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NewsGator Launches Recommended Feeds Based on Attention Data

May 22 at 7:07pm by

Ever since news aggregators emerged online, they’ve been aiming to provide recommendations for the best news to read. The trend has recently resurfaced with new tools, user feedback and communities, all of which provide an ample amount of data that can be used to infer certain things about the popularity of a given news item.

NewsGator’s online RSS Reader, which includes FeedDemon and NetNewsWire under its umbrella, has teamed up with SenseArray to provide recommendations to its users. SenseArray filters through NewsGator’s feeds, and sorts them according to relevance based on the number of times users click on links, tag items, or forward articles. Direct user feedback, which can now be provided on NewsGator items with thumbs up/down voting, is being incorporated into the new SenseArray filters for recommendation purposes as well.

At first glance, it didn’t appear as though the new recommendations would be tied to the new increased support for APML (attention data), which NewsGator just released yesterday. But in corresponding with the NewsGator team, I found out that recommendations and your APML will be linked together from the very beginning. According to NewsGator, “whenever a user clips, tags, emails, or does any other action, we store a weighted score. We use that to create an attention score for the feed in APML, and we also use those scores like ratings in the collaborative filter.”

This is a good thing, as the user attention data and recommendations are quite related, and will only become more so once things get going with NewsGator’s new system. Tying recommendations into APML from the beginning will only add value to the end users in the long run. mashable109:http://mashable.com/2008/05/22/newsgator-recommendations-sensearra/

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NewsGator Launches Recommended Feeds Based on Attention Data