Hybrid systems, attempting to combine the advantages of content- team (Advanced Knowledge Technologies (200). It models ased and collaborative recommender systems, have proved people, projects, papers, events and research interests. The pular to-date. The feedback required for content-based ontology itself is implemented in Protege 2000 [101, a graphical commendation is shared, allowing collaborative tool for developing knowledge-based systems. It is populated with ecommendation as well. We use the Quickstep [18 hybrid information extracted automatically from a departmental papers. is focused on people, projects, and publications 2.1 The cold-start problem One difficult problem commonly faced by recommender systems 3.1 The Interest-acquisition Problem the cold-start problem [17) where recommendations are Peoples areas of expertise and interests are an important type of quired for new items or users for whom little or no information knowledge for many applications, for example expert finders [ 9] has yet been acquired. Poor performance resulting from a cold Semantic web technology can be a good source of such start can deter user uptake of a recommender system. This effect is thus self-destructive, since the recommender never achieves good the web pages up-to-date. The majority of web pages receive little performance since users never use it for long enough. We will maintenance, holding information that does not date quickly examine two types of cold-start problem Since interests and areas of expertise are dynamic in nature they The new-system cold-start problem is where there are are not often held within web pages. It is thus particularly difficult ratings by users, and hence no profiles of users. In this for an ontology to acquire such information; this is the interest- most recommender systems have no basis Is sItuation acquisition problem. recommend, and hence perform very poorly Many existing systems force users to perform self-assessment to The new-user cold-start problem is where the system s exist, but gather such information, but this has numerous disadvantages [ 5] running for a while and a set of user profiles and ratings Lotus have developed a system that monitors user interaction with no information is available about a new user. Most recon a document to capture interests and expertise [16]. Their system systems perform poorly in this situation too does not, however, consider the online documents that users Collaborative recommender systems fail to help in cold-star ituations, as they cannot discover similar user behaviour because This paper investigates linking an ontology with a recommender there is not enough previously logged behaviour data upon whic system to help overcoming the interest acquisition problem. The to base any correlations. Content-based and hybrid recommender recommender system will regularly provide the ontology with systems perform a little better since they need just a few examp nterest profiles for users, obtained by monitoring user web of user interest in order to find similar items ysing feedback on recommended papers No recommender system can cope alone with a totally cold-start however, since even content-based recommenders require a small 4. Related work number of examples on which to base recommendations. We ropose to link together a recommender system and an ontology recommend items liked by similar people. PHOAKS [26 is an to address this problem. The ontology can provide a variety of example of a collaborative filtering, recommending web links information on users and their publications. Publications provide mentioned in newsgroups articles. Only newsgroups with at least mportant information about what interests a user has had in the 20 posted web links are considered by PHOAKs, avoiding the ast, so provide a basis upon which to create initial profiles that can address the new-system cold start problem. Personnel records cold-start problems associated with newer newsgroups containing allow similar users to be identified. This will address the new-user less messages. Group Lens [14 is an alternative example, recommending newsgroup articles. Group Lens reports two cold old-start problem by providing a set of similar users on which to start problems in their experimental analysis. Users abandoned the base a new-user profile. system before they had provided enough ratings to receive commendations and early adopters of the system received poor 3. ONTOLOGIES recommendations until enough ratings were gathered. These An ontology is a conceptualisation of a domain into a human- systems are typical of collaborative recommenders, where a cold- nderstandable, but machine-readable format consisting of start makes early recommendation poor until sufficient people ships, and axioms [12]. Ontologies can provide a rich conceptualisation of the working domain of an Content-based recommender systems recommend items with organisation, representing the main concepts and relationships of similar content to things the user has liked before. An example of information such as an employees home phone number, or they could represent an activity such as authoring a document,or pages. Fab needs a few early ratings from each user in order to recommender, recommending funding information from a In this paper we use the term ontology to refer to the classification database. elfI observes users using a database and infers both tructure and instances within the knowledge base positive and negative examples of interest from this behaviour The ontology used in our work is designed to represent the oth these systems are typical of content-based recommender academic domain, and was developed by Sou on's akt systems, requiring users to use the system for an initial period of time before the cold-start problem is overcomeHybrid systems, attempting to combine the advantages of content￾based and collaborative recommender systems, have proved popular to-date. The feedback required for content-based recommendation is shared, allowing collaborative recommendation as well. We use the Quickstep [18] hybrid recommender system in this paper to recommend on-line research papers. 2.1 The Cold-start Problem One difficult problem commonly faced by recommender systems is the cold-start problem [17], where recommendations are required for new items or users for whom little or no information has yet been acquired. Poor performance resulting from a cold￾start can deter user uptake of a recommender system. This effect is thus self-destructive, since the recommender never achieves good performance since users never use it for long enough. We will examine two types of cold-start problem. The new-system cold-start problem is where there are no initial ratings by users, and hence no profiles of users. In this situation most recommender systems have no basis on which to recommend, and hence perform very poorly. The new-user cold-start problem is where the system has been running for a while and a set of user profiles and ratings exist, but no information is available about a new user. Most recommender systems perform poorly in this situation too. Collaborative recommender systems fail to help in cold-start situations, as they cannot discover similar user behaviour because there is not enough previously logged behaviour data upon which to base any correlations. Content-based and hybrid recommender systems perform a little better since they need just a few examples of user interest in order to find similar items. No recommender system can cope alone with a totally cold-start however, since even content-based recommenders require a small number of examples on which to base recommendations. We propose to link together a recommender system and an ontology to address this problem. The ontology can provide a variety of information on users and their publications. Publications provide important information about what interests a user has had in the past, so provide a basis upon which to create initial profiles that can address the new-system cold start problem. Personnel records allow similar users to be identified. This will address the new-user cold-start problem by providing a set of similar users on which to base a new-user profile. 3. ONTOLOGIES An ontology is a conceptualisation of a domain into a human￾understandable, but machine-readable format consisting of entities, attributes, relationships, and axioms [12]. Ontologies can provide a rich conceptualisation of the working domain of an organisation, representing the main concepts and relationships of the work activities. These relationships could represent isolated information such as an employee’s home phone number, or they could represent an activity such as authoring a document, or attending a conference. In this paper we use the term ontology to refer to the classification structure and instances within the knowledge base. The ontology used in our work is designed to represent the academic domain, and was developed by Southampton’s AKT team (Advanced Knowledge Technologies [20]). It models people, projects, papers, events and research interests. The ontology itself is implemented in Protégé 2000 [10], a graphical tool for developing knowledge-based systems. It is populated with information extracted automatically from a departmental personnel database and publication database. The ontology consists of around 80 classes, 40 slots, over 13000 instances and is focused on people, projects, and publications. 3.1 The Interest-acquisition Problem People’s areas of expertise and interests are an important type of knowledge for many applications, for example expert finders [9]. Semantic web technology can be a good source of such information, but usually requires substantial maintenance to keep the web pages up-to-date. The majority of web pages receive little maintenance, holding information that does not date quickly. Since interests and areas of expertise are dynamic in nature they are not often held within web pages. It is thus particularly difficult for an ontology to acquire such information; this is the interest￾acquisition problem. Many existing systems force users to perform self-assessment to gather such information, but this has numerous disadvantages [5]. Lotus have developed a system that monitors user interaction with a document to capture interests and expertise [16]. Their system does not, however, consider the online documents that users browse. This paper investigates linking an ontology with a recommender system to help overcoming the interest acquisition problem. The recommender system will regularly provide the ontology with interest profiles for users, obtained by monitoring user web browsing and analysing feedback on recommended research papers. 4. Related Work Collaborative recommender systems utilize user ratings to recommend items liked by similar people. PHOAKS [26] is an example of a collaborative filtering, recommending web links mentioned in newsgroups articles. Only newsgroups with at least 20 posted web links are considered by PHOAKS, avoiding the cold-start problems associated with newer newsgroups containing less messages. Group Lens [14] is an alternative example, recommending newsgroup articles. Group Lens reports two cold￾start problems in their experimental analysis. Users abandoned the system before they had provided enough ratings to receive recommendations and early adopters of the system received poor recommendations until enough ratings were gathered. These systems are typical of collaborative recommenders, where a cold￾start makes early recommendation poor until sufficient people have provided ratings. Content-based recommender systems recommend items with similar content to things the user has liked before. An example of a content-based recommender is Fab [4], which recommends web pages. Fab needs a few early ratings from each user in order to create a training set. ELFI [25] is another content-based recommender, recommending funding information from a database. ELFI observes users using a database and infers both positive and negative examples of interest from this behaviour. Both these systems are typical of content-based recommender systems, requiring users to use the system for an initial period of time before the cold-start problem is overcome