GroupLens: an open architecture for collaborative filtering of netnews
CSCW '94 Proceedings of the 1994 ACM conference on Computer supported cooperative work
Automatic personalization based on Web usage mining
Communications of the ACM
Personalizing web sites for mobile users
Proceedings of the 10th international conference on World Wide Web
Methods and metrics for cold-start recommendations
SIGIR '02 Proceedings of the 25th annual international ACM SIGIR conference on Research and development in information retrieval
Amazon.com Recommendations: Item-to-Item Collaborative Filtering
IEEE Internet Computing
Content-boosted collaborative filtering for improved recommendations
Eighteenth national conference on Artificial intelligence
Ontological user profiling in recommender systems
ACM Transactions on Information Systems (TOIS)
Recency-based collaborative filtering
ADC '06 Proceedings of the 17th Australasian Database Conference - Volume 49
ITWP'03 Proceedings of the 2003 international conference on Intelligent Techniques for Web Personalization
Personalizing the settings for Cf-based recommender systems
Proceedings of the fourth ACM conference on Recommender systems
Imputing missing values in nuclear safeguards evaluation by a 2-tuple computational model
ICAISC'10 Proceedings of the 10th international conference on Artificial intelligence and soft computing: Part I
Random walk based entity ranking on graph for multidimensional recommendation
Proceedings of the fifth ACM conference on Recommender systems
Expert Systems with Applications: An International Journal
GRASP and path relinking for the equitable dispersion problem
Computers and Operations Research
| Hi-index | 0.00 |
Collaborative Filtering(CF) is a widely accepted method of creating recommender systems. CF is based on the similarities among users or items. Measures of similarity including the Pearson Correlation Coefficient and the Cosine Similarity work quite well for explicit ratings, but do not capture real similarity from the ratings derived from implicit feedback. This paper identifies some problems that existing similarity measures have with implicit ratings by analyzing the characteristics of implicit feedback, and proposes a new similarity measure called Inner Product that is more appropriate for implicit ratings. We conducted experiments on user-based collaborative filtering using the proposed similarity measure for two e-commerce environments. Empirical results show that our similarity measure better captures similarities for implicit ratings and leads to more accurate recommendations. Our inner product-based similarity measure could be useful for CF-based recommender systems using implicit ratings in which negative ratings are difficult to be incorporated.