Social Recommendations Systems: Leveraging the Power of Social Networks in Generating Recommendations

Ofer Arazy; Nanda Kumar; Bracha Shapira

Social Recommendations Systems: Leveraging the Power of Social Networks in Generating Recommendations

Ofer Arazy, Nanda Kumar, Bracha Shapira

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

Abstract

Recommendation systems, and specifically Social Filtering (SF) systems, play a significant role in reducing information overload and providing users with information relevant to their specific interest. For over a decade now, the ad-hoc standard in social filtering employed an approach, where recommendations were generated by computing "shared interests" based on users' preferences for items. The rapid growth in online social networks presents an opportunity for a new social filtering approach. The main thrust of our work is in identifying the relevant relationship characteristics among participants who know each other and use these characteristics to improve the quality of the recommendations generated. This paper develops a model that incorporates users'

Original language	English
Title of host publication	Proceeding of the 1st Design Science Research in Information Systems and Technology conference (DESRIST)
Number of pages	20
State	Published - 2006
Externally published	Yes

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Arazy, O., Kumar, N., & Shapira, B. (2006). Social Recommendations Systems: Leveraging the Power of Social Networks in Generating Recommendations. In Proceeding of the 1st Design Science Research in Information Systems and Technology conference (DESRIST) http://oferarazy.com/PDF/Social%20Recommendations%20Systems%20Leveraging%20the%20Power%20of%20Social%20Networks%20in%20Generating%20Recommendations.pdf

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title = "Social Recommendations Systems: Leveraging the Power of Social Networks in Generating Recommendations",

abstract = "Recommendation systems, and specifically Social Filtering (SF) systems, play a significant role in reducing information overload and providing users with information relevant to their specific interest. For over a decade now, the ad-hoc standard in social filtering employed an approach, where recommendations were generated by computing {"}shared interests{"} based on users' preferences for items. The rapid growth in online social networks presents an opportunity for a new social filtering approach. The main thrust of our work is in identifying the relevant relationship characteristics among participants who know each other and use these characteristics to improve the quality of the recommendations generated. This paper develops a model that incorporates users'",

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Arazy, O, Kumar, N & Shapira, B 2006, Social Recommendations Systems: Leveraging the Power of Social Networks in Generating Recommendations. in Proceeding of the 1st Design Science Research in Information Systems and Technology conference (DESRIST). <http://oferarazy.com/PDF/Social%20Recommendations%20Systems%20Leveraging%20the%20Power%20of%20Social%20Networks%20in%20Generating%20Recommendations.pdf>

TY - GEN

T1 - Social Recommendations Systems

T2 - Leveraging the Power of Social Networks in Generating Recommendations

AU - Arazy, Ofer

AU - Kumar, Nanda

AU - Shapira, Bracha

PY - 2006

Y1 - 2006

N2 - Recommendation systems, and specifically Social Filtering (SF) systems, play a significant role in reducing information overload and providing users with information relevant to their specific interest. For over a decade now, the ad-hoc standard in social filtering employed an approach, where recommendations were generated by computing "shared interests" based on users' preferences for items. The rapid growth in online social networks presents an opportunity for a new social filtering approach. The main thrust of our work is in identifying the relevant relationship characteristics among participants who know each other and use these characteristics to improve the quality of the recommendations generated. This paper develops a model that incorporates users'

AB - Recommendation systems, and specifically Social Filtering (SF) systems, play a significant role in reducing information overload and providing users with information relevant to their specific interest. For over a decade now, the ad-hoc standard in social filtering employed an approach, where recommendations were generated by computing "shared interests" based on users' preferences for items. The rapid growth in online social networks presents an opportunity for a new social filtering approach. The main thrust of our work is in identifying the relevant relationship characteristics among participants who know each other and use these characteristics to improve the quality of the recommendations generated. This paper develops a model that incorporates users'

M3 - Conference contribution

BT - Proceeding of the 1st Design Science Research in Information Systems and Technology conference (DESRIST)

ER -

Social Recommendations Systems: Leveraging the Power of Social Networks in Generating Recommendations

Abstract

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