Using collaborative filtering to weave an information tapestry
Communications of the ACM - Special issue on information filtering
GroupLens: an open architecture for collaborative filtering of netnews
CSCW '94 Proceedings of the 1994 ACM conference on Computer supported cooperative work
Social information filtering: algorithms for automating “word of mouth”
CHI '95 Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Communications of the ACM
Analysis of recommendation algorithms for e-commerce
Proceedings of the 2nd ACM conference on Electronic commerce
On the recommending of citations for research papers
CSCW '02 Proceedings of the 2002 ACM conference on Computer supported cooperative work
Evaluating collaborative filtering recommender systems
ACM Transactions on Information Systems (TOIS)
A collaborative filtering algorithm and evaluation metric that accurately model the user experience
Proceedings of the 27th annual international ACM SIGIR conference on Research and development in information retrieval
Improving recommendation lists through topic diversification
WWW '05 Proceedings of the 14th international conference on World Wide Web
Being accurate is not enough: how accuracy metrics have hurt recommender systems
CHI '06 Extended Abstracts on Human Factors in Computing Systems
Collaborative Filtering Process in a Whole New Light
IDEAS '06 Proceedings of the 10th International Database Engineering and Applications Symposium
Advanced topics in recommendation
Proceedings of the 12th international conference on Intelligent user interfaces
A new similarity measure for collaborative filtering to alleviate the new user cold-starting problem
Information Sciences: an International Journal
A Comparison of Collaborative-Filtering Recommendation Algorithms for E-commerce
IEEE Intelligent Systems
Empirical analysis of predictive algorithms for collaborative filtering
UAI'98 Proceedings of the Fourteenth conference on Uncertainty in artificial intelligence
User-based Collaborative Filtering: Sparsity and Performance
Proceedings of the 2010 conference on STAIRS 2010: Proceedings of the Fifth Starting AI Researchers' Symposium
Recent developments in information retrieval
ECIR'2010 Proceedings of the 32nd European conference on Advances in Information Retrieval
A mobile 3D-GIS hybrid recommender system for tourism
Information Sciences: an International Journal
| Hi-index | 0.00 |
This paper investigates the significance of numeric user ratings in recommender systems by considering their inclusion / exclusion in both the generation and evaluation of recommendations. When standard evaluation metrics are used, experimental results show that inclusion of numeric rating values in the recommendation process does not enhance the results. However, evaluating the accuracy of a recommender algorithm requires identifying the aim of the system. Evaluation metrics such as precision and recall evaluate how well a system performs at recommending items that have been previously rated by the user. By contrast, a new metric, known as Approval Rate, is intended to evaluate how well a system performs at recommending items that would be rated highly by the user. Experimental results demonstrate that these two aims are not synonymous and that for an algorithm to attempt both obscures the investigation. The results also show that appropriate use of numeric rating valuesin the process of calculating user similarity can enhance the performance when Approval Rate is used.