IEEE Transactions on Knowledge and Data Engineering
Collaborative filtering with temporal dynamics
Communications of the ACM
RECON: a reciprocal recommender for online dating
Proceedings of the fourth ACM conference on Recommender systems
People recommendation based on aggregated bidirectional intentions in social network site
PKAW'10 Proceedings of the 11th international conference on Knowledge management and acquisition for smart systems and services
Interaction-based collaborative filtering methods for recommendation in online dating
WISE'10 Proceedings of the 11th international conference on Web information systems engineering
People-to-People recommendation using multiple compatible subgroups
AI'12 Proceedings of the 25th Australasian joint conference on Advances in Artificial Intelligence
A hidden Markov model for collaborative filtering
MIS Quarterly
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Users of online social networks such as dating websites often need help to find successful matches. People-to-people recommender systems can be used in social networks to help users find better matches, which requires solving the problem of reciprocal recommendation. However, most existing reciprocal recommenders use either profile similarity or interaction similarity to recommend new matches, without considering temporal features. In this paper we introduce a method for temporal reciprocal recommender systems using Hidden Markov Models to generate recommendations. Instead of summarising the whole historical data in one past state, we propose a model that formalises historical data on interactions as a series of successive states changing over time and then tries to find the recommended next state. We have implemented this new approach and the results of testing on industrial-scale data from a real dating website show a noticeable improvement over the previous best-performing recommenders.