Exploratory News Recommendation

Exploratory News Recommendation

Jon Atle Gulla (Norwegian University of Science and Technology, Norway), Özlem Özgöbek (Norwegian University of Science and Technology, Norway) and Xiaomeng Su (Norwegian University of Science and Technology, Norway)
DOI: 10.4018/978-1-5225-3686-4.ch001
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Abstract

Research on mobile news recommendation has become popular over the last few years, though the news domain is challenging and there are still few advanced commercial systems with success. This paper presents the exploratory news recommender system under development in the SmartMedia program. In exploratory news recommendation the reader can compose his own recommendation strategies on the fly and use deep semantic content analysis to extract prominent entities and navigate between relevant content at a semantic level. The readers are more likely to read a larger share of the relevant recommended articles, as there is no need to browse long tedious lists of articles or post explicit queries. The assumption is that more active and exploring readers will make implicit feedback more complete and more consistent with the readers' real interests. Tests shows a 5.14% improvement of accuracy when our collaborative filtering component is enriched with implicit feedback that combines correlations between explicit ratings with the reading times of articles viewed by readers.
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Background

The SmartMedia program at NTNU in Trondheim was established in 2012 as part of a collaboration with Norwegian telecom and media industry. With an emphasis on news recommendation and semantics, the program is investigating new and semantically deeper approaches for building real-world large-scale news recommender systems and analysis tools for data-driven journalism. The projects in SmartMedia are partly funded by industry, but have also received substantial support from Innovation Norway1 and the Research Council of Norway2.

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