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.
Stochastic matching and collaborative filtering to recommend people to peoplemore .
These two assumptions are quite reasonable and are the driving motivation for technological approaches to dating. We compute a different formula for each individual online dating user.
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.
The results show that our recommendation algorithms significantly outperform previously proposed approaches, and the collaborative filtering-based algorithms achieve much better performance than content-based algorithms in both precision and recall.
Our results also reveal interesting behavioral difference between male and female users when it comes to looking for potential dates.
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.
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.