Resumo

ALEKSEEV, Anton  e  NIKOLENKO, Sergey. Word Embeddings for User Profiling in Online Social Networks. Comp. y Sist. [online]. 2017, vol.21, n.2, pp.203-226. ISSN 2007-9737.  https://doi.org/10.13053/cys-21-2-2734.

User profiling in social networks can be significantly augmented by using available full-text items such as posts or statuses and ratings (in the form of likes) that users give them. In this work, we apply modern natural language processing techniques based on word embeddings to several problems related to user profiling in social networks. First, we present an approach to create user profiles that 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, e.g., for cold start recommendations for items, targeted advertisement, and other purposes; our experiments show that the interests mining method performs on a level comparable with collaborative algorithms while at the same time being a cold start approach, i.e., it does not use the likes of an item being recommended. Second, we study the problem of predicting a user’s demographic attributes such as age and gender based on his or her full-text items. We evaluate the efficiency of various age prediction algorithms based on word2vec word embeddings and conduct an extensive experimental evaluation, comparing these algorithms with each other and with classical baseline approaches.

Palavras-chave : User profiling; word embeddings; distributional semantics; ranking.

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