Learning word embeddings from wikipedia for content-based recommender systems

In this paper we present a preliminary investigation towards the adoption of Word Embedding techniques in a content-based recommendation scenario. Specifically, we compared the effectiveness of three widespread approaches as Latent Semantic Indexing, Random Indexing and Word2Vec in the task of learning a vector space representation of both items to be recommended as well as user profiles. To this aim, we developed a content-based recommendation (CBRS) framework which uses textual features extracted from Wikipedia to learn user profiles based on such Word Embeddings, and we evaluated this framework against two state-of-the-art datasets. The experimental results provided interesting insights, since our CBRS based on Word Embeddings showed results comparable to those of well-performing algorithms based on Collaborative Filtering and Matrix Factorization, especially in high-sparsity recommendation scenarios.