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Combining content with user preferences for non-fiction multimedia recommendation: a study on TED lectures.

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Title: Combining content with user preferences for non-fiction multimedia recommendation: a study on TED lectures.
Authors: Pappas, Nikolaos1 nikolaos.pappas@idiap.ch, Popescu-Belis, Andrei2 andrei.popescu-belis@idiap.ch
Source: Multimedia Tools & Applications. Feb2015, Vol. 74 Issue 4, p1175-1197. 23p.
Subjects: TED Conference, Lectures & lecturing, Internet content, Multimedia systems, Recommender systems
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 combined recommendation methods for TED lectures and use cross-validation to select the best parameters of keyword-based (TFIDF) and semantic vector space-based 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. [ABSTRACT FROM AUTHOR]
Copyright of Multimedia Tools & Applications is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Database: Engineering Source
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  Data: <searchLink fieldCode="DE" term="%22TED+Conference%22">TED Conference</searchLink><br /><searchLink fieldCode="DE" term="%22Lectures+%26+lecturing%22">Lectures & lecturing</searchLink><br /><searchLink fieldCode="DE" term="%22Internet+content%22">Internet content</searchLink><br /><searchLink fieldCode="DE" term="%22Multimedia+systems%22">Multimedia systems</searchLink><br /><searchLink fieldCode="DE" term="%22Recommender+systems%22">Recommender systems</searchLink>
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  Data: 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 combined recommendation methods for TED lectures and use cross-validation to select the best parameters of keyword-based (TFIDF) and semantic vector space-based 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. [ABSTRACT FROM AUTHOR]
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  Data: <i>Copyright of Multimedia Tools & Applications is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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