Moura, S;
Asarbaev, A;
Amini, M-R;
(2018)
Heterogeneous Dyadic Multi-task Learning with Implicit Feedback.
In: Cheng, Long and Chi Sing Leung, Andrew and Ozawa, Seiichi, (eds.)
Proceedings of 25th International Conference on Neural Information Processing (ICONIP 2018).
Springer: Cham, Switzerland.
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Abstract
In this paper we present a framework for learning models for Recommender Systems (RS) in the case where there are multiple implicit feedback associated to items. Based on a set of features, representing the dyads of users and items extracted from an implicit feedback collection, we propose a stochastic gradient descent algorithm that learn jointly classification, ranking and embeddings for users and items. Our experimental results on a subset of the collection used in the RecSys 2016 challenge for job recommendation show the effectiveness of our approach with respect to single task approaches and paves the way for future work in jointly learning models for multiple implicit feedback for RS.
| Type: | Proceedings paper |
|---|---|
| Title: | Heterogeneous Dyadic Multi-task Learning with Implicit Feedback |
| Event: | 25th International Conference on Neural Information Processing (ICONIP 2018), 13-16 December 2018, Siem Reap, Cambodia |
| ISBN-13: | 978-3-030-04181-6 |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.1007/978-3-030-04182-3_58 |
| Language: | English |
| Additional information: | This version is the author accepted manuscript. For information on re-use, please refer to the publisher’s terms and conditions. |
| Keywords: | Recommendation systems, Multiple implicit feedback, Dyadic prediction, Multi-task learning |
| UCL classification: | UCL UCL > Provost and Vice Provost Offices > UCL BEAMS UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Computer Science |
| URI: | https://discovery.ucl.ac.uk/id/eprint/10075203 |
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