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A Personalised Reader for Crowd Curated Content

A Personalised Reader for Crowd Curated Content
A Personalised Reader for Crowd Curated Content
Personalised news recommender systems traditionally rely on content ingested from a select set of publishers and ask users to indicate their interests from a predefined list of top- ics. They then provide users a feed of news items for each of their topics. In this demo, we present a mobile app that automatically learns users’ interests from their browsing or twitter history and provides them with a personalised feed of diverse, crowd curated content. The app also continuously learns from the users’ interactions as they swipe to like or skip items recommended to them. In addition, users can discover trending stories and content liked by other users they follow. The crowd is thus formed of the users, who as a whole act as the curators of the content to be recommended.
Kazai, Gabriella
4ab50aae-13d7-4634-8c2a-2ae276276ae8
Clarke, Daoud
03f7404f-05c1-43bd-906a-ea536e16f73e
Yusof, Iskander
7a83778c-f9a7-4c77-a633-fa67e2123b2d
Venanzi, Matteo
ba24a77f-31a6-4c05-a647-babf8f660440
Kazai, Gabriella
4ab50aae-13d7-4634-8c2a-2ae276276ae8
Clarke, Daoud
03f7404f-05c1-43bd-906a-ea536e16f73e
Yusof, Iskander
7a83778c-f9a7-4c77-a633-fa67e2123b2d
Venanzi, Matteo
ba24a77f-31a6-4c05-a647-babf8f660440

Kazai, Gabriella, Clarke, Daoud, Yusof, Iskander and Venanzi, Matteo (2015) A Personalised Reader for Crowd Curated Content. The 9th ACM Conference on Recommender Systems (RecSys 2015). (doi:10.1145/2792838.2796552).

Record type: Conference or Workshop Item (Paper)

Abstract

Personalised news recommender systems traditionally rely on content ingested from a select set of publishers and ask users to indicate their interests from a predefined list of top- ics. They then provide users a feed of news items for each of their topics. In this demo, we present a mobile app that automatically learns users’ interests from their browsing or twitter history and provides them with a personalised feed of diverse, crowd curated content. The app also continuously learns from the users’ interactions as they swipe to like or skip items recommended to them. In addition, users can discover trending stories and content liked by other users they follow. The crowd is thus formed of the users, who as a whole act as the curators of the content to be recommended.

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Published date: 2015
Venue - Dates: The 9th ACM Conference on Recommender Systems (RecSys 2015), 2015-01-01
Organisations: Agents, Interactions & Complexity

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Local EPrints ID: 380762
URI: http://eprints.soton.ac.uk/id/eprint/380762
PURE UUID: 682d2c5a-bc7b-437a-87d8-c26e5c784d2a

Catalogue record

Date deposited: 20 Aug 2015 09:31
Last modified: 30 Sep 2019 18:48

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