منابع مشابه
Active Learning in Recommender Systems
Recommender Systems (RSs) are often assumed to present items to users for one reason – to recommend items a user will likely be interested in. Of course RSs do recommend, but this assumption is biased, with no help of the title, towards the “recommending” the system will do. There is another reason for presenting an item to the user: to learn more about their preferences, or their likes and dis...
متن کاملActive Learning in Collaborative Filtering Recommender Systems
In Collaborative Filtering Recommender Systems user’s preferences are expressed in terms of rated items and each rating allows to improve system prediction accuracy. However, not all of the ratings bring the same amount of information about the user’s tastes. Active Learning aims at identifying rating data that better reflects users’ preferences. Active learning Strategies are used to selective...
متن کاملActive Learning for Recommender Systems with Multiple Localized Models
For effective predictive modeling in large scale recommender systems, it is essential to have many customers rate a large number of products, i.e., obtain a large number of labeled data. However, most consumers often do not provide their preferences without proper incentives. Given a budget to reward consumers for their feedback, it would be beneficial to have a policy to suggest the ratings of...
متن کاملComparing Prediction Models for Active Learning in Recommender Systems
Recommender systems help web users to address information overload. Their performance, however, depends on the amount of information that users provide about their preferences. Users are not willing to provide information for a large amount of items, thus the quality of recommendations is affected. Active learning for recommender systems has been proposed in the past, to acquire preference info...
متن کاملImproved Questionnaire Trees for Active Learning in Recommender Systems
A key challenge in recommender systems is how to profile new-users. This problem is called cold-start problem or new-user problem. A well-known solution for this problem is to use active learning techniques and ask new users to rate a few items in order to reveal their preferences. Recently, questionnaire trees (tree structures) have been proposed to build such adaptive questionnaires. In this ...
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ژورنال
عنوان ژورنال: KI - Künstliche Intelligenz
سال: 2014
ISSN: 0933-1875,1610-1987
DOI: 10.1007/s13218-014-0323-2