Users of online dating sites are facing information overload that requires them to manually construct queries and browse huge amount of matching user profiles. This becomes even more problematic for multimedia profiles. Although matchmaking is frequently cited as a typical application for recommender systems, there is a surprising lack of work published in this area. In this paper we describe a recommender system we implemented and perform a quantitative comparison of two collaborative filtering (CF) and two global algorithms. Results show that collaborative filtering recommenders significantly outperform global algorithms that are currently used by dating sites. A blind experiment with real users also confirmed that users prefer CF based recommendations to global popularity recommendations. Recommender systems show a great potential for online dating where they could improve the value of the service to users and improve monetization of the service.
Recommender systems @cite_8 are a popular and successful way of tackling the information overload. Recommender systems have been popularized by applications such as Amazon @cite_17 or Netflix recommenders http: amazon.com , http: netflix.com . The most widely used recommender systems are based on collaborative filtering algorithms. One of the first collaborative filtering systems was Tapestry @cite_1 . Other notable CF systems include jester @cite_18 , Ringo @cite_14 , Movielens and Launch.com.
@cite_18 @cite_14 @cite_8 @cite_1 @cite_17
2117354486 1608058569 1966553486 2159094788
Eigentaste is a collaborative filtering algorithm that uses i>universal queries to elicit real-valued user ratings on a common set of items and applies principal component analysis (PCA) to the resulting dense subset of the ratings matrix. PCA facilitates dimensionality reduction for offline clustering of users and rapid computation of recommendations. For a database of i>n users, standard nearest-neighbor techniques require i>O(i>n) processing time to compute recommendations, whereas Eigentaste requires i>O(1) (constant) time. We compare Eigentaste to alternative algorithms using data from i>Jester, an online joke recommending system. Jester has collected approximately 2,500,000 ratings from 57,000 users. We use the Normalized Mean Absolute Error (NMAE) measure to compare performance of different algorithms. In the Appendix we use Uniform and Normal distribution models to derive analytic estimates of NMAE when predictions are random. On the Jester dataset, Eigentaste computes recommendations two orders of magnitude faster with no loss of accuracy. Jester is online at: http: eigentaste.berkeley.edu Thesis (M. Eng)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 1994. The Tapestry experimental mail system developed at the Xerox Palo Alto Research Center is predicated on the belief that information filtering can be more effective when humans are involved in the filtering process. Tapestry was designed to support both content-based filtering and collaborative filtering, which entails people collaborating to help each other perform filtering by recording their reactions to documents they read. The reactions are called annotations; they can be accessed by other people’s filters. Tapestry is intended to handle any incoming stream of electronic documents and serves both as a mail filter and repository; its components are the indexer, document store, annotation store, filterer, little box, remailer, appraiser and reader browser. Tapestry’s client server architecture, its various components, and the Tapestry query language are described. Recommendation algorithms are best known for their use on e-commerce Web sites, where they use input about a customer's interests to generate a list of recommended items. Many applications use only the items that customers purchase and explicitly rate to represent their interests, but they can also use other attributes, including items viewed, demographic data, subject interests, and favorite artists. At Amazon.com, we use recommendation algorithms to personalize the online store for each customer. The store radically changes based on customer interests, showing programming titles to a software engineer and baby toys to a new mother. There are three common approaches to solving the recommendation problem: traditional collaborative filtering, cluster models, and search-based methods. Here, we compare these methods with our algorithm, which we call item-to-item collaborative filtering. Unlike traditional collaborative filtering, our algorithm's online computation scales independently of the number of customers and number of items in the product catalog. Our algorithm produces recommendations in real-time, scales to massive data sets, and generates high quality recommendations.