https://recsyswiki.com/api.php?action=feedcontributions&feedformat=atom&user=Torbenbrodt 2026-04-21T12:43:37Z User contributions MediaWiki 1.34.2 https://recsyswiki.com/index.php?title=Plista&diff=2013 2013-10-12T07:40:20Z

<p>Torbenbrodt: </p> <hr /> <div>plista is a data-driven content and advertising platform for editorial content and videos as well as performance, native- and branding-driven advertising.<br /> <br /> Website: http://plista.com<br /> <br /> = Research =<br /> plista likes the academic world. We often host or sponsor events like the Recommender Systems Meetup in Berlin, we publish papers, we have cooperations with universities, we invite any researcher to work with our recommenders using the Open Recommendation Platform (ORP). <br /> <br /> = Focus =<br /> * context: we try to find either the best article or the best algorithm specific to the context<br /> * real-time: the focus on streaming algorithms rather than using batch<br /> * generalization: we build either article recommendations, advertising recommendations or meta-learning recommendation on the same platform<br /> * mixing: there will not the one algorithm, we believe in blending a multitude of algorithms</div>

Torbenbrodt https://recsyswiki.com/index.php?title=Plista&diff=2012 2013-10-12T07:39:34Z

<p>Torbenbrodt: Created page with &quot;plista is a data-driven content and advertising platform for editorial content and videos as well as performance, native- and branding-driven advertising. = Research = plista...&quot;</p> <hr /> <div>plista is a data-driven content and advertising platform for editorial content and videos as well as performance, native- and branding-driven advertising.<br /> <br /> = Research =<br /> plista likes the academic world. We often host or sponsor events like the Recommender Systems Meetup in Berlin, we publish papers, we have cooperations with universities, we invite any researcher to work with our recommenders using the Open Recommendation Platform (ORP). <br /> <br /> = Focus =<br /> * context: we try to find either the best article or the best algorithm specific to the context<br /> * real-time: the focus on streaming algorithms rather than using batch<br /> * generalization: we build either article recommendations, advertising recommendations or meta-learning recommendation on the same platform<br /> * mixing: there will not the one algorithm, we believe in blending a multitude of algorithms</div>

Torbenbrodt https://recsyswiki.com/index.php?title=News_recommendation&diff=2011 2013-10-12T01:53:26Z

<p>Torbenbrodt: Created page with &quot;Nowadays more and more people read their news online rather than on traditional print media. With theincreasing importance of online newsportals, we can also observe an increa...&quot;</p> <hr /> <div>Nowadays more and more people read their news online rather than on traditional print media. With theincreasing importance of online newsportals, we can also observe an increasing need for personalised news services that recommend those articles that are relevant to the users’ information need.<br /> <br /> == Dataset ==<br /> Although research on adaptive news retrieval and recommendation has been performed for many years, most research has been focused on rather small datasets or on datasets which have been designed for a different purpose, casting doubt on the scalability of these approaches.<br /> Find more information about the plista dataset in paper &quot;The plista Dataset&quot;.<br /> <br /> == Open Recommendation Platform (ORP) ==<br /> If you want to try your news recommender algorithms on a real news platform, read about the ORP, which is provided by plista to allow researchers to give recommendations to real publishers and real users. More information at http://orp.plista.com</div>

Torbenbrodt