@inproceedings{discovery10075203,
           month = {November},
          series = {Lecture Notes in Computer Science (LNCS)},
           title = {Heterogeneous Dyadic Multi-task Learning with Implicit Feedback},
            year = {2018},
       publisher = {Springer},
          volume = {11303},
            note = {This version is the author accepted manuscript. For information on re-use, please refer to the publisher's terms and conditions.},
         address = {Cham, Switzerland},
       booktitle = {Proceedings of 25th International Conference on Neural Information Processing (ICONIP 2018)},
          editor = {Long Cheng and Andrew Chi Sing Leung and Seiichi Ozawa},
          author = {Moura, S and Asarbaev, A and Amini, M-R},
        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.},
             url = {https://discovery.ucl.ac.uk/id/eprint/10075203/},
        keywords = {Recommendation systems, Multiple implicit feedback, Dyadic prediction, Multi-task learning}
}