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Combining Content with User Preferences for Non-Fiction Multimedia Recommendation: A Study on TED Lectures
Type of publication: Journal paper
Citation: Pappas_MTAP_2014
Publication status: Published
Journal: Multimedia Tools and Applications, Special Issue on Content Based Multimedia Indexing
Volume: 74
Number: 4
Year: 2015
Month: February
Pages: 1175-1197
ISSN: 1380-7501; 1573-7721
DOI: 10.1007/s11042-013-1840-y
Abstract: This paper introduces a new dataset and compares several methods for the recommendation of non-fiction audio visual material, namely lectures from the TED website. The TED dataset contains 1,149 talks and 69,023 profiles of users, who have made more than 100,000 ratings and 200,000 comments. The corresponding metadata, which we make available, can be used for training and testing generic or personalized recommender systems. We define content-based, collaborative, and methods (LSI, LDA, RP, and ESA). We compare these methods on a personalized recommendation task in two settings, a cold-start and a non-cold-start one. In the cold-start setting, semantic vector spaces perform better than keywords. In the non-cold-start setting, where collaborative information can be exploited, content-based methods are outperformed by collaborative filtering ones, but the proposed combined method shows acceptable performances, and can be used in both settings. For the generic recommendation task, LSI and RP again outperform TF-IDF.
Keywords: Collaborative Filtering
Projects Idiap
Authors Pappas, Nikolaos
Popescu-Belis, Andrei
Added by: [UNK]
Total mark: 0
  • Pappas_MTAP_2014.pdf