Social Matching Systems Research Articles

Directions for professional social matching systems

Future PSM systems will require diversity-enhancing yet contextually sensitive designs.

Social Matching Systems, Intimate Personal Data, and Romantic Compatibility on Internet Dating Sites and Apps

Social Matching Systems, Intimate Personal Data, and Romantic Compatibility on Internet Dating Sites and Apps

Scholars\u2019 Perceptions of Relevance in Bibliography-Based People Recommender System

Collaboration and social networking are increasingly important for academics, yet identifying relevant collaborators requires remarkable effort. While there are various networking services optimized for seeking similarities between the users, the scholarly motive of producing new knowledge calls for assistance in identifying people with complementary qualities. However, there is little empirical understanding of how academics perceive relevance, complementarity, and diversity of individuals in their profession and how these concepts can be optimally embedded in social matching systems. This paper aims to support the development of diversity-enhancing people recommender systems by exploring senior researchers’ perceptions of recommended other scholars at different levels on a similar–different continuum. To conduct the study, we built a recommender system based on topic modeling of scholars’ publications in the DBLP computer science bibliography. A study of 18 senior researchers comprised a controlled experiment and semi-structured interviewing, focusing on their subjective perceptions regarding relevance, similarity, and familiarity of the given recommendations, as well as participants’ readiness to interact with the recommended people. The study implies that the homophily bias (behavioral tendency to select similar others) is strong despite the recognized need for complementarity. While the experiment indicated consistent and significant differences between the perceived relevance of most similar vs. other levels, the interview results imply that the evaluation of the relevance of people recommendations is complex and multifaceted. Despite the inherent bias in selection, the participants could identify highly interesting collaboration opportunities on all levels of similarity.

Geolocation-based Phone Dating Apps, Digital Intimacies, and Social Matching Systems in the Online Sexual Marketplace

Geolocation-based Phone Dating Apps, Digital Intimacies, and Social Matching Systems in the Online Sexual Marketplace

Identifying Opportunities for Valuable Encounters

Mobile social matching systems have the potential to transform the way we make new social ties, but only if we are able to overcome the many challenges that exist as to how systems can utilize contextual data to recommend interesting and relevant people to users and facilitate valuable encounters between strangers. This article outlines how context and mobility influence people's motivations to meet new people and presents innovative design concepts for mediating mobile encounters through context-aware social matching systems. Findings from two studies are presented. The first, a survey study (n = 117) explored the concept of contextual rarity of shared user attributes as a measure to improve desirability in mobile social matches. The second, an interview study (n = 58) explored people's motivations to meet others in various contexts. From these studies we derived a set of novel context-aware social matching concepts, including contextual sociability and familiarity as an indicator of opportune social context; contextual engagement as an indicator of opportune personal context; and contextual rarity, oddity, and activity partnering as an indicator of opportune relational context. The findings of these studies establish the importance of different contextual factors and frame the design space of context-aware social matching systems.

An Approach for Personalized Social Matching Systems by Using Ant Colony

Personalized social matching systems can be seen as recommender systems that recommend people to others in the social networks, with desirable skills/characteristics. In this work, an algorithm based on Ant Colony is proposed to solve the optimization problem of clustering/matching people in a social network specifically designed for this purpose; during this process, their personal characteristics and preferences (and the degree of importance thereof) are taken into account. The numerical results indicate that the proposed algorithm can successfully perform clustering with a variable number of individuals.

PERSONALITYML: A MARKUP LANGUAGE TO STANDARDIZE THE USER PERSONALITY IN RECOMMENDER SYSTEMS

In recent years the study of how human psychological aspects may improve the decision-making process in computers has became a new trend. This subject has attracted the attention from both academy and industry in areas such as human-computer interaction, computer in education, recommender systems and social matching systems, among others. However, one of the biggest problems faced by them is how effectively to use, model and implement those psychological aspects in computers. This paper comes to fill partly this gap by proposing a markup language to standardize the representation of personality. The PersonalityML proposes a set of recommender inputs to be used as starting data to classical cold-start problem in recommender systems, as well as, in personality-based recommender systems and others personality-based web applications.

Social matching

Social matching systems bring people together in both physical and online spaces. They have the potential to increase social interaction and foster collaboration. However, social matching systems lack a clear intellectual foundation: the nature of the design space, the key research challenges, and the roster of appropriate methods are all ill-defined. This article begins to remedy the situation. It clarifies the scope of social matching systems by distinguishing them from other recommender systems and related systems and techniques. It identifies a set of issues that characterize the design space of social matching systems and shows how existing systems explore different points within the design space. It also reviews selected social science results that can provide input into system design. Most important, the article presents a research agenda organized around a set of claims. The claims embody our understanding of what issues are most important to investigate, our beliefs about what is most likely to be true, and our suggestions of specific research directions to pursue.