@inproceedings{3f7e6b36c9644dacbd28cfcd8cbf3e81,
title = "Content-Based News Recommendation: Comparison of Time-Based System and Keyphrase-Based System",
abstract = "As internet resources are increasing at an unprecedented speed, users are tired of searching important ones among them. So, what users need and where they can find them are getting more important. Users require a personalized support in sifting through large amounts of available information according to their interests and recommendation systems try to answer this need. In this context, it is crucial to offer user friendly tools that facilitate faster and more accurate access to articles in digital newspapers. In this paper, a content-based news recommendation system for news domain is presented and contents of news articles are represented by words appearing in news articles or their keyphrases. News articles are recommended according to user dynamic and static profiles. User dynamic profiles reflect user past interests and recent interests play much bigger roles in the selection of recommendations. Our recommendation system is a complete content-based recommendation system together with categorization, summarization and news collection modules.",
keywords = "Content-based filtering, Keyphrase-based recommendation, Recommendation systems, Text classification, Text summarization",
author = "Servet Tasci and Ilyas Cicekli",
year = "2015",
doi = "10.1007/978-3-319-25840-9\_6",
language = "English",
isbn = "978-3-319-25839-3",
volume = "553",
series = "Communications In Computer And Information Science",
publisher = "Springer Nature",
pages = "84--98",
editor = "A Fred and JLG Dietz and D Aveiro and K Liu and J Filipe",
booktitle = "Knowledge Discovery, Knowledge Engineering And Knowledge Management, Ic3k 2014",
note = "6th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management (IC3K) ; Conference date: 21-10-2014 Through 24-10-2014",
}