We introduce a novel approach to constructing user profiles for recommender systems based on full-text items such as posts in a social network and implicit ratings (in the form of likes) that users give them. The profiles measure a user’s interest in various topics mined from the full texts of the items. As a result, we get a user profile that can be used for cold start recommendations for items, targeted advertisement, and other purposes. Our experiments show that the method performs on a level comparable with classical collaborative filtering algorithms while at the same time being a cold start approach, i.e., it does not use the likes of an item being recommended.
CITATION STYLE
Alekseev, A., & Nikolenko, S. (2017). User profiling in text-based recommender systems based on distributed word representations. In Communications in Computer and Information Science (Vol. 661, pp. 196–207). Springer Verlag. https://doi.org/10.1007/978-3-319-52920-2_19
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