Abstract
Trust has been extensively studied and its effectiveness demonstrated in recommender systems. Due to the lack of explicit trust information in most systems, many trust metric approaches have been proposed to infer implicit trust from user ratings. However, previous works have not compared these different approaches, and oftentimes focus only on the performance of predictive item ratings. In this paper, we first analyse five kinds of trust metrics in light of the properties of trust. We conduct an empirical study to explore the ability of trust metrics to distinguish explicit trust from implicit trust and to generate accurate predictions. Experimental results on two real-world data sets show that existing trust metrics cannot provide satisfying performance, and indicate that future metrics should be designed more carefully.
Original language | English |
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Title of host publication | Proceedings of the 29th Annual ACM Symposium on Applied Computing, SAC 2014 |
Publisher | Association for Computing Machinery (ACM) |
Pages | 248-253 |
Number of pages | 6 |
ISBN (Print) | 9781450324694 |
DOIs | |
Publication status | Published - 2014 |
Externally published | Yes |
Event | 29th Annual ACM Symposium on Applied Computing, SAC 2014 - Gyeongju, Korea, Republic of Duration: 24 Mar 2014 → 28 Mar 2014 |
Conference
Conference | 29th Annual ACM Symposium on Applied Computing, SAC 2014 |
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Country/Territory | Korea, Republic of |
City | Gyeongju |
Period | 24/03/14 → 28/03/14 |
Keywords
- Ratings
- Recommender systems
- Similarity
- Trust metrics