We consider the problem of similarity search within a set of top-k lists under the Kendall's Tau distance function. This distance describes how related two rankings are in terms of concordantly and discordantly ordered items. As top-k lists are usually very short compared to the global domain of possible items to be ranked, creating an inverted index to look up overlapping lists is possible but does not capture tight enough the similarity measure. In this work, we investigate locality sensitive hashing schemes for the Kendall's Tau distance and evaluate the proposed methods using two real-world datasets.
@cite_12 consider matchmaking between users in a dating portal. The attributes considered are scalar (e.g., age, weight, and height) or categorical (e.g., married, smoking, and education) and focus is put on feature selection and learning for effective match making. Work on rank aggregation @cite_9 @cite_10 aims at synthesizing a representative ranking that minimizes the distances to the given rankings, for a given input set of rankings.
@cite_9 @cite_10 @cite_12
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Online dating sites have become popular platforms for people to look for potential romantic partners. Many online dating sites provide recommendations on compatible partners based on their proprietary matching algorithms. It is important that not only the recommended dates match the user’s preference or criteria, but also the recommended users are interested in the user and likely to reciprocate when contacted. The goal of this paper is to predict whether an initial contact message from a user will be replied to by the receiver. The study is based on a large scale real-world dataset obtained from a major dating site in China with more than sixty million registered users. We formulate our reply prediction as a link prediction problem of social networks and approach it using a machine learning framework. The availability of a large amount of user profile information and the bipartite nature of the dating network present unique opportunities and challenges to the reply prediction problem. We extract user-based features from user profiles and graph-based features from the bipartite dating network, apply them in a variety of classification algorithms, and compare the utility of the features and performance of the classifiers. Our results show that the user-based and graph-based features result in similar performance, and can be used to effectively predict the reciprocal links. Only a small performance gain is achieved when both feature sets are used. Among the five classifiers we considered, random forests method outperforms the other four algorithms (naive Bayes, logistic regression, KNN, and SVM). Our methods and results can provide valuable guidelines to the design and performance of recommendation engine for online dating sites. We present a new recommender system for online dating. Using a large dataset from a major online dating website, we first show that similar people, as defined by a set of personal attributes, like and dislike similar people and are liked and disliked by similar people. This analysis provides the foundation for our Content-Collaborative Reciprocal (CCR) recommender approach. The content-based part uses selected user profile features and similarity measure to generate a set of similar users. The collaborative filtering part uses the interactions of the similar users, including the people they like dislike and are liked disliked by, to produce reciprocal recommendations. CCR addresses the cold start problem of new users joining the site by being able to provide recommendations immediately, based on their profiles. 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. Online dating sites have become popular platforms for people to look for potential romantic partners. 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. 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. 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. In particular, males tend to be focused on their own interest and oblivious towards their attractiveness to potential dates, while females are more conscientious to their own attractiveness to the other side of the line. Match-making systems refer to systems where users want to meet other individuals to satisfy some underlying need. Examples of match-making systems include dating services, resume job bulletin boards, community based question answering, and consumer-to-consumer marketplaces. One fundamental component of a match-making system is the retrieval and ranking of candidate matches for a given user. We present the first in-depth study of information retrieval approaches applied to match-making systems. Specifically, we focus on retrieval for a dating service. This domain offers several unique problems not found in traditional information retrieval tasks. These include two-sided relevance, very subjective relevance, extremely few relevant matches, and structured queries. We propose a machine learned ranking function that makes use of features extracted from the uniquely rich user profiles that consist of both structured and unstructured attributes. An extensive evaluation carried out using data gathered from a real online dating service shows the benefits of our proposed methodology with respect to traditional match-making baseline systems. Our analysis also provides deep insights into the aspects of match-making that are particularly important for producing highly relevant matches.