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- Publisher Website: 10.1145/2600428.2609554
- Scopus: eid_2-s2.0-84904570429
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Conference Paper: A Revisit to Social Network-Based Recommender Systems
Title | A Revisit to Social Network-Based Recommender Systems |
---|---|
Authors | |
Keywords | Recommender System Social Network Social Influence |
Issue Date | 2014 |
Publisher | Association for Computing Machinery (ACM). |
Citation | Proceedings of the 37th Annual International Association for Computing Machinery (ACM) Special Interest Group On Information Retrieval (SIGIR) Conference, Gold Coast, Australia, 6-11 July 2014, p. 1239-1242 How to Cite? |
Abstract | With the rapid expansion of online social networks, social network-based recommendation has become a meaningful and effective way of suggesting new items or activities to users. In this paper, we propose two methods to improve the performance of the state-of-art social network-based recommender system (SNRS), which is based on a probabilistic model. Our first method classifies the correlations between pairs of users' ratings. The other is making the system robust to sparse data, i.e., few immediate friends having few common ratings with the target user. Our experimental study demonstrates that our techniques significantly improve the accuracy of SNRS. |
Persistent Identifier | http://hdl.handle.net/10722/198601 |
ISBN |
DC Field | Value | Language |
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dc.contributor.author | Li, H | en_US |
dc.contributor.author | Wu, D | en_US |
dc.contributor.author | Mamoulis, N | en_US |
dc.date.accessioned | 2014-07-07T08:09:39Z | - |
dc.date.available | 2014-07-07T08:09:39Z | - |
dc.date.issued | 2014 | en_US |
dc.identifier.citation | Proceedings of the 37th Annual International Association for Computing Machinery (ACM) Special Interest Group On Information Retrieval (SIGIR) Conference, Gold Coast, Australia, 6-11 July 2014, p. 1239-1242 | en_US |
dc.identifier.isbn | 9781450322577 | - |
dc.identifier.uri | http://hdl.handle.net/10722/198601 | - |
dc.description.abstract | With the rapid expansion of online social networks, social network-based recommendation has become a meaningful and effective way of suggesting new items or activities to users. In this paper, we propose two methods to improve the performance of the state-of-art social network-based recommender system (SNRS), which is based on a probabilistic model. Our first method classifies the correlations between pairs of users' ratings. The other is making the system robust to sparse data, i.e., few immediate friends having few common ratings with the target user. Our experimental study demonstrates that our techniques significantly improve the accuracy of SNRS. | - |
dc.language | eng | en_US |
dc.publisher | Association for Computing Machinery (ACM). | - |
dc.relation.ispartof | Proceedings of the Annual ACM SIGIR Conference | en_US |
dc.subject | Recommender System | - |
dc.subject | Social Network | - |
dc.subject | Social Influence | - |
dc.title | A Revisit to Social Network-Based Recommender Systems | en_US |
dc.type | Conference_Paper | en_US |
dc.identifier.email | Wu, D: dmwu@cs.hku.hk | en_US |
dc.identifier.email | Mamoulis, N: nikos@cs.hku.hk | en_US |
dc.identifier.authority | Mamoulis, N=rp00155 | en_US |
dc.description.nature | published_or_final_version | - |
dc.identifier.doi | 10.1145/2600428.2609554 | - |
dc.identifier.scopus | eid_2-s2.0-84904570429 | - |
dc.identifier.hkuros | 230025 | en_US |
dc.identifier.spage | 1239 | - |
dc.identifier.epage | 1242 | - |
dc.publisher.place | United States | - |