On-line dating; is a digital kiss and virtual wink the new way of finding love?

In 2007, I asked for a show of hands of people who ‘might consider online dating as a way of finding a partner’. Not a single hand went up and the lecture theatre was stony silent. The same question, some five years later resulted in a significant show of hands and a buzz of chatter. Something had changed and perceptions around online dating had shifted. Nielsen Research last year found most Australians (51 per cent) had either tried online dating or would consider doing so. RSVP and eHarmony claim to have 2 million members and more than 4 million people have apparently joined RSVP since it was launched 17 years ago. Online dating is experiencing significant growth in Asia as well, with the number of new web services (some, like ‘Muslima’, are tightly focused) growing exponentially. Online dating is a global phenomenon. This paper will use current media studies research literature and data from conversations with university students in Australia and Indonesia to explore how the changing world of online dating is helping/hindering young people as they shape and develop identity, represent themselves in the virtual world and ultimately, how they find love on line.

The Online Romance Scam: A Serious Cybercrime

The Online Romance Scam is a relatively new form of fraud that became apparent in about 2008. In this crime, criminals pretend to initiate a relationship through online dating sites then defraud their victims of large sums of money. This paper presents some descriptive statistics about knowledge and victimization of the online dating romance scam in Great Britain. Our study found that despite its newness, an estimated 230,000 British citizens may have fallen victim to this crime. We conclude that there needs to be some rethinking about providing avenues for victims to report the crime or at least making them more comfortable when doing so.

Longitudinal Incidence Study of Clinically-Relevant HPV and Vaccine-Type HPV in Young Online Female Daters

Thesis (Master's)--University of Washington, 2016-08

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.

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).

A people-to-people recommendation system using Tensor Space Models

Existing recommendation systems often recommend products to users by capturing the item-to-item and user-to-user similarity measures. These types of recommendation systems become inefficient in people-to-people networks for people to people recommendation that require two way relationship. Also, existing recommendation methods use traditional two dimensional models to find inter relationships between alike users and items. It is not efficient enough to model the people-to-people network with two-dimensional models as the latent correlations between the people and their attributes are not utilized. In this paper, we propose a novel tensor decomposition-based recommendation method for recommending people-to-people based on users profiles and their interactions. The people-to-people network data is multi-dimensional data which when modeled using vector based methods tend to result in information loss as they capture either the interactions or the attributes of the users but not both the information. This paper utilizes tensor models that have the ability to correlate and find latent relationships between similar users based on both information, user interactions and user attributes, in order to generate recommendations. Empirical analysis is conducted on a real-life online dating dataset. As demonstrated in results, the use of tensor modeling and decomposition has enabled the identification of latent correlations between people based on their attributes and interactions in the network and quality recommendations have been derived using the 'alike' users concept.

Finding and matching communities in social networks using data mining

The rapid growth in the number of users using social networks and the information that a social network requires about their users make the traditional matching systems insufficiently adept at matching users within social networks. This paper introduces the use of clustering to form communities of users and, then, uses these communities to generate matches. Forming communities within a social network helps to reduce the number of users that the matching system needs to consider, and helps to overcome other problems from which social networks suffer, such as the absence of user activities' 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 the community information.

Kiss

In an age of mobile phones, Facebook, Twitter and online dating, interactions in mediated environments often outnumber face to face encounters. Kiss is an interactive light artwork by artists Priscilla Bracks & Gavin Sade. Kiss reacts to people standing in front of the artwork looking at each other - the moment before kissing. Without interaction the work generates a seductive, ambient, red lighting display, that creates the restful sense of staring into a fire. A fleeting response of white light – like sparks flying in the air – occurs the moment before two faces touch. These sparks are visible in peripheral vision, but fade when the kissing couple turns to look at the work. This moment - as two people look at each other - is a primal moment when two people recognise each other. Face to face encounters with another person are a privileged phenomenon in which the other person's presence and proximity are strongly felt. Kiss does not respond to every instance of a kiss or a look. Its recognition algorithms are fussy, selecting some faces and not others. As in life it’s difficult to tell why sparks fly with some people but not with others. For some this will be felt as a glitch. “This machine should be part of my social life!” But it does promote trial and error, asking viewers to be intimate in public and look at each other for longer than otherwise socially normal. 10 minutes continuous eye contact is said in most cases to arouse sexual feelings in both parties. But even if we don’t look that long, a short time may be all that is needed to explore the face of the person we are looking at. We see that they are human like us. We experience beauty, difference, discomfort, perhaps even nervous laughing, before turning to a more intimate moment of recognition.

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.

Grouping people in social networks using a weighted multi-constraints clustering method

Grouping users in social networks is an important process that improves matching and recommendation activities in social networks. The data mining methods of clustering can be used in grouping the users in social networks. However, the existing general purpose clustering algorithms perform poorly on the social network data due to the special nature of users' data in social networks. One main reason is the constraints that need to be considered in grouping users in social networks. Another reason is the need of capturing large amount of information about users which imposes computational complexity to an algorithm. In this paper, we propose a scalable and effective constraint-based clustering algorithm based on a global similarity measure that takes into consideration the users' constraints and their importance in social networks. Each constraint's importance is calculated based on the occurrence of this constraint in the dataset. Performance of the algorithm is demonstrated on a dataset obtained from an online dating website using internal and external evaluation measures. Results show that the proposed algorithm is able to increases the accuracy of matching users in social networks by 10% in comparison to other algorithms.

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.

A reciprocal collaborative method using relevance feedback and feature importance

In a people-to-people matching systems, filtering is widely applied to find the most suitable matches. The results returned are either too many or only a few when the search is generic or specific respectively. The use of a sophisticated recommendation approach becomes necessary. Traditionally, the object of recommendation is the item which is inanimate. In online dating systems, reciprocal recommendation is required to suggest a partner only when the user and the recommended candidate both are satisfied. In this paper, an innovative reciprocal collaborative method is developed based on the idea of similarity and common neighbors, utilizing the information of relevance feedback and feature importance. Extensive experiments are carried out using data gathered from a real online dating service. Compared to benchmarking methods, our results show the proposed method can achieve noticeable better performance.

A recommendation approach dealing with multiple market segments

A new community and communication type of social networks - online dating - are gaining momentum. With many people joining in the dating network, users become overwhelmed by choices for an ideal partner. A solution to this problem is providing users with partners recommendation based on their interests and activities. Traditional recommendation methods ignore the users’ needs and provide recommendations equally to all users. In this paper, we propose a recommendation approach that employs different recommendation strategies to different groups of members. A segmentation method using the Gaussian Mixture Model (GMM) is proposed to customize users’ needs. Then a targeted recommendation strategy is applied to each identified segment. Empirical results show that the proposed approach outperforms several existing recommendation methods.

Two-way recommendation methods for social networks

In this work, we present the challenges associated with the two-way recommendation methods in social networks and the solutions. We discuss them from the perspective of community-type social networks such as online dating networks.