4 research outputs found
A Compositional Model of Multi-faceted Trust for Personalized Item Recommendation
Trust-based recommender systems improve rating prediction with respect to
Collaborative Filtering by leveraging the additional information provided by a
trust network among users to deal with the cold start problem. However, they
are challenged by recent studies according to which people generally perceive
the usage of data about social relations as a violation of their own privacy.
In order to address this issue, we extend trust-based recommender systems with
additional evidence about trust, based on public anonymous information, and we
make them configurable with respect to the data that can be used in the given
application domain: 1 - We propose the Multi-faceted Trust Model (MTM) to
define trust among users in a compositional way, possibly including or
excluding the types of information it contains. MTM flexibly integrates social
links with public anonymous feedback received by user profiles and user
contributions in social networks. 2 - We propose LOCABAL+, based on MTM, which
extends the LOCABAL trust-based recommender system with multi-faceted trust and
trust-based social regularization. Experiments carried out on two public
datasets of item reviews show that, with a minor loss of user coverage,
LOCABAL+ outperforms state-of-the art trust-based recommender systems and
Collaborative Filtering in accuracy, ranking of items and error minimization
both when it uses complete information about trust and when it ignores social
relations. The combination of MTM with LOCABAL+ thus represents a promising
alternative to state-of-the-art trust-based recommender systems