, a hybrid recommender system which exploits the demographic and content based filtering features can be used for alleviating cold start problem.