Information filtering and information retrieval: two sides of the same coin?
Communications of the ACM - Special issue on information filtering
Probabilistic latent semantic indexing
Proceedings of the 22nd annual international ACM SIGIR conference on Research and development in information retrieval
A vector space model for automatic indexing
Communications of the ACM
Random projection in dimensionality reduction: applications to image and text data
Proceedings of the seventh ACM SIGKDD international conference on Knowledge discovery and data mining
User Modeling for Adaptive News Access
User Modeling and User-Adapted Interaction
The Geometry of Information Retrieval
The Geometry of Information Retrieval
Orthogonal negation in vector spaces for modelling word-meanings and document retrieval
ACL '03 Proceedings of the 41st Annual Meeting on Association for Computational Linguistics - Volume 1
Cross-language information filtering: word sense disambiguation vs. distributional models
AI*IA'11 Proceedings of the 12th international conference on Artificial intelligence around man and beyond
A probabilistic definition of item similarity
Proceedings of the fifth ACM conference on Recommender systems
Preference elicitation techniques for group recommender systems
Information Sciences: an International Journal
Enhanced semantic TV-show representation for personalized electronic program guides
UMAP'12 Proceedings of the 20th international conference on User Modeling, Adaptation, and Personalization
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The use of Vector Space Models (VSM) in the area of Information Retrieval is an established practice within the scientific 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 simplicity does not hurt the effectiveness of the model. Although Information Retrieval and Information Filtering undoubtedly 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 dimensionality and the inability to manage the semantics of documents. The second extends the previous one by integrating a negation operator implemented in the Semantic Vectors1 open-source package. The results emerged from an experimental 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.