Using co-presence communities to enhance social recommendation

Tokarchuk, Laurissa; Shoop, Karen; Ma, Anna

doi:10.1109/wons.2009.4801863

“…Researchers have also explored many other innovative applications such as matching people as they browse the web [1], introducing professionals based on skills [2] and matching people who are collocated in a geographical location [3]. Although there have been several studies by social psychologists on what attracts people to each other, there 2 has not been a well-defined study that incorporates these findings into the design principle of computer-mediated social networks.…”

Section: Motivationmentioning

confidence: 99%

“…3 shows the definitions associated with each profile's attribute and the appropriate comparison algorithm. For example if first weight W 1 was an age comparison, the profile would define it as a type Numeric or Numeric range; therefore the results in the P matrix for column one would be the results of equation A(t) or B(t) where…”

mentioning

confidence: 99%

RAIMA: a Framework for the Design and Analysis of Self-Adaptive Egocentric Social Networks

Rahnama¹

2021

Preprint

0

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An emerging research area in pervasive computing is the inference of social context in order to facilitate and mediate communications among collocated people. Understanding users' needs through information reasoning and leveraging principles of social networks plays an important role in the emergence of innovative computer-mediated social networks. This thesis introduces a generic social networking framework for the design, analysis and visualization of opportunistic social networks. The proposed framework is capable of analyzing social similarities in order to provide decision support to users in the form of ego-centric social graphs. Using opportunistic data networks, a distributed inference model is introduced to provide multi-criteria attribute matching in an ad hoc computing environment. Enhancing communications protocols to deal with real-time analysis of dynamic data, generation of spontaneous semantics, and introducing efficient social visualization techniques are salient goals of this research. Efficient pattern matching algorithms in mobile ad hoc networks can have significant benefits in generating real-time context and eliminate the need for a centralized arbiter. In our research, we demonstrate a generic and customizable software architecture for achieving efficient pattern matching in mobile ad hoc networks. In this research we present a novel design for the development of a generic matching engine that is customizable to changing social scenarios. We show how customizable semantics can play an important role in decision-making, selection of a desired attribute, and notifying users with messages in a volatile mobile network.

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“…Researchers have also explored many other innovative applications such as matching people as they browse the web [1], introducing professionals based on skills [2] and matching people who are collocated in a geographical location [3]. Although there have been several studies by social psychologists on what attracts people to each other, there 2 has not been a well-defined study that incorporates these findings into the design principle of computer-mediated social networks.…”

Section: Motivationmentioning

confidence: 99%

“…3 shows the definitions associated with each profile's attribute and the appropriate comparison algorithm. For example if first weight W 1 was an age comparison, the profile would define it as a type Numeric or Numeric range; therefore the results in the P matrix for column one would be the results of equation A(t) or B(t) where…”

mentioning

confidence: 99%

RAIMA: a Framework for the Design and Analysis of Self-Adaptive Egocentric Social Networks

Rahnama¹

2021

Preprint

0

View full text Add to dashboard Cite

An emerging research area in pervasive computing is the inference of social context in order to facilitate and mediate communications among collocated people. Understanding users' needs through information reasoning and leveraging principles of social networks plays an important role in the emergence of innovative computer-mediated social networks. This thesis introduces a generic social networking framework for the design, analysis and visualization of opportunistic social networks. The proposed framework is capable of analyzing social similarities in order to provide decision support to users in the form of ego-centric social graphs. Using opportunistic data networks, a distributed inference model is introduced to provide multi-criteria attribute matching in an ad hoc computing environment. Enhancing communications protocols to deal with real-time analysis of dynamic data, generation of spontaneous semantics, and introducing efficient social visualization techniques are salient goals of this research. Efficient pattern matching algorithms in mobile ad hoc networks can have significant benefits in generating real-time context and eliminate the need for a centralized arbiter. In our research, we demonstrate a generic and customizable software architecture for achieving efficient pattern matching in mobile ad hoc networks. In this research we present a novel design for the development of a generic matching engine that is customizable to changing social scenarios. We show how customizable semantics can play an important role in decision-making, selection of a desired attribute, and notifying users with messages in a volatile mobile network.

show abstract

“…In [293] the authors have proposed a social recommendation algorithm for use in a research social network environment. The social recommendation algorithm proposed combines the concepts of a relationship ontology and item-based collaborative filtering (CF).…”

Section: Related Workmentioning

confidence: 99%