Abstract
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.
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Musto, C., Semeraro, G., de Gemmis, M., Lops, P. (2016). Learning Word Embeddings from Wikipedia for Content-Based Recommender Systems. In: Ferro, N., et al. Advances in Information Retrieval. ECIR 2016. Lecture Notes in Computer Science(), vol 9626. Springer, Cham. https://doi.org/10.1007/978-3-319-30671-1_60
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DOI: https://doi.org/10.1007/978-3-319-30671-1_60
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-30670-4
Online ISBN: 978-3-319-30671-1
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