Reciprocal recommender system for online dating reboundn dating site

The results show that our recommendation algorithms significantly outperform previously proposed approaches, and the collaborative filtering-based algorithms achieve much better performance than contentbased 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.

Incorrect privacy policy settings can easily lead to leaks in private and personal information.

At the same time, being too restrictive would re With the fast development of online and mobile technologies, individualized or personalized learning is becoming increasingly important. (2016) DOI 10.1007/s13278-016-0340-2 ORIGINAL ARTICLE Design of reciprocal recommendation systems for online dating Peng Xia1 • Shuangfei Zhai2 • Benyuan Liu1 • Yizhou Sun3 • Cindy Chen1 Received: 29 November 2015 / Revised: 17 April 2016 / Accepted:  Springer-Verlag Wien 2016 Abstract Online dating sites have become popular platforms for people to look for potential romantic partners.

However, planar components can also be considered; circular tiles, squares, triangles and more articulated or irre The Online Dating Romance Scam is a relatively new form of online fraud.

This article draws from three qualitative studies: an analysis of posts from a public online support group, in-depth interviews with victims of this crime and an interview with With the rapid development of smart mobile devices, phone games become an important way of entertainments.

This paper presents a systematic review of the literature that analyzes the use of machine learning algorithms in recommender systems and identifies new research opportunities.

The goals of this study are to (i) identify trends in the use or research of machine learning algorithms in recommender systems; (ii) identify open questions in the use or research of machine learning algorithms; and (iii) assist new researchers to position new research activity in this domain appropriately.

Many online dating sites provide suggestions on compatible partners based on their proprietary matching algorithms.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.The performance of our proposed recommendation system is evaluated on a real-world dataset from a major online dating site in China.This paper provides a description of the crowdfunding sector, considering investment-based crowdfunding platforms as well as platforms in which funders do not obtain monetary payments.It lays out key features of this quickly developing sector and explores the economic forces at play that can explain the design of these platforms.

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