CCR - A Content-Collaborative Reciprocal Recommender for Online Dating
نویسندگان
چکیده
We present a new recommender system for online dating. Using a large dataset from a major online dating website, we first show that similar people, as defined by a set of personal attributes, like and dislike similar people and are liked and disliked by similar people. 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. The collaborative filtering part uses the interactions of the similar users, including the people they like/dislike and are liked/disliked by, to produce reciprocal recommendations. CCR addresses the cold start problem of new users joining the site by being able to provide recommendations immediately, based on their profiles. Evaluation results show that the success rate of the recommendations is 69.26% compared with a baseline of 35.19% for the top 10 ranked recommendations.
منابع مشابه
The nature of reciprocal recommenders with a case study in online dating
People-to-people recommenders constitute an important class of recommender systems. Examples include online dating, where people have the common goal of finding a partner, and employment websites where one group of users needs to find a job (employer) and another group needs to find an employee. People-to-people recommenders differ from the traditional items-to-people recommenders as they must ...
متن کاملA Scalable People-to-People Hybrid Reciprocal Recommender Using Hidden Markov Models
Most existing reciprocal recommender systems use either profile similarity or interaction similarity to recommend new matches, assuming that user preferences are static and ignoring temporal aspects of user behaviour. This paper takes a different approach, and addresses the issue of representing user preferences as dynamic. We introduce a new representation for changes in user preferences and u...
متن کاملRecommendation in Reciprocal and Bipartite Social Networks-A Case Study of Online Dating
Many social networks in our daily life are bipartite networks that are built on reciprocity. How can we recommend users/friends to a user, so that the user is interested in and attractive to recommended users? In this research, we propose a new collaborative filtering model to improve user recommendations in reciprocal and bipartite social networks. The model considers a user’s “taste” in picki...
متن کاملیک سامانه توصیهگر ترکیبی با استفاده از اعتماد و خوشهبندی دوجهته بهمنظور افزایش کارایی پالایشگروهی
In the present era, the amount of information grows exponentially. So, finding the required information among the mass of information has become a major challenge. The success of e-commerce systems and online business transactions depend greatly on the effective design of products recommender mechanism. Providing high quality recommendations is important for e-commerce systems to assist users i...
متن کاملCollaborative Filtering for people-to-people recommendation in online dating: Data analysis and user trial
A common perception is that online dating systems “match” people on the basis of profiles containing demographic and psychographic information and/or user interests. In contrast, product recommender systems are typically based on Collaborative Filtering, suggesting purchases not based on “content” but on the purchases of “similar” users. In this paper, we study Collaborative Filtering for peopl...
متن کامل