Abstract
Content personalization is a long-standing problem for online news services. In most personalization approaches users are represented by topical interest profiles that are matched with news articles in order to properly decide which articles are to be recommended. When constructing user profiles, existing personalization methods exploit the user activity observed within the news service itself without incorporating information from other sources.
In this paper we study the problem of news personalization by leveraging usage information that is external to the news service. We propose a novel approach that relies on the concept of “search profiles”, which are user profiles that are built based on the past interactions of the user with a web search engine. We extensively test our proposal on real-world datasets obtained from Yahoo. We explore various dimensions and granularities at which search profiles can be built. Experimental results show that, compared to a basic strategy that does not exploit the search activity of users, our approach is able to boost the clicks on news articles shown at the top positions of a ranked result list.
An extended version of this paper appeared in [3]. Most of the work was done while all the authors were affiliated with Yahoo Labs, Barcelona, Spain.
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Notes
- 1.
Publicly available at https://webscope.sandbox.yahoo.com/catalog.php?datatype=r&did=75.
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Bai, X., Cambazoglu, B.B., Gullo, F., Mantrach, A., Silvestri, F. (2020). Improving News Personalization Through Search Logs. In: Boratto, L., Faralli, S., Marras, M., Stilo, G. (eds) Bias and Social Aspects in Search and Recommendation. BIAS 2020. Communications in Computer and Information Science, vol 1245. Springer, Cham. https://doi.org/10.1007/978-3-030-52485-2_14
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