A New Ontology-Based User Modeling Method for Personalized recommendation Jiangling Yuan, Hui Zhang, Jiangfeng Ni State Key laboratory of Software Development Environment Beihang University, School of Computer Science 100191, Beijing, China Galunnier,hzhang,nijf@nlsde.buaa.edu.cn Abstrack-Personalized recommendation is an effective method semantic information, so these models can't accurately to resolve the current problem of Internet information describe the users' interests [4-6]. Ontology is used to depict overload. In the recommendation systems, user modeling is a the domain knowledge, provides the common understanding ucial step. Whether the model can accurately describe the of the knowledge about one area, defines the common sers' interests directly determines the quality of the cognitive vocabulary, and gives the clear definition on personalized recommendations. At present in most different domain terms. This paper presents a new ontology personalized service systems keywords models or user-item based user modeling method which uses ontology concept odels are used to describe the users' preferences, but vectors hierarchy tree to represent the users'interests, and we use the formation, so it is difficult to accurately model the users' reasoning and extension technique of the ontology to mine interests and hobbies, and it is also hard to extend the users, the users' potential interests. Experiment results show that interests. Ontology as a tool used to describe the domai this method can more accurately describe the users'interest knowledge is very powerful in conceptual describing and In the recommendation systems similarity measure plays gical reasoning. Computation of the neighbor set of users or an important role, which is the base procedure for finding out esources is also an important step in the recommendation, but the neighbor set of users or resources. At present three at present three commonly used similarity algorithms have commonly used similarity algorithms are: cosine-based some shortcomings which lead the system sometimes difficulty similarity, correlation-based similarity and adjusted-cosine to find similar users or resources. This paper presents a new similarity [7-9]. In this paper, we briefly mention the tology-based user modeling approach and an improved inherent drawbacks of the above three similarity algorithms similarity algorithm. Our experiments show that the user and present an improved similarity algorithm, which can model presented in r can effectively describe the effectively overcome these drawbacks users’ personalized p es, and we also prove that the The rest of the paper is organized as follows. In section 2, improved similarity is better than other three domain ontology building approach is proposed In section 3, commonly used similarity algorithms we show the ontology-based user modeling method. Section 4 presents an improved similarity algorithm called Simi-New Keywords-personalized recommendation; ontology; semantic Experimental results are provided in section 5. Section easoning; user modeling; similarity measure states the conclusion of this paper. . INTRODUCTION IL. DOMAIN ONTOLOGY CONSTRUCTION AND DATA The explosive growth in the information available on the EPROCESSING Web In this section we use the owL Web personalization systems that understand and exploit user developed by W3C to build the domain ontology. This preferences to dynamically serve customized content to language can define the ontology structure, name space, individual users [1]. The method that how to build the user basic elements(classes, individuals, properties)and ontology model determines the model whether can accurately describe mapping relationships. We define all kinds of attributes and a the users'real interests and the system whether can variety of property relationships between the ontology recommend the right items to users, so user modeling has concepts. In the paper, we take the rock and mineral fossils become the key step in the personalized recommendation domain for example. We make use of automatic construction 2-3]. At present Most of the personalized and hand-built components to build this domain ontology. recommendation systems use keywords vectors or user- Firstly, we use the codes of rock and mineral fossils resource matrix to represent the users'interests. However, resources to build the meta-data classification hierarchy tree. with the increase of users and resources in system, the scale Secondly, we use the meta-data classification hierarchy tree of vectors or matrixes will tremendously grow, which drops to build the ontology concept hierarchy tree. At last we add the efficiency of the system. As we all know there are the properties to concept nodes by hand. Figure 1 shows part emantic relationships between the resources visited by users, of the rock and mineral fossils meta-data classification but some commonly used models havent taken advantage of hierarchy tree these semantic relationships, some simply make use of the 978-142445540-9/10s2600@2010IEEEA New Ontology-Based User Modeling Method for Personalized Recommendation Jiangling Yuan, Hui Zhang, Jiangfeng Ni State Key Laboratory of Software Development Environment Beihang University, School of Computer Science 100191, Beij ing, China {jalunnier, hzhang, nijf}@nlsde.buaa.edu.cn Abstract-Personalized recommendation is an effective method to resolve the current problem of Internet information overload. In the recommendation systems, user modeling is a crucial step. Whether the model can accurately describe the users' interests directly determines the quality of the personalized recommendations. At present in most personalized service systems keywords models or user-item models are used to describe the users' preferences, but vectors or matrixes used in these models do not contain semantic information, so it is difficult to accurately model the users' interests and hobbies, and it is also hard to extend the users' interests. Ontology as a tool used to describe the domain knowledge is very powerful in conceptual describing and logical reasoning. Computation of the neighbor set of users or resources is also an important step in the recommendation, but at present three commonly used similarity algorithms have some shortcomings which lead the system sometimes difficulty to find similar users or resources. This paper presents a new ontology-based user modeling approach and an improved similarity algorithm. Our experiments show that the user model presented in this paper can effectively describe the users' personalized preferences, and we also prove that the improved similarity algorithm is better than other three commonly used similarity algorithms. Keywords-personalized recommendation; ontology; semantic reasoning; user modeling; similarity measure 1. INTRODUCTION The explosive growth in the information available on the Web has prompted the need for developing Web personalization systems that understand and exploit user preferences to dynamically serve customized content to individual users [1]. The method that how to build the user model determines the model whether can accurately describe the users' real interests and the system whether can recommend the right items to users, so user modeling has become the key step in the personalized recommendation systems [2-3]. At present Most of the personalized recommendation systems use keywords vectors or user￾resource matrix to represent the users' interests. However, with the increase of users and resources in system, the scale of vectors or matrixes will tremendously grow, which drops the efficiency of the system. As we all know there are semantic relationships between the resources visited by users, but some commonly used models haven't taken advantage of these semantic relationships, some simply make use of the 978-1-4244-5540-9/10/$26.00 ©2010 IEEE 363 semantic information, so these models can't accurately describe the users' interests [4-6]. Ontology is used to depict the domain knowledge, provides the common understanding of the knowledge about one area, defines the common cognitive vocabulary, and gives the clear definition on different domain terms. This paper presents a new ontology￾based user modeling method which uses ontology concept hierarchy tree to represent the users' interests, and we use the reasoning and extension technique of the ontology to mine the users' potential interests. Experiment results show that this method can more accurately describe the users' interests. In the recommendation systems similarity measure plays an important role, which is the base procedure for finding out the neighbor set of users or resources. At present three commonly used similarity algorithms are: cosine-based similarity, correlation-based similarity and adjusted-cosine similarity [7-9]. In this paper, we briefly mention the inherent drawbacks of the above three similarity algorithms and present an improved similarity algorithm, which can effectively overcome these drawbacks. The rest of the paper is organized as follows. In section 2, domain ontology building approach is proposed. In section 3, we show the ontology-based user modeling method. Section 4 presents an improved similarity algorithm called Simi-New. Experimental results are provided in section 5. Section 6 states the conclusion of this paper. II. DOMAIN ONTOLOGY CONSTRUCTION AND DATA PREPROCESSING In this section we use the OWL Web Ontology Language developed by W3C to build the domain ontology. This language can define the ontology structure, name space, basic elements (classes, individuals, properties) and ontology mapping relationships. We define all kinds of attributes and a variety of property relationships between the ontology concepts. In the paper, we take the rock and mineral fossils domain for example. We make use of automatic construction and hand-built components to build this domain ontology. Firstly, we use the codes of rock and mineral fossils resources to build the meta-data classification hierarchy tree. Secondly, we use the meta-data classification hierarchy tree to build the ontology concept hierarchy tree. At last we add the properties to concept nodes by hand. Figure 1 shows part of the rock and mineral fossils meta-data classification hierarchy tree