Abstract
Group recommender systems have become systems of great interest in the CBR community. In previous papers we have described and validated a social recommendation model that solves different group recommendation challenges using knowledge from social networks. In this paper we have run across two identified limitations of our model, unprofiled users and “hierarchical relations” within a group, and have proposed and validated CBR solutions for them.
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Notes
- 1.
\(p_u\) represents user u’s predominant behavior according to her/his TKI evaluation [22]. It fits within a range of (0,1], 0 being the reflection of a very cooperative person and 1 the reflection of a very assertive one. This value is computed through a compulsory personality test in HappyMovie as detailed in [17].
- 2.
This factor fits within a range of (0,1], 0 being the reflection of a person someone is not close to and 1 the reflection of a person someone is really close to.
- 3.
There are several techniques for individual preferences aggregation [12], being least misery (where the minimum is taken), most pleasure (where the maximum is taken) and average satisfaction (where the average is taken) the most common ones.
- 4.
That is: users’ personality (\(p_u\)) and individual preferences (\(r_{u,i}\)) that are obtained through tests in HappyMovie and users’ trust with each other (\(t_{u,u'}\)) that is automatically computed through user’s personal information stored in Facebook profiles (see [17] for HappyMovie’s functionality details).
- 5.
This is done for obvious reasons: (1) user \(u'\), inscribing in the group the unprofiled user u, is not able to provide the system with concrete values representing user’s u personality and preferences. (2) We believe it will not be adequate or practical/possible to ask at this point to user u to start answering the needed tests.
- 6.
These ranges could be of course extended and have been selected as representations of the main stages of life.
- 7.
We have empirically assigned a value of \(\pm 1\) to \(\alpha \) in order to have a moderate impact of the dominance factor.
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Quijano-Sánchez, L., Recio-García, J.A., Díaz-Agudo, B. (2015). Modelling Hierarchical Relationships in Group Recommender Systems. In: Hüllermeier, E., Minor, M. (eds) Case-Based Reasoning Research and Development. ICCBR 2015. Lecture Notes in Computer Science(), vol 9343. Springer, Cham. https://doi.org/10.1007/978-3-319-24586-7_22
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