Reciprocal recommender system online dating nerd dating sites
The goal of online dating is to help the user weed out potential time wasters and other bad matches so that your experience in the offline dating is enjoyable and more likely to lead to you finding a partner. We understand that some users have different interests than others and value different things.
The use of individual formulas for love can help to measure if two people will like each other.
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.
A reciprocal score that measures the compatibility between a user and each potential dating candidate is computed, and the recommendation list is generated to include users with top scores.
In particular, males tend to be focused on their own interest and oblivious toward their attractiveness to potential dates, while females are more conscientious to their own attractiveness to the other side of the line.
Online dating sites have become popular platforms for people to look for potential romantic partners.
This article is the first to present a comprehensive view of this important recommender class.
We first identify the characteristics of reciprocal recommenders and compare them with traditional recommenders, which are widely used in e-commerce websites.
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.
We use this case study to illustrate the distinctive requirements of reciprocal recommenders and highlight important challenges, such as the need to avoid bad recommendations since they may make users to feel rejected.