Collaborative filtering recommender systems are essential tools in many modern applications. Their main advantage compared with the alternatives is that they require only a matrix of user-item interactions to recommend a subset of relevant items for a given user. However, the increasing volume of the data consumed by these systems may lead to a representation model with very high sparsity and dimensionality. Several approaches to overcome this problem have been proposed, neural embeddings being one of the most recent. Since then, many recommender systems were made using this representation model, but few consumed temporal information during the learning phase. This study shows how to adapt a pioneering method of item embeddings by adding a sliding window over time, in conjunction with a split in the user’s interaction history. Results indicate that considering temporal information when learning neural embeddings for items can significantly improve the quality of the recommendations.
CITATION STYLE
Pires, P. R., Pascon, A. C., & Almeida, T. A. (2021). Time-Dependent Item Embeddings for Collaborative Filtering. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 13074 LNAI, pp. 309–324). Springer Science and Business Media Deutschland GmbH. https://doi.org/10.1007/978-3-030-91699-2_22
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