Ontology-Based News Recommendation Wouter Intema Frank Goossen wouterijntema@gmail.com frankgoossen@gmail.com Flavius frasincar Frederik Hogenboom frasincar @ese. eur. nl hogeboom@ese. eur. nl Erasmus University Rotterdam PO Box 1738. NL-3000 Rotterdam the Netherlands ABSTRACT platform to find them. However, these news items are not ecommending news items is traditionally done by term. personalized for one's interests. In this paper we propose based algorithms like TF-IDF. This paper concentrates on an approach based on rich semantics for delivering the most the benefits of recommending news items using a domain nteresting news items to the user ontology instead of using a term-based approach. For this Recommending news items can be done by calculating the rpose, we propose Athena, which is an extension to the similarity between the current news item and the previously xisting Hermes framework. Athena employs a user profile browsed news items. Traditionally, this similarity is calcu- o store terms or concepts found in news items browsed by lated by an algorithm that is content-based, which practi- the user. Based on this information. the framework uses a cally means that every word in a news item is taken into traditional method based on TF-IDF, and several ontology account. However, a news item often contains key concepts based methods to recommend new articles to the user. The that capture the semantic context of the article. Recom- paper concludes with the evaluation of the different meth menders that focus on the key concepts might produce faster ods, which shows that the new ontology-based method that and more accurate recommendations than the content-based we propose in this paper performs better(wrt racy, recommenders, since they don't need to consider all words, and recall) than the traditional method with and unlike words, concepts are not ambiguous. Such an ap- tion of one measure(recall), also better the proach is called a semantic-based recommendation system other considered ontology-based approaches Other recommendation systems are either collaborative or hybrid, and are outside the scope of this paper Categories and Subject Descriptors In 7, we introduced the Hermes framework, which pro- vides a semantic method for personalizing news items. It H 3.3 Information Storage and Retrieval]: Information uses an ontology to store concepts and their relations to Search and Retrieval-Information filtering, Relevance feed- the news items. Our paper focuses on a new way of rec- back: 1.2.4 [Artificial Intelligence): Knowledge Represen- ommending, based on concepts found in the news items, by tation Formalisms and Methods-Representation languages employing some of the functionalities offered by Hermes. In order to recommend news items. first we model the General terms user's browsing behavior. By recording a history of read news items, a profile of the user can be made. Based on Design, Experimentation this profile, it is possible to propose new news items that the user might find interesting. The goal of our research is to investigate the benefit of recommending news items Recommender systems, User profiling, Ontology by using domain ontology-based recommenders with respect to traditional term-based recommenders, and to determine 1. INTRODUCTION which of the ontology-based recommenders performs best In this paper we propose Athena, which is an extension In the last decade. the Web has become increasingly im- of the Hermes framework. Athena is able to observe user portant in delivering news to individuals. Many people read behavior and generate recommendations based on this be- news articles for different purposes and the Web is the best havior. The program uses a traditional term-based recom- mender and several semantic-based recommendation algo- rithms to compare unread news items with the user profile The news items having the highest similarity with the user Permission to make digital or hard copies of all or part of profile are recommended to the user. use is granted without fee pre The structure of this paper is as follows. First, we discuss bear this notice and the full citation on the first page. To copy the related work in Sect. 2. Section 3 presents the Athena republish, to post on servers or to redistribute to lists, requires prior specific framework, the Hermes framework, and the Hermes News Portal(HNP), which is the implementation of the Hermes Copyright2010ACM978-1-60558-945-9/100003.51000 ramework. After that, Sect. 4 describes the implementationOntology-Based News Recommendation Wouter IJntema wouterijntema@gmail.com Flavius Frasincar frasincar@ese.eur.nl Frank Goossen frankgoossen@gmail.com Frederik Hogenboom fhogenboom@ese.eur.nl Erasmus University Rotterdam PO Box 1738, NL-3000 Rotterdam, the Netherlands ABSTRACT Recommending news items is traditionally done by term￾based algorithms like TF-IDF. This paper concentrates on the benefits of recommending news items using a domain ontology instead of using a term-based approach. For this purpose, we propose Athena, which is an extension to the existing Hermes framework. Athena employs a user profile to store terms or concepts found in news items browsed by the user. Based on this information, the framework uses a traditional method based on TF-IDF, and several ontology￾based methods to recommend new articles to the user. The paper concludes with the evaluation of the different meth￾ods, which shows that the new ontology-based method that we propose in this paper performs better (w.r.t. accuracy, precision, and recall) than the traditional method and, with the exception of one measure (recall), also better than the other considered ontology-based approaches. Categories and Subject Descriptors H.3.3 [Information Storage and Retrieval]: Information Search and Retrieval—Information filtering, Relevance feed￾back; I.2.4 [Artificial Intelligence]: Knowledge Represen￾tation Formalisms and Methods—Representation languages General Terms Design, Experimentation Keywords Recommender systems, User profiling, Ontology 1. INTRODUCTION In the last decade, the Web has become increasingly im￾portant in delivering news to individuals. Many people read news articles for different purposes and the Web is the best Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. To copy otherwise, to republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. EDBT 2010, March 22–26, 2010, Lausanne, Switzerland. Copyright 2010 ACM 978-1-60558-945-9/10/0003 ...$10.00 platform to find them. However, these news items are not personalized for one’s interests. In this paper we propose an approach based on rich semantics for delivering the most interesting news items to the user. Recommending news items can be done by calculating the similarity between the current news item and the previously browsed news items. Traditionally, this similarity is calcu￾lated by an algorithm that is content-based, which practi￾cally means that every word in a news item is taken into account. However, a news item often contains key concepts that capture the semantic context of the article. Recom￾menders that focus on the key concepts might produce faster and more accurate recommendations than the content-based recommenders, since they don’t need to consider all words, and unlike words, concepts are not ambiguous. Such an ap￾proach is called a semantic-based recommendation system. Other recommendation systems are either collaborative or hybrid, and are outside the scope of this paper. In [7], we introduced the Hermes framework, which pro￾vides a semantic method for personalizing news items. It uses an ontology to store concepts and their relations to the news items. Our paper focuses on a new way of rec￾ommending, based on concepts found in the news items, by employing some of the functionalities offered by Hermes. In order to recommend news items, first we model the user’s browsing behavior. By recording a history of read news items, a profile of the user can be made. Based on this profile, it is possible to propose new news items that the user might find interesting. The goal of our research is to investigate the benefit of recommending news items by using domain ontology-based recommenders with respect to traditional term-based recommenders, and to determine which of the ontology-based recommenders performs best. In this paper we propose Athena, which is an extension of the Hermes framework. Athena is able to observe user behavior and generate recommendations based on this be￾havior. The program uses a traditional term-based recom￾mender and several semantic-based recommendation algo￾rithms to compare unread news items with the user profile. The news items having the highest similarity with the user profile are recommended to the user. The structure of this paper is as follows. First, we discuss the related work in Sect. 2. Section 3 presents the Athena framework, the Hermes framework, and the Hermes News Portal (HNP), which is the implementation of the Hermes framework. After that, Sect. 4 describes the implementation