Collective embedding for neural context-aware recommender systems

Felipe Soares Da Costa, Peter Dolog

Research output: Contribution to book/anthology/report/conference proceedingArticle in proceedingResearchpeer-review

19 Citations (Scopus)


Context-aware recommender systems consider contextual features as additional information to predict user's preferences. For example, the recommendations could be based on time, location, or the company of other people. Among the contextual information, time became an important feature because user preferences tend to change over time or be similar in the near future. Researchers have proposed diferent models to incorporate time into their recommender system, however, the current models are not able to capture specifc temporal patterns. To address the limitation observed in previous works, we propose Collective embedding for Neural Context-Aware Recommender Systems (CoNCARS). The proposed solution jointly model the item, user and time embeddings to capture temporal patterns. Then, CoNCARS use the outer product to model the user-item-time correlations between dimensions of the embedding space. The hidden features feed our Convolutional Neural Networks (CNNs) to learn the non-linearities between the diferent features. Finally, we combine the output from our CNNs in the fusion layer and then predict the user's preference score. We conduct extensive experiments on real-world datasets, demonstrating CoNCARS improves the top-N item recommendation task and outperform the state-of-the-art recommendation methods.

Original languageEnglish
Title of host publicationProceedings of the 13th ACM Conference on Recommender Systems, RecSys 2019, Copenhagen, Denmark, September 16-20, 2019.
EditorsToine Bogers, Alan Said, Peter Brusilovsky, Domonkos Tikk
Number of pages9
PublisherAssociation for Computing Machinery
Publication date2019
ISBN (Print)978-1-4503-6243-6
ISBN (Electronic)9781450362436
Publication statusPublished - 2019
EventRecSys 2019: 13th ACM Conference on Recommender Systems - Copenhagen, Denmark, Copenhagen, Denmark
Duration: 16 Sept 201820 Sept 2018
Conference number: 13


ConferenceRecSys 2019: 13th ACM Conference on Recommender Systems
LocationCopenhagen, Denmark
Internet address


  • Collective Embedding
  • Context-aware Recommender Systems
  • Convolutional Neural Networks
  • Time-aware Recommendations


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