Social information filtering: algorithms for automating “word of mouth”
CHI '95 Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Fab: content-based, collaborative recommendation
Communications of the ACM
Item-based collaborative filtering recommendation algorithms
Proceedings of the 10th international conference on World Wide Web
Evaluation of Item-Based Top-N Recommendation Algorithms
Proceedings of the tenth international conference on Information and knowledge management
Amazon.com Recommendations: Item-to-Item Collaborative Filtering
IEEE Internet Computing
New Recommendation Techniques for Multicriteria Rating Systems
IEEE Intelligent Systems
Assessment of conversation co-mentions as a resource for software module recommendation
Proceedings of the third ACM conference on Recommender systems
Content-based recommendation systems
The adaptive web
Learning to make social recommendations: a model-based approach
ADMA'11 Proceedings of the 7th international conference on Advanced Data Mining and Applications - Volume Part II
Reciprocal and heterogeneous link prediction in social networks
PAKDD'12 Proceedings of the 16th Pacific-Asia conference on Advances in Knowledge Discovery and Data Mining - Volume Part II
An Adaptive Match-Making System reflecting the explicit and implicit preferences of users
Expert Systems with Applications: An International Journal
PRICAI'12 Proceedings of the 12th Pacific Rim international conference on Trends in Artificial Intelligence
People-to-People recommendation using multiple compatible subgroups
AI'12 Proceedings of the 25th Australasian joint conference on Advances in Artificial Intelligence
A people-to-people content-based reciprocal recommender using hidden markov models
Proceedings of the 7th ACM conference on Recommender systems
Hi-index | 0.00 |
We consider the problem of developing a recommender system for suggesting suitable matches in an online dating web site. The main problem to be solved is that matches must be highly personalized. Moreover, in contrast to typical product recommender systems, it is unhelpful to recommend popular items: matches must be extremely specific to the tastes and interests of the user, but it is difficult to generate such matches because of the two way nature of the interactions (user initiated contacts may be rejected by the recipient). In this paper, we show that collaborative filtering based on interactions between users is a viable approach in this domain. We propose a number of new methods and metrics to measure and predict potential improvement in user interaction success, which may lead to increased user satisfaction with the dating site. We use these metrics to rigorously evaluate the proposed methods on historical data collected from a commercial online dating web site. The evaluation showed that, had users been able to follow the top 20 recommendations of our best method, their success rate would have improved by a factor of around 2.3.