Verus Pronk

Enhanced Semantic TV-Show Representation for Personalized Electronic Program Guides

Lecture Notes in Computer Science, 2012

Personalized electronic program guides help users overcome information overload in the TV and vid... more Personalized electronic program guides help users overcome information overload in the TV and video domain by exploiting recommender systems that automatically compile lists of novel and diverse video assets, based on implicitly or explicitly defined user preferences. In this context, we assume that user preferences can be specified by program genres (documentary, sports, . . . ) and that an asset can be labeled by one or more program genres, thus allowing an initial and coarse preselection of potentially interesting assets. As these assets may come from various sources, program genre labels may not be consistent among these sources, or not even be given at all, while we assume that each asset has a possibly short textual description. In this paper, we tackle this problem by considering whether those textual descriptions can be effectively used to automatically retrieve the most related TV shows for a specific program genre. More specifically, we compare a statistical approach called logistic regression with an enhanced version of the commonly used vector space model, called random indexing, where the latter is extended by means of a negation operator based on quantum logic. We also apply a new feature generation technique based on explicit semantic analysis for enriching the textual description associated to a TV show with additional features extracted from Wikipedia.

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TV-Show Retrieval and Classification

Abstract. Recommender systems are popular tools to aid users in finding interesting and relevant ... more Abstract. Recommender systems are popular tools to aid users in finding interesting and relevant TV shows and other digital video assets, based on implicitly defined user preferences. In this context, a common assumption is that user preferences can be specified by program types (such as documentary, sports), and that an asset can be labeled by one or more program types, thus allowing an initial coarse preselection of potentially interesting assets. Furthermore each asset has a short textual description, which allows us to ...

Incorporating user control into recommender systems based on naive bayesian classification

Recommender systems are increasingly being employed to personalize services, such as on the web, ... more Recommender systems are increasingly being employed to personalize services, such as on the web, but also in electronics devices, such as personal video recorders. These recommenders learn a user profile, based on rating feedback from the user on, e.g., books, songs, or TV programs, and use machine learning techniques to infer the ratings of new items. The techniques commonly used are collaborative filtering and naive Bayesian classification, and they are known to have several problems, in particular the cold-start problem and its slow adaptivity to changing user preferences. These problems can be mitigated by allowing the user to set up or manipulate his profile. In this paper, we propose an extension to the naive Bayesian classifier that enhances user control. We do this by maintaining and flexibly integrating two profiles for a user, one learned by rating feedback, and one created by the user. We in particular show how the cold-start problem is mitigated.

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Enhanced Semantic TV-Show Representation for Personalized Electronic Program Guides

by Fedelucio Narducci and Verus Pronk

Lecture Notes in Computer Science, 2012

Personalized electronic program guides help users overcome information overload in the TV and vid... more Personalized electronic program guides help users overcome information overload in the TV and video domain by exploiting recommender systems that automatically compile lists of novel and diverse video assets, based on implicitly or explicitly defined user preferences. In this context, we assume that user preferences can be specified by program genres (documentary, sports,…) and that an asset can be labeled by one or more program genres, thus allowing an initial and coarse preselection of potentially interesting assets. As these ...

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