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Towards bridging the gap between manufacturer and users to facilitate better recommendation
Published in AAAI press
Pages: 468 - 473
The success of a recommender system lies in capturing preferences of users and recommending products that best cater to their needs. We restrict our focus to knowledge based recommender systems where we have the flexibility to model users preferences on individual features of the product. In this work, along with learning users preferences, we bring in the idea of looking at the problem of recommending from the manufacturer's point of view. We model prospective buyers of each product in the domain and use this information in predicting products that would potentially be of interest to a given user. Copyright © 2018, Association for the Advancement of Artificial Intelligence (www.aaai.org). All rights reserved.
About the journal
JournalProceedings of the 31st International Florida Artificial Intelligence Research Society Conference, FLAIRS 2018
PublisherAAAI press
Open AccessNo