Abstract is missing.
- Keynote: Bayesian Deep Learning Models for Recommendation ApplicationsDit-Yan Yeung. 1 [doi]
- Contextual Sequence Modeling for Recommendation with Recurrent Neural NetworksElena Smirnova, Flavian Vasile. 2-9 [doi]
- Specializing Joint Representations for the task of Product RecommendationThomas Nedelec, Elena Smirnova, Flavian Vasile. 10-18 [doi]
- Towards Recommender Systems for Police Photo LineupLadislav Peska, Hana Trojanova. 19-23 [doi]
- Inter-Session Modeling for Session-Based RecommendationMassimiliano Ruocco, Ole Steinar Lillestøl Skrede, Helge Langseth. 24-31 [doi]
- A Deep Multimodal Approach for Cold-start Music RecommendationSergio Oramas, Oriol Nieto, Mohamed Sordo, Xavier Serra. 32-37 [doi]
- Recurrent Latent Variable Networks for Session-Based RecommendationSotirios P. Chatzis, Panayiotis Christodoulou, Andreas S. Andreou. 38-45 [doi]
- Music Playlist Continuation by Learning from Hand-Curated Examples and Song Features: Alleviating the Cold-Start Problem for Rare and Out-of-Set SongsAndreu Vall, Hamid Eghbal-zadeh, Matthias Dorfer, Markus Schedl, Gerhard Widmer. 46-54 [doi]
- Comparing Neural and Attractiveness-based Visual Features for Artwork RecommendationVicente Dominguez, Pablo Messina, Denis Parra, Domingo Mery, Christoph Trattner, Alvaro Soto. 55-59 [doi]
- Auto-Encoding User Ratings via Knowledge Graphs in Recommendation ScenariosVito Bellini, Vito Walter Anelli, Tommaso Di Noia, Eugenio Di Sciascio. 60-66 [doi]