Be utilized one approach is to use unsupervised

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Unformatted text preview: ised learning over unrated book descriptions to improve supervised learning from a smaller number of rated examples. A successful method for doing this in text categorization is presented in 24 . Another approach is active learning, in which examples are ac2 Amazon has already made signi cantly more income from the rst author based on recommendations provided by Libra than those provided by its own recommender system; however, this is hardly a rigorous, unbiased comparison. quired incrementally and the system attempts to use what it has already learned to limit training by selecting only the most informative new examples for the user to rate 9 . Speci c techniques for applying this to text categorization have been developed and shown to signi cantly reduce the quantity of labeled examples required 18, 19 . A slightly di erent approach is to advise users on easy and productive strategies for selecting good training examples themselves. We have found that one e ective approach is to rst provide a small number of highly rated examples which are presumably easy for users to generate, running the system to generate initial recommendations, reviewing the top recommendations for obviously bad items, providing low ratings for these examples, and retraining the system to obtain new recommendations. We intend to conduct experiments on the existing data sets evaluating such strategies for selecting training examples. Studying additional ways of combining content-based and collaborative recommending is particularly important. The use of collaborative content in Libra was found to be useful, and if signi cant data bases of both user ratings and item content are available, both of these sources of information could contribute to better recommendations 3, 4 . One additional approach is to automatically add the related books of each rated book as additional training examples with the same or similar rating, thereby using collaborative information to expand the training examples available for content-based recommending. A list of additional topics for investigation include the following. Allowing a user to initially provide keywords that are of known interest or disinterest, and incorporating this information into learned pro les by biasing the parameter estimates for these words 25 . Comparing di erent text-categorization algorithms: In addition to more sophisticated Bayesian methods, neuralnetwork and case-based methods could be explored. Combining content extracted from multiple sources: For example, combining information about a title from Amazon, BarnesAndNoble, on-line library catalogs, etc. Using full-text as content: Utilizing complete on-line text for a book, instead of abstracted summaries and reviews, as the content description. 5 CONCLUSIONS Unlike collaborative ltering, content-based recommending holds the promise of being able to e ectively recommend unrated items and to provide quality recommendations to users with unique, individual tastes. Libra...
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