Exploiting Surface Features for the Prediction of Podcast Preference
Identifieur interne : 000573 ( Main/Exploration ); précédent : 000572; suivant : 000574Exploiting Surface Features for the Prediction of Podcast Preference
Auteurs : Manos Tsagkias [Pays-Bas] ; Martha Larson [Pays-Bas] ; Maarten De Rijke [Pays-Bas]Source :
- Lecture Notes in Computer Science [ 0302-9743 ] ; 2009.
English descriptors
- Teeft :
- 2month enclosure duration, Automatic podcast preference prediction, Bayes, Best performance, Certain podcasts, Copyright feed authors count feed periodicity feed period less1week level, Credibility, Data mining, Descr, Descr length episode title, Description feed descr length feed categories count feed keywords count feed, Enclosure, Enclosure enclosure count, Enclosures enclosure, Encoding, Episode, Episode episode descr ratio episode, Extractable features, Failure analysis, Features encoding properties, Feed feed, Feed number, Feed periodicity, Further experimentation, Future work, Ground truth, Human analysis, Indicator, Information retrieval, Itunes, Link2page episode count episode authors count level, Logo, Logo feed logo linkback feed, Logo links, Metadata, Naive bayes, Nominal integer, Podcast, Podcast content, Podcast context, Podcast episode length, Podcast portal, Podcast preference, Podcaster, Podcasts, Podcred, Podcred framework, Popularity bars, Preference, Preference indicators, Preference prediction, Preferred podcasts, Random baseline, Randomforest, Ranking experiments, Ranking podcasts, Reciprocal rank, Retrieval, Rijke, Rijke table, Single feature, Solid performance, Speech speed, Support vector machine, Surface features, Technical execution, Topical categories, Tsagkias, Unique authors, User, User preference, User ratings.
Abstract
Abstract: Podcasts display an unevenness characteristic of domains dominated by user generated content, resulting in potentially radical variation of the user preference they enjoy. We report on work that uses easily extractable surface features of podcasts in order to achieve solid performance on two podcast preference prediction tasks: classification of preferred vs. non-preferred podcasts and ranking podcasts by level of preference. We identify features with good discriminative potential by carrying out manual data analysis, resulting in a refinement of the indicators of an existent podcast preference framework. Our preference prediction is useful for topic-independent ranking of podcasts, and can be used to support download suggestion or collection browsing.
Url:
DOI: 10.1007/978-3-642-00958-7_42
Affiliations:
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Rijke</name><affiliation wicri:level="4"><country xml:lang="fr">Pays-Bas</country><wicri:regionArea>ISLA, University of Amsterdam, Kruislaan 403, 1098 SJ, Amsterdam</wicri:regionArea><placeName><settlement type="city">Amsterdam</settlement><region nuts="2" type="province">Hollande-Septentrionale</region></placeName><orgName type="university">Université d'Amsterdam</orgName></affiliation><affiliation wicri:level="1"><country wicri:rule="url">Pays-Bas</country></affiliation></author></analytic><monogr></monogr><series><title level="s">Lecture Notes in Computer Science</title><imprint><date>2009</date></imprint><idno type="ISSN">0302-9743</idno><idno type="eISSN">1611-3349</idno><idno type="ISSN">0302-9743</idno></series></biblStruct></sourceDesc><seriesStmt><idno type="ISSN">0302-9743</idno></seriesStmt></fileDesc><profileDesc><textClass><keywords scheme="Teeft" xml:lang="en"><term>2month enclosure duration</term><term>Automatic podcast preference prediction</term><term>Bayes</term><term>Best performance</term><term>Certain podcasts</term><term>Copyright feed authors count feed periodicity feed period less1week level</term><term>Credibility</term><term>Data mining</term><term>Descr</term><term>Descr length episode title</term><term>Description feed descr length feed categories count feed keywords count feed</term><term>Enclosure</term><term>Enclosure enclosure count</term><term>Enclosures enclosure</term><term>Encoding</term><term>Episode</term><term>Episode episode descr ratio episode</term><term>Extractable features</term><term>Failure analysis</term><term>Features encoding properties</term><term>Feed feed</term><term>Feed number</term><term>Feed periodicity</term><term>Further experimentation</term><term>Future work</term><term>Ground truth</term><term>Human analysis</term><term>Indicator</term><term>Information retrieval</term><term>Itunes</term><term>Link2page episode count episode authors count level</term><term>Logo</term><term>Logo feed logo linkback feed</term><term>Logo links</term><term>Metadata</term><term>Naive bayes</term><term>Nominal integer</term><term>Podcast</term><term>Podcast content</term><term>Podcast context</term><term>Podcast episode length</term><term>Podcast portal</term><term>Podcast preference</term><term>Podcaster</term><term>Podcasts</term><term>Podcred</term><term>Podcred framework</term><term>Popularity bars</term><term>Preference</term><term>Preference indicators</term><term>Preference prediction</term><term>Preferred podcasts</term><term>Random baseline</term><term>Randomforest</term><term>Ranking experiments</term><term>Ranking podcasts</term><term>Reciprocal rank</term><term>Retrieval</term><term>Rijke</term><term>Rijke table</term><term>Single feature</term><term>Solid performance</term><term>Speech speed</term><term>Support vector machine</term><term>Surface features</term><term>Technical execution</term><term>Topical categories</term><term>Tsagkias</term><term>Unique authors</term><term>User</term><term>User preference</term><term>User ratings</term></keywords></textClass><langUsage><language ident="en">en</language></langUsage></profileDesc></teiHeader><front><div type="abstract" xml:lang="en">Abstract: Podcasts display an unevenness characteristic of domains dominated by user generated content, resulting in potentially radical variation of the user preference they enjoy. We report on work that uses easily extractable surface features of podcasts in order to achieve solid performance on two podcast preference prediction tasks: classification of preferred vs. non-preferred podcasts and ranking podcasts by level of preference. We identify features with good discriminative potential by carrying out manual data analysis, resulting in a refinement of the indicators of an existent podcast preference framework. Our preference prediction is useful for topic-independent ranking of podcasts, and can be used to support download suggestion or collection browsing.</div></front></TEI><affiliations><list><country><li>Pays-Bas</li></country><region><li>Hollande-Septentrionale</li></region><settlement><li>Amsterdam</li></settlement><orgName><li>Université d'Amsterdam</li></orgName></list><tree><country name="Pays-Bas"><region name="Hollande-Septentrionale"><name sortKey="Tsagkias, Manos" sort="Tsagkias, Manos" uniqKey="Tsagkias M" first="Manos" last="Tsagkias">Manos Tsagkias</name></region><name sortKey="De Rijke, Maarten" sort="De Rijke, Maarten" uniqKey="De Rijke M" first="Maarten" last="De Rijke">Maarten De Rijke</name><name sortKey="De Rijke, Maarten" sort="De Rijke, Maarten" uniqKey="De Rijke M" first="Maarten" last="De Rijke">Maarten De Rijke</name><name sortKey="Larson, Martha" sort="Larson, Martha" uniqKey="Larson M" first="Martha" last="Larson">Martha Larson</name><name sortKey="Larson, Martha" sort="Larson, Martha" uniqKey="Larson M" first="Martha" last="Larson">Martha Larson</name><name sortKey="Tsagkias, Manos" sort="Tsagkias, Manos" uniqKey="Tsagkias M" first="Manos" last="Tsagkias">Manos Tsagkias</name></country></tree></affiliations></record>Pour manipuler ce document sous Unix (Dilib)
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