Recon a reciprocal recommended for online dating red hot dating phone line
MOOC forums and discussion boards offer learners a medium to communicate with each other and maximize their learning outcomes.
However, oftentimes learners are hesitant to approach each other for different reasons (being shy, don't know the right match, etc.).
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.
ABSTRACT: Massive open online courses (MOOC) describe platforms where users with completely different backgrounds subscribe to various courses on offer.
Even when an equation is simple enough to be accurate for a large number of people (just as the one that we present), there will always be plenty of individuals who will prove it wrong.
Different from traditional user-item recommendations where the goal is to match items (e.g., books, videos, etc) with a user s interests, a recommendation system for online dating aims to match people who are mutually interested in and likely to communicate with each other.
We introduce similarity measures that capture the unique features and characteristics of the online dating network, for example, the interest similarity between two users if they send messages to same users, and attractiveness similarity if they receive messages from same users.
This paper proposes a simple yet novel regularization term, the Mutual-Attraction Indicator, to model the mutual preferences of both parties.
Given such indicator, we design a transfer-learning based CF model for reciprocal recommender.
People are different, so are their understanding of love and relationships.