A social matching system for an online dating network : a preliminary study

Due to the change in attitudes and lifestyles, people expect to find new partners and friends via various ways now-a-days. Online dating networks create a network for people to meet each other and allow making contact with different objectives of developing a personal, romantic or sexual relationship. Due to the higher expectation of users, online matching companies are trying to adopt recommender systems. However, the existing recommendation techniques such as content-based, collaborative filtering or hybrid techniques focus on users explicit contact behaviors but ignore the implicit relationship among users in the network. This paper proposes a social matching system that uses past relations and user similarities in finding potential matches. The proposed system is evaluated on the dataset collected from an online dating network. Empirical analysis shows that the recommendation success rate has increased to 31% as compared to the baseline success rate of 19%.

Social network analysis of an online dating network

Online social networks can be found everywhere from chatting websites like MSN, blogs such as MySpace to social media such as YouTube and second life. Among them, there is one interesting type of online social networks, online dating network that is growing fast. This paper analyzes an online dating network from social network analysis point of view. Observations are made and results are obtained in order to suggest a better recommendation system for people-to-people networks.

A social matching system : using implicit and explicit information for personalized recommendation in online dating service

Online dating websites enable a specific form of social networking and their efficiency can be increased by supporting proactive recommendations based on participants' preferences with the use of data mining. This research develops two-way recommendation methods for people-to-people recommendation for large online social networks such as online dating networks. This research discovers the characteristics of the online dating networks and utilises these characteristics in developing efficient people-to-people recommendation methods. Methods developed support improved recommendation accuracy, can handle data sparsity that often comes with large data sets and are scalable for handling online networks with a large number of users.

A recommendation method for online dating networks based on social relations and demographic information

A new relationship type of social networks - online dating - are gaining popularity. With a large member base, users of a dating network are overloaded with choices about their ideal partners. Recommendation methods can be utilized to overcome this problem. However, traditional recommendation methods do not work effectively for online dating networks where the dataset is sparse and large, and a two-way matching is required. This paper applies social networking concepts to solve the problem of developing a recommendation method for online dating networks. We propose a method by using clustering, SimRank and adapted SimRank algorithms to recommend matching candidates. Empirical results show that the proposed method can achieve nearly double the performance of the traditional collaborative filtering and common neighbor methods of recommendation.

The economies within an online social network market : A case study of Ravelry

The capacity of the internet to handle micro-transactions and to cater to niche markets is a boon for some areas of the creative industries, which have always been associated with smallscale micro business activities. This paper looks at the specific case of the specialist Social Networking Site Ravelry: a site for knitters, crocheters, spinners and dyers. It traces the interactions between amateurs and professionals through the emergence of social networking sites. An analytic framework of social network markets (see Potts, Cunningham, Hartley and Omerod, 2008) is employed to allow for the inclusion of amateur, social, semi-professional,professional and institutional actors within a networked sphere of activity, rather than excluding some of these actors as outside of recognised value-production. The reliance on social networks to determine the economic success of design, production and consumption is exemplified in this small scale example. This paper eschews the dichotomy of commercial and non-commercial by bringing to the fore the hybridity of this site where financial and social economies co-exist.

Dating and intimacy in the 21st century : the use of online dating sites in Australia

This article examines the growing phenomenon of online dating and intimacy in the 21st century. The exponential rise of communications technologies, which is both reflective and constitutive of an increasingly networked and globalized society, has the potential to significantly influence the nature of intimacy in everyday life. Yet, to date, there has been a minimal response by sociologists to seek, describe and understand this influence. In this article, we present some of the key findings of our research on online dating in Australia, in order to foster a debate about the sociological impacts on intimacy in the postmodern world. Based on a web audit of more than 60 online dating sites and in-depth interviews with 23 users of online dating services, we argue that recent global trends are influencing the uptake of online technologies for the purposes of forming intimate relations. Further, some of the mediating effects of these technologies – in particular, the hypercommunication – may have specific implications for the nature of intimacy in the global era.

Improving matching process in social network

Online dating networks, a type of social network, are gaining popularity. With many people joining and being available in the network, users are overwhelmed with choices when choosing their ideal partners. This problem can be overcome by utilizing recommendation methods. However, traditional recommendation methods are ineffective and inefficient for online dating networks where the dataset is sparse and/or large and two-way matching is required. We propose a methodology by using clustering, SimRank to recommend matching candidates to users in an online dating network. Data from a live online dating network is used in evaluation. The success rate of recommendation obtained using the proposed method is compared with baseline success rate of the network and the performance is improved by double.

Online dating and intimacy in a mobile world

The nature and possibilities for intimacy between adults are changing in the mobile era. Bauman (2003) has decreed this the era of ‘liquid love’, in which intimacy is commodified and committed relationships have been replaced by fleeting connections. In contrast, Giddens (1991; 1993) suggests that the reordering of everyday life in late-stage modernity has given rise to the possibility of a democratization of interpersonal interaction, characterized by reflexive ‘pure relationships’. The purpose of this paper is to consider theoretical debates about intimacy in the mobile era with regard to the contemporary practice of online dating. Drawing on our qualitative research with 23 online daters in Australia, we argue that, while the architecture of online dating is consistent with liquid love, many online daters simultaneously desire the possibilities for consumption afforded by liquid love, while aspiring to the formation of pure relationships and/or more practical forms of caring. This creates tensions in people’s experiences of this form of purposeful meeting, which are reflective of the conflicting socialities of intimacy available to us in the mobile era. At the same time, our research revealed disruptions to these tensions, by illuminating experiences where the consumerist orientation of online dating stimulated processes of reflexive self-discovery amongst our participants.

A rule-based hybrid method for anomaly detection in online-social-network graphs

Detecting anomalies in the online social network is a significant task as it assists in revealing the useful and interesting information about the user behavior on the network. This paper proposes a rule-based hybrid method using graph theory, Fuzzy clustering and Fuzzy rules for modeling user relationships inherent in online-social-network and for identifying anomalies. Fuzzy C-Means clustering is used to cluster the data and Fuzzy inference engine is used to generate rules based on the cluster behavior. The proposed method is able to achieve improved accuracy for identifying anomalies in comparison to existing methods.

A common neighbour based two-way collaborative recommendation method

Traditional recommendation methods offer items, that are inanimate and one way recommendation, to users. Emerging new applications such as online dating or job recruitments require reciprocal people-to-people recommendations that are animate and two-way recommendations. In this paper, we propose a reciprocal collaborative method based on the concepts of users' similarities and common neighbors. The dataset employed for the experiment is gathered from a real life online dating network. The proposed method is compared with baseline methods that use traditional collaborative algorithms. Results show the proposed method can achieve noticeably better performance than the baseline methods.

Users segmentations for recommendation

Traditional recommendation methods provide recommendations equally to all users. In this paper, a segmentation method using the Gaussian Mixture Model (GMM) is proposed to customize users’ needs in order to offer a specific recommendation strategy to each segment. Experiment is conducted using a live online dating network data.

A people-to-people matching system using graph mining techniques

A people-to-people matching system (or a match-making system) refers to a system in which users join with the objective of meeting other users with the common need. Some real-world examples of these systems are employer-employee (in job search networks), mentor-student (in university social networks), consume-to-consumer (in marketplaces) and male-female (in an online dating network). The network underlying in these systems consists of two groups of users, and the relationships between users need to be captured for developing an efficient match-making system. Most of the existing studies utilize information either about each of the users in isolation or their interaction separately, and develop recommender systems using the one form of information only. It is imperative to understand the linkages among the users in the network and use them in developing a match-making system. This study utilizes several social network analysis methods such as graph theory, small world phenomenon, centrality analysis, density analysis to gain insight into the entities and their relationships present in this network. This paper also proposes a new type of graph called “attributed bipartite graph”. By using these analyses and the proposed type of graph, an efficient hybrid recommender system is developed which generates recommendation for new users as well as shows improvement in accuracy over the baseline methods.

Improving matching process in social network using implicit and explicit user information

Personalised social matching systems can be seen as recommender systems that recommend people to others in the social networks. However, with the rapid growth of users in social networks and the information that a social matching system requires about the users, recommender system techniques have become insufficiently adept at matching users in social networks. This paper presents a hybrid social matching system that takes advantage of both collaborative and content-based concepts of recommendation. The clustering technique is used to reduce the number of users that the matching system needs to consider and to overcome other problems from which social matching systems suffer, such as cold start problem due to the absence of implicit information about a new user. The proposed system has been evaluated on a dataset obtained from an online dating website. Empirical analysis shows that accuracy of the matching process is increased, using both user information (explicit data) and user behavior (implicit data).

'Try hard' : Attitudes to advertising in online social networks

Advertising has recently entered many new spaces it does not fully understand. The rules that apply in traditional media do not always translate in new media environments. However, their low cost of entry and the availability of hard-to-reach target markets, such as Generation Y, make environments such as online social networking sites attractive to marketers. This paper accumulates teenage perspectives from two qualitative studies to identify attitudes towards advertising in online social network sites and develop implications for marketers seeking to advertising on social network sites.

Getting it [on]line : sociological implications of e-dating

This article explores the role of sociology in understanding the phenomenon of online dating. Based on an examination of our qualitative study of 23 online daters, combined with the findings of the small number of other empirical studies available, we argue that further sociological consideration of the online dating phenomenon is required to: illuminate the social conditions informing these activities; enhance knowledge of if, and how, online technologies mediate intimate connections; and advance a critically informed understanding of the nature of intimacy in a global era.