AMORE: Design & Implementation of a Commercial-Strength Parallel Hybrid Movie Recommendation Engine

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Abstract

AMORE is a hybrid recommendation system that provides movie recommenda- tion functionality to video-on-demand subscribers of a major triple-play service provider in Greece. Without any user relevance feedback for movies available, all recommendations are solely based on the users’ viewing history. To overcome such limitations as well as the extra problem of user histories that are usually the merger of the preferences of all persons in each household, we have performed extensive experiments with open-source recommendation software such as Apache Mahout and Lens-Kit, as well as with our own implementa- tions of several user-based, item-based, and content-based recommendation algorithms. Our results indicate that our own custom multi-threaded implementation of collaborative filtering combined with a custom content-based algorithm outperforms current state-of-the-art imple- mentations of similar algorithms both in solution quality and in response time by margins exceeding 100 % in terms of recall quality and 6300 % in terms of running time. The hybrid nature of the ensemble allows the system to perform well and to overcome inherent limitations of collaborative filtering, such as various cold-start problems. AMORE has been deployed in a production environment where it has contributed to an increase in the provider’s rental profits, while at the same time offers customer retention support.
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Detaljer

AMORE is a hybrid recommendation system that provides movie recommenda- tion functionality to video-on-demand subscribers of a major triple-play service provider in Greece. Without any user relevance feedback for movies available, all recommendations are solely based on the users’ viewing history. To overcome such limitations as well as the extra problem of user histories that are usually the merger of the preferences of all persons in each household, we have performed extensive experiments with open-source recommendation software such as Apache Mahout and Lens-Kit, as well as with our own implementa- tions of several user-based, item-based, and content-based recommendation algorithms. Our results indicate that our own custom multi-threaded implementation of collaborative filtering combined with a custom content-based algorithm outperforms current state-of-the-art imple- mentations of similar algorithms both in solution quality and in response time by margins exceeding 100 % in terms of recall quality and 6300 % in terms of running time. The hybrid nature of the ensemble allows the system to perform well and to overcome inherent limitations of collaborative filtering, such as various cold-start problems. AMORE has been deployed in a production environment where it has contributed to an increase in the provider’s rental profits, while at the same time offers customer retention support.
OriginalsprogEngelsk
TidsskriftKnowledge and Information Systems
Volume/Bind47
Tidsskriftsnummer3
Sider (fra-til)671-696
Antal sider26
ISSN0219-1377
DOI
StatusUdgivet - jun. 2016
PublikationsartForskning
Peer reviewJa
ID: 216773137