Title: Finding similar movies: dataset, tools, and methods
Authors: Leng, Hongkun
De La Cruz Paulino, Caleb
Haider, Momina
Lu, Rui
Zhou, Zhehui
Mengshoel, Ole
Brodin, Per-Erik
Forgeat, Julien
Jude, Alvin
Citation: WSCG '2018: short communications proceedings: The 26th International Conference in Central Europe on Computer Graphics, Visualization and Computer Vision 2016 in co-operation with EUROGRAPHICS: University of West Bohemia, Plzen, Czech Republic May 28 - June 1 2018, p. 115-124.
Issue Date: 2018
Publisher: Václav Skala - UNION Agency
Document type: konferenční příspěvek
URI: wscg.zcu.cz/WSCG2018/!!_CSRN-2802.pdf
ISBN: 978-80-86943-41-1
ISSN: 2464-4617
Keywords: doporučující systémy;podobnost položek;crowdsourcing;učení pod dohledem;MovieLens
Keywords in different language: recommender systems;item-item similarity;crowdsourcing;supervised learning;MovieLens
Abstract: Recommender systems are becoming ubiquitous in online commerce as well as in video-on-demand (VOD) and music streaming services. A popular form of giving recommendations is to base them on a currently selected product (or items), and provide “More Like This,” “Items Similar to This,” or “People Who Bought This also Bought” functionality. These recommendations are based on similarity computations, also known as item-item similarity computations. Such computations are typically implemented by heuristic algorithms, which may not match the perceived item-item similarity of users. In contrast, we study in this paper a data-driven approach to similarity for movies using labels crowdsourced from a previous work. Specifically, we develop four similarity methods and investigate how user-contributed labels can be used to improve similarity computations to better match user perceptions in movie recommendations. These four methods were tested against the best known method with a user experiment (n = 114) using the MovieLens 20M dataset. Our experiment showed that all our supervised methods beat the unsupervised benchmark and the differences were both statistically and practically significant. This paper’s main contributions include user evaluation of similarity methods for movies, user-contributed labels indicating movie similarities, and code for the annotation tool which can be found at http://MovieSim.org.
Rights: © Václav Skala - UNION Agency
Appears in Collections:WSCG '2018: Short Papers Proceedings

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