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Interpretable recommendation via attraction modeling: Learning multilevel attractiveness over multimodal movie contents
Authors
L Cao
Q Chen
L Hu
S Jian
Publication date
1 January 2018
Publisher
Abstract
© 2018 International Joint Conferences on Artificial Intelligence. All right reserved. New contents like blogs and online videos are produced in every second in the new media age. We argue that attraction is one of the decisive factors for user selection of new contents. However, collaborative filtering cannot work without user feedback; and the existing content-based recommender systems are ineligible to capture and interpret the attractive points on new contents. Accordingly, we propose attraction modeling to learn and interpret user attractiveness. Specially, we build a multilevel attraction model (MLAM) over the content features-the story (textual data) and cast members (categorical data) of movies. In particular, we design multilevel personal filters to calculate users' attractiveness on words, sentences and cast members at different levels. The experimental results show the superiority of MLAM over the state-of-the-art methods. In addition, a case study is provided to demonstrate the interpretability of MLAM by visualizing user attractiveness on a movie
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OPUS - University of Technology Sydney
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Last time updated on 18/10/2019