Enhanced vector space models for content-based recommender systems

The use of Vector Space Models (VSM) in the area of Infor-mation Retrieval is an established practice within the sci-entific community. The reason is twofold: first, its very clean and solid formalism allows us to represent objects in a vector space and to perform calculations on them. On the other hand, as proved by many contributions, its simplic-ity does not hurt the effectiveness of the model. Although Information Retrieval and Information Filtering undoubt-edly represent two related research areas, the use of VSM in Information Filtering is much less analzyed. The goal of this work is to investigate the impact of vector space models in the Information Filtering area. Specifically, I will introduce two approaches: the first one, based on a technique called Random Indexing, reduces the impact of two classical VSM problems, this is to say its high dimen-sionality and the inability to manage the semantics of docu-ments. The second extends the previous one by integrating a negation operator implemented in the Semantic Vectors1 open-source package. The results emerged from an exper-imental evaluation performed on a large dataset and the applicative scenarios opened by these approaches confirmed the effectiveness of the model and induced to investigate more these techniques