Reciprocal recommender system for online dating Online chat dating sucht
We report analyses of the impact of dierent elements of the approach.One contribution of this work is the denition of the properties of, and promising approaches, for a new class of recommender, the reciprocal recommender.However, making that trade-off decision is something that warrants future research, as it is not clear how different criteria affect user experience and likelihood of finding a partner in a live online dating context.ABSTRACT One important class of recommender system involves people as both the subject and object of the recommendation. Some examples are: employment web sites, which help a job seeker and employer and the right employer nd each other; dating web sites; mentor-mentee matching systems.This analysis provides the foundation for our Content-Collaborative Reciprocal (CCR) recommender approach.The content-based part uses selected user profile features and similarity measure to generate a set of similar users.
It uses a large dataset from a major online dating website.
Additionally, factors influencing the design of online dating recommenders are described, and support for these characteristics are derived from our historical data set and previous research on other data sets.
The empirical comparison of the five methods on different recommendation quality criteria shows that no method is overwhelmingly better than the others and that a trade-off need be taken when choosing one for a live system.
To help them in their endeavor and to cope with information overload, recommender systems can be utilized.
This thesis introduces reciprocal recommender systems that are aimed towards the domain of online dating.