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Benefitting from sophisticated payment environments of mobile platforms, e.g., Apple APP store, the In-APP purchases which sell equipments or v With the development of e-commerce, a growing number of people prefer to purchase medicine online for the sake of convenience.

In this paper, we study the reciprocal online dating recommendation system based on a large real-world dataset obtained from a major online dating site in China with a total number of 60 million registered users.Unlike in traditional user-item recommendation systems where the goal is typically to predict a user’s opinion toward passive items (e.g., books, movies), when making recommendation of potential dates to a user (referred to as service user) on an online dating site, it is important that not only the recommended users match the user’s dating interest, but also the recommended users are interested in the service user and thus likely to reciprocate when contacted.A successful online dating recommendation system should match users with mutual interest in each 1 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.

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