Quality Enhancement of Recommendation using Improved Triangle Ratings

摘要

Recommender Systems are a potent technology used in many social networking sites. Personalized recommender systems are an added method for improving the quality of recommendation and customer's requirements. There are many kinds of techniques available to get personalised recommendations such as Content based, Collaborative filtering and Hybrid filtering. In these mentioned techniques, the most popular CF technique is used to enhance the accuracy of RS with some shortcomings such as sparsity, scalability and cold start user problems. To enhance the quality of collaborative filtering using tagging, the proposed approach IUGT-Jaccard-ITR used may target the issue of cold start user or item problems in recommendation.