Trust-based collaborative filtering: Tackling the cold start problem using regular equivalence

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Abstract

User-based Collaborative Filtering (CF) is one of the most popular approaches to create recommender systems. This approach is based on finding the most relevant k users from whose rating history we can extract items to recommend. CF, however, suffers from data sparsity and the cold-start problem since users often rate only a small fraction of available items. One solution is to incorporate additional information into the recommendation process such as explicit trust scores that are assigned by users to others or implicit trust relationships that result from social connections between users. Such relationships typically form a very sparse trust network, which can be utilized to generate recommendations for users based on people they trust. In our work, we explore the use of regular equivalence applied to a trust network to generate a similarity matrix that is used to select the k-nearest neighbors for recommending items. We evaluate our approach on Epinions and we find that we can outperform related methods for tackling cold-start users in terms of recommendation accuracy.

Originalspracheenglisch
TitelRecSys 2018 - 12th ACM Conference on Recommender Systems
Herausgeber (Verlag)Association of Computing Machinery
Seiten446-450
Seitenumfang5
ISBN (elektronisch)9781450359016
DOIs
PublikationsstatusVeröffentlicht - 27 Sep 2018
Veranstaltung12th ACM Conference on Recommender Systems, RecSys 2018 - Vancouver, Kanada
Dauer: 2 Okt 20187 Okt 2018

Konferenz

Konferenz12th ACM Conference on Recommender Systems, RecSys 2018
LandKanada
OrtVancouver
Zeitraum2/10/187/10/18

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    ASJC Scopus subject areas

    • !!Hardware and Architecture
    • Software

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    Duricic, T., Kowald, D., Lacic, E., & Lex, E. (2018). Trust-based collaborative filtering: Tackling the cold start problem using regular equivalence. in RecSys 2018 - 12th ACM Conference on Recommender Systems (S. 446-450). Association of Computing Machinery. https://doi.org/10.1145/3240323.3240404