Automatic text processing
Affective computing
Application of Spreading Activation Techniques in InformationRetrieval
Artificial Intelligence Review
Sparse Distributed Memory
Hybrid Recommender Systems: Survey and Experiments
User Modeling and User-Adapted Interaction
Text-Learning and Related Intelligent Agents: A Survey
IEEE Intelligent Systems
Integrating tags in a semantic content-based recommender
Proceedings of the 2008 ACM conference on Recommender systems
On the tip of my thought: playing the Guillotine game
IJCAI'09 Proceedings of the 21st international jont conference on Artifical intelligence
Using Wikipedia to boost collaborative filtering techniques
Proceedings of the fifth ACM conference on Recommender systems
Preference elicitation techniques for group recommender systems
Information Sciences: an International Journal
Top-N recommendations from implicit feedback leveraging linked open data
Proceedings of the 7th ACM conference on Recommender systems
Workshop on recommender systems meet big data & semantic technologies: SeRSy 2013
Proceedings of the 7th ACM conference on Recommender systems
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Content-based recommender systems try to recommend items similar to those a given user has liked in the past. The basic process consists of matching up the attributes of a user profile, in which preferences and interests are stored, with the attributes of a content object (item). Common-sense and domain-specific knowledge may be useful to give some meaning to the content of items, thus helping to generate more informative features than "plain" attributes. The process of learning user profiles could also benefit from the infusion of exogenous knowledge or open source knowledge, with respect to the classical use of endogenous knowledge (extracted from the items themselves). The main contribution of this paper is a proposal for knowledge infusion into content-based recommender systems, which suggests a novel view of this type of systems, mostly oriented to content interpretation by way of the infused knowledge. The idea is to provide the system with the "linguistic" and "cultural" background knowledge that hopefully allows a more accurate content analysis than classic approaches based on words. A set of knowledge sources is modeled to create a memory of linguistic competencies and of more specific world "facts", that can be exploited to reason about content as well as to support the user profiling and recommendation processes. The modeled knowledge sources include a dictionary, Wikipedia, and content generated by users (i.e. tags provided on items), while the core of the reasoning component is a spreading activation algorithm.