Combining Content with User Preferences for Non-Fiction Multimedia Recommendation: A Study on TED Lectures

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.


Published in:
Multimedia Tools and Applications, Special Issue on Content Based Multimedia Indexing, 74, 4, 1175-1197
Year:
2015
Publisher:
Dordrecht, Springer
ISSN:
1380-7501
Keywords:
Laboratories:




 Record created 2014-02-19, last modified 2018-01-28

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