Reciprocal recommender system for online dating

Pizzato, Luiz; Rej, Tomek; Chung, Thomas; Koprinska, Irena; Yacef, Kalina; Kay, Judy

doi:10.1145/1864708.1864787

Cited by 96 publications

(127 citation statements)

References 1 publication

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“…4. Next, we ranked potential partners for every feature separately and computed the success (or hit) rate at rank k (SR@k), a well established performance metric in recommender systems (Pizzato et al 2010). This metric indicates whether at least one actual partner has been recommended within the top k (Bischoff 2012).…”

Section: Detecting Partnershipmentioning

confidence: 99%

Detecting partnership in location-based and online social networks

Trattner

Steurer

2015

Soc. Netw. Anal. Min.

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Existing approaches to identify the tie strength between users involve typically only one type of network. To date, no studies exist that investigate the intensity of social relations and in particular partnership between users across social networks. To fill this gap in the literature, we studied over 50 social proximity features to detect the tie strength of users defined as partnership in two different types of networks: location-based and online social networks. We compared user pairs in terms of partners and non-partners and found significant differences between those users. Following these observations, we evaluated the social proximity of users via supervised and unsupervised learning approaches and establish that location-based social networks have a great potential for the identification of a partner relationship. In particular, we established that location-based social networks and correspondingly induced features based on events attended by users could identify partnership with 0.922 AUC, while online social network data had a classification power of 0.892 AUC. When utilizing data from both types of networks, a partnership could be identified to a great extent with 0.946 AUC. This article is relevant for engineers, researchers and teachers who are interested in social network analysis and mining.

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Section: Detecting Partnershipmentioning

confidence: 99%

Detecting partnership in location-based and online social networks

Trattner

Steurer

2015

Soc. Netw. Anal. Min.

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“…Recommender systems can be classified into itemto-people and people-to-people recommenders [4,5]. Item-to-people recommendation has been studied from the early stages of the Web, and has been applied in various domains.…”

Section: Related Workmentioning

confidence: 99%

People-to-People Recommendation Using Multiple Compatible Subgroups

Kim

Mahidadia

Compton

et al. 2012

Lecture Notes in Computer Science

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Abstract. People-to-people recommendation aims at suggesting suitable matches to people in a way that increases the likelihood of a positive interaction. This problem is more difficult than conventional itemto-people recommendation since the preferences of both parties need to be taken into account. Previously we proposed a profile-based recommendation method that first uses compatible subgroup rules to select a single best attribute value for each corresponding value of the user, then combines these attribute value pairs into a rule that determines the recommendations. Though this method produces a significant improvement in the probability of an interaction being successful, it has two significant limitations: (i) by considering only single matching attribute values the method ignores cases where different attribute values are closely related, missing potential candidates, and (ii) when ranking candidates for recommendation the method does not consider individual behaviour. This paper addresses these two issues, showing how multiple attributes can be used with compatible subgroup rules and individual reply rates used for ranking candidates. Our experimental results show that the new approach significantly improves the probability of an interaction being successful compared to our previous approach.

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“…Although over the past decade or so much effort has gone into creating techniques to increase and evaluate the recommendation quality for objects such as books and movies, the personalized search for subjects such as experts in a particular field has not so far received much attention [19,29,36]. This is in part due to the difficulty in measuring some of the related aspects.…”

Section: Expert Selectionmentioning

confidence: 99%

“…Systems in the second approach, on the other hand, consider social relations and network statistics. Examples include Referral Web [21] which uses coauthoring and co-citation relationships, Expert Recommender [32] which considers friendship and departmental relations, and the dating recommender [36] which considers network centrality when selecting individuals.…”

Section: Expert Selectionmentioning

confidence: 99%

Expert recommendation based on social drivers, social network analysis, and semantic data representation

Fazel-Zarandi

Devlin

Huang

et al. 2011

Proceedings of the 2nd International Workshop on Information Heterogeneity and Fusion in Recommender Systems

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Knowledge networks and recommender systems are especially important for expert finding within organizations and scientific communities. Useful recommendation of experts, however, is not an easy task for many reasons: It requires reasoning about multiple complex networks from heterogeneous sources (such as collaboration networks of individuals, article citation networks, and concept networks) and depends significantly on the needs of individuals in seeking recommendations.Although over the past decade much effort has gone into developing techniques to increase and evaluate the quality of recommendations, personalizing recommendations according to individuals' motivations has not received much attention. While previous work in the literature has focused primarily on identifying experts, our focus here is on personalizing the selection of an expert through a principled application of social science theories to model the user's motivation. In this paper, we present an expert recommender system capable of applying multiple theoretical mechanisms to the problem of personalized recommendations through profiling users' motivations and their relations. To this end, we use the Multi-Theoretical Multi-Level (MTML) framework which investigates social drivers for network formation in the communities with diverse goals. This framework serves as the theoretical basis for mapping motivations to the appropriate domain data, heuristic, and objective functions for the personalized expert recommendation.As a proof of concept, we developed a prototype recommender grounded in social science theories, and utilizing computational techniques from social network analysis and representational techniques from the semantic web to facilitate combining and operating on data from heterogeneous sources. We evaluated the prototype's ability to predict collaborations for scientific research teams, using a simple off-line methodology. Preliminary results demonstrate encouraging success while offering significant personalization options and providing flexibility in customizing the recommendation heuristic based on users' motivations. In particular, recommendation heuristics based on different motivation profiles result in different recommendations, and taken as a whole better capture the diversity of observed expert collaboration.

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Reciprocal recommender system for online dating

Cited by 96 publications

References 1 publication

Detecting partnership in location-based and online social networks

Detecting partnership in location-based and online social networks

People-to-People Recommendation Using Multiple Compatible Subgroups

Expert recommendation based on social drivers, social network analysis, and semantic data representation

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