The explosive growth of online social networks in recent times has presented a powerful source of information to be utilised in personalised recommendations. Unsurprisingly there has already been a large body of work completed in the recommender system field to incorporate this social information into the recommendation process. In this paper we examine the practice of leveraging a user's social graph in order to generate recommendations. Using various neighbourhood selection strategies, we examine the user satisfaction and the level of perceived trust in the recommendations received.
%0 Conference Paper
%1 Bourke:2011:PPE:2043932.2043997
%A Bourke, Steven
%A McCarthy, Kevin
%A Smyth, Barry
%B Proceedings of the Fifth ACM Conference on Recommender Systems
%C New York, NY, USA
%D 2011
%I ACM
%K Social-system neighbourhood recommender social-information-access social-network
%P 337--340
%R 10.1145/2043932.2043997
%T Power to the People: Exploring Neighbourhood Formations in Social Recommender System
%U http://doi.acm.org/10.1145/2043932.2043997
%X The explosive growth of online social networks in recent times has presented a powerful source of information to be utilised in personalised recommendations. Unsurprisingly there has already been a large body of work completed in the recommender system field to incorporate this social information into the recommendation process. In this paper we examine the practice of leveraging a user's social graph in order to generate recommendations. Using various neighbourhood selection strategies, we examine the user satisfaction and the level of perceived trust in the recommendations received.
%@ 978-1-4503-0683-6
@inproceedings{Bourke:2011:PPE:2043932.2043997,
abstract = {The explosive growth of online social networks in recent times has presented a powerful source of information to be utilised in personalised recommendations. Unsurprisingly there has already been a large body of work completed in the recommender system field to incorporate this social information into the recommendation process. In this paper we examine the practice of leveraging a user's social graph in order to generate recommendations. Using various neighbourhood selection strategies, we examine the user satisfaction and the level of perceived trust in the recommendations received.},
acmid = {2043997},
added-at = {2018-02-14T01:18:44.000+0100},
address = {New York, NY, USA},
author = {Bourke, Steven and McCarthy, Kevin and Smyth, Barry},
biburl = {https://www.bibsonomy.org/bibtex/2252ab5456d91afd2ef80fc0460390223/aziz885},
booktitle = {Proceedings of the Fifth ACM Conference on Recommender Systems},
description = {Power to the people},
doi = {10.1145/2043932.2043997},
interhash = {32c8a43d94691d7dc1a27ad93f257911},
intrahash = {252ab5456d91afd2ef80fc0460390223},
isbn = {978-1-4503-0683-6},
keywords = {Social-system neighbourhood recommender social-information-access social-network},
location = {Chicago, Illinois, USA},
numpages = {4},
pages = {337--340},
publisher = {ACM},
series = {RecSys '11},
timestamp = {2018-02-14T01:22:25.000+0100},
title = {Power to the People: Exploring Neighbourhood Formations in Social Recommender System},
url = {http://doi.acm.org/10.1145/2043932.2043997},
year = 2011
}