A statistical approach to the spam problem
Linux Journal
TiVo: making show recommendations using a distributed collaborative filtering architecture
Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining
Ending Spam: Bayesian Content Filtering and the Art of Statistical Language Classification
Ending Spam: Bayesian Content Filtering and the Art of Statistical Language Classification
Toward harnessing user feedback for machine learning
Proceedings of the 12th international conference on Intelligent user interfaces
Case amazon: ratings and reviews as part of recommendations
Proceedings of the 2007 ACM conference on Recommender systems
Using data mining and recommender systems to scale up the requirements process
Proceedings of the 2nd international workshop on Ultra-large-scale software-intensive systems
Introduction to Information Retrieval
Introduction to Information Retrieval
Programming collective intelligence
Programming collective intelligence
A user profile-based personalization system for digital multimedia content
Proceedings of the 3rd international conference on Digital Interactive Media in Entertainment and Arts
COMPSAC '08 Proceedings of the 2008 32nd Annual IEEE International Computer Software and Applications Conference
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Decision Support Systems
Hackers & Painters: Big Ideas from the Computer Age
Hackers & Painters: Big Ideas from the Computer Age
Content-based tag generation to enable a tag-based collaborative tv-recommendation system.
Proceedings of the 8th international interactive conference on Interactive TV&Video
Exploiting content relevance and social relevance for personalized ad recommendation on internet TV
ACM Transactions on Multimedia Computing, Communications, and Applications (TOMCCAP)
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This paper presents an approach to build a TV recommendation system called PersonalTV that enables the use of multiple classifiers, each one specialized on selected attributes of detailed program information. For generating adequate recommendations, the system makes use of content filtering and the preferences directly specified by the user within an MPEG-7 profile. By tracking user actions and interpreting their semantics, the system is able to individually weight these actions and dynamically adjusts the process to the user's evolving preferences. We show how specialized spam fighting methods can successfully be transferred to the area of recommendation systems and adapted accordingly. Being lightweight, these methods are especially applicable in resource-constrained environments such as TV set-top boxes or mobile devices. Moreover, the use of the variance of the beta-distribution as a confidence value for each recommendation is presented.