Abstract
Online news portals constantly produce a huge amount of content about different events and topics. In such data streams scenarios, delivering relevant recommendations that best suit each user’s interests is a challenging task. Indeed, tight-time constraints and highly dynamic conditions in these environments make traditional batch recommendation approaches ineffective. In this paper, we present a scalable news recommendation system that takes into account data semantics, trending topics, users’ behaviors and the usage context in order to (1) model news articles, (2) infer users’ preferences and (3) provide real-time suggestions. In fact, our proposal is based on the semantic analysis of news articles’ content in order to extract relevant keywords and referenced named entities. This information is then used to model users’ interests by analyzing their attitudes while interacting with the available content. Moreover, our proposition accounts for the temporal variance of a news article’s utility by considering its freshness, popularity and attractiveness. To prove our proposition’s quality, scalability and efficiency in real-time data streaming environments, it was evaluated during the CLEF-NEWSREEL challenge connecting recommender systems to an active large-scale news delivery platform. Experiment results show that our system produces high quality and reliable performances in such dynamic environments.
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Ficel, H., Haddad, M.R., Baazaoui Zghal, H. (2018). Large-Scale Real-Time News Recommendation Based on Semantic Data Analysis and Users’ Implicit and Explicit Behaviors. In: Benczúr, A., Thalheim, B., Horváth, T. (eds) Advances in Databases and Information Systems. ADBIS 2018. Lecture Notes in Computer Science(), vol 11019. Springer, Cham. https://doi.org/10.1007/978-3-319-98398-1_17
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DOI: https://doi.org/10.1007/978-3-319-98398-1_17
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