The use of Vector Space Models (VSM) in the area of Information Retrieval is an established practice, thanks to its very clean and solid formalism that allows us to easily represent objects in a vector space and to perform calculations on them. The goal of this work is to investigate the impact of VSM on Recommender Systems (RS) performance. Specifically, we will introduce two approaches: The first is based on a dimensionality reduction technique called Random Indexing, while the second extends the previous one by integrating a negation operator implemented in the Semantic Vectors open-source package. The results emerged from the experimental evaluation confirmed the predictive accuracy of the model. This work summarizes the results already presented in the RecSys 2010 Doctoral Consortium.
Random indexing for content-based recommender systems
Musto C.;Lops P.;De Gemmis M.;Semeraro G.
2011
Abstract
The use of Vector Space Models (VSM) in the area of Information Retrieval is an established practice, thanks to its very clean and solid formalism that allows us to easily represent objects in a vector space and to perform calculations on them. The goal of this work is to investigate the impact of VSM on Recommender Systems (RS) performance. Specifically, we will introduce two approaches: The first is based on a dimensionality reduction technique called Random Indexing, while the second extends the previous one by integrating a negation operator implemented in the Semantic Vectors open-source package. The results emerged from the experimental evaluation confirmed the predictive accuracy of the model. This work summarizes the results already presented in the RecSys 2010 Doctoral Consortium.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
