Content-Based Book Recommending Using Learning for Text Categorization

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Content-Based Book Recommending Using Learning for Text Categorization (1999)

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by Raymond J. Mooney , Loriene Roy

Venue:	IN PROCEEDINGS OF THE FIFTH ACM CONFERENCE ON DIGITAL LIBRARIES
Citations:	333 - 8 self

BibTeX

@INPROCEEDINGS{Mooney99content-basedbook,
    author = {Raymond J. Mooney and Loriene Roy},
    title = {Content-Based Book Recommending Using Learning for Text Categorization},
    booktitle = {IN PROCEEDINGS OF THE FIFTH ACM CONFERENCE ON DIGITAL LIBRARIES},
    year = {1999},
    pages = {195--204},
    publisher = {ACM Press}
}

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Abstract

Recommender systems improve access to relevant products and information by making personalized suggestions based on previous examples of a user's likes and dislikes. Most existing recommender systems use collaborative filtering methods that base recommendations on other users' preferences. By contrast, content-based methods use information about an item itself to make suggestions. This approach has the advantage of being able to recommend previously unrated items to users with unique interests and to provide explanations for its recommendations. We describe a content-based book recommending system that utilizes information extraction and a machine-learning algorithm for text categorization. Initial experimental results demonstrate that this approach can produce accurate recommendations.

Keyphrases

text categorization content-based book recommending using learning recommender system initial experimental result existing recommender system collaborative filtering method machine-learning algorithm content-based book information extraction accurate recommendation content-based method base recommendation unrated item previous example personalized suggestion unique interest

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