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Speeding up the Recommender Systems by Excluding the Low Rated Items

Authors:
Yousef Kilani

Keywords: collaborative filtering; recommender systems; speed; low rated items

Abstract:
Recommender systems become an important tool to help users in searching for their favorite items in many real life applications. A Collaborative Filtering is a commonly used technique in RS. In order to recommend items to the active user (the user we want to make recommendation for), collaborative filtering-based RS uses similar users to the active user and/or latent factor techniques. In our project, we show that excluding the items I that have not been rated high by any user speeds up the recommendation process and gives better accuracy, precision, and recall. The recommender systems recommend the items that have been rated high by the similar users to the active user. Therefore, no item from I that has been rated high by the similar users and hence will not be recommended to the active user. As far as we know, there is no any similar work in the literature.

Pages: 24 to 27

Copyright: Copyright (c) IARIA, 2018

Publication date: November 18, 2018

Published in: conference

ISSN: 2308-4499

ISBN: 978-1-61208-677-4

Location: Athens, Greece

Dates: from November 18, 2018 to November 22, 2018