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Text-based collaborative filtering for cold-start soothing and recommendation enrichment

Abstract : —The difficulty to deal with new users, items and the poor explainability of predictions are well-known weaknesses of collaborative filtering. Classically, the cold-start issue is tackled either by asking for user interaction or exploiting side information while additional explanations are often extracted afterwards in a standalone process. Here, we propose a text-based collabora-tive filtering recommender system which provides a framework to solve both issues. Our method extends a scalable text embedding technique to build a unified vector space where users and items are mapped. We show how to use this space in a collaborative filtering scheme and demonstrate the interest of our text-based method for cold start soothing and recommendation explanation. The suitability of our approach is backed by competitive results on rating prediction task.
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Contributor : Raphaël Fournier-S'Niehotta <>
Submitted on : Monday, November 20, 2017 - 5:04:18 PM
Last modification on : Thursday, March 21, 2019 - 1:08:13 PM
Document(s) archivé(s) le : Wednesday, February 21, 2018 - 2:05:24 PM


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  • HAL Id : hal-01640268, version 1


Charles-Emmanuel Dias, Vincent Guigue, Patrick Gallinari. Text-based collaborative filtering for cold-start soothing and recommendation enrichment. AISR2017, May 2017, Paris, France. ⟨hal-01640268⟩



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