Abstract
In this paper we consider the problem of simultaneously eliciting the preferences of a group of users in an interactive way. We focus on constructive recommendation tasks, where the instance to be recommended should be synthesized by searching in a constrained configuration space rather than choosing among a set of pre-determined options. We adopt a setwise max-margin optimization method, that can be viewed as a generalization of max-margin learning to sets, supporting the identification of informative questions and encouraging sparsity in the parameter space. We extend setwise max-margin to multiple users and we provide strategies for choosing the user to be queried next and identifying an informative query to ask. At each stage of the interaction, each user is associated with a set of parameter weights (a sort of alternative options for the unknown user utility) that can be used to identify “similar” users and to propagate preference information between them. We present simulation results evaluating the effectiveness of our procedure, showing that our approach compares favorably with respect to straightforward adaptations in a multi-user setting of elicitation methods conceived for single users.
Part of this research was done while ST was at University of Trento, partially sup- ported by CARITRO Foundation grant 2014.0372.
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Notes
- 1.
In [22], the authors convert user choices to pairwise ranking constraints using a custom procedure. Here we opted for a straightforward winner-vs-others representation, as described in the main text. This modification did not appear to significantly alter the performance of the swmm algorithm in our simulations (data not show).
- 2.
This detail is omitted from Algorithm 2 for simplicity.
- 3.
A constraint repeated l times instantiates l slack variables in OP1, thus becoming “harder” by a factor of \(\alpha l\). The effect however is much softer than for \(|\mathcal {D}^u|\).
- 4.
Our experimental setup is available at: https://github.com/stefanoteso/musm-adt17.
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Acknowledgements
This work is partially supported by the ANR project CoCoRICo-CoDec ANR-14-CE24-0007-01. ST was partially supported by the CARITRO Foundation through grant 2014.0372.
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Teso, S., Passerini, A., Viappiani, P. (2017). Constructive Preference Elicitation for Multiple Users with Setwise Max-margin. In: Rothe, J. (eds) Algorithmic Decision Theory. ADT 2017. Lecture Notes in Computer Science(), vol 10576. Springer, Cham. https://doi.org/10.1007/978-3-319-67504-6_1
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