Ontology-Based Recommender Systems Stuart E. Middleton, David De Roure, and Nigel R. Shadbolt2 I IT Innovation Centre, University of Southampton, Southampton SO16 7NP, UF sem@it-innovation, soton, ac uk 2 Intelligence, Agents, Multimedia Group, Department of Electronics and Computer Science, University of Southampton, Southampton SO17 1B, UK, ddergecs, soton. ac uk. nrsgecs, soton. ac uk Summary. We present an overview of the latest approaches to using ontologies in recommender systems and our work on the problem of recommending on-line academic research papers. Our two experimental systems, Quickstep and Foxtrot create user profiles from unobtrusively monitored behaviour and relevance feedback representing the profiles in terms of a research paper topic ontology. A novel profile visualization approach is taken to acquire profile feedback. Research papers are clas- sified using ontological classes and collaborative recommendation algorithms used to recommend papers seen by similar people on their current topics of interest. Onto- logical inference is shown to improve user profiling, external ontological knowledge used to successfully bootstrap a recommender system and profile visualization em- ployed to improve profiling accuracy. In a specific case study we report results from two small-scale experiments, with ubjects over 3 months, and a large-scale experiment, with 260 subjects over an academic year, are conducted to evaluate different aspects of our approach. The over- all performance of our ontological recommender systems are favourably compared to other systems in the literature 1 Introduction The mass of content available on the World-Wide Web raises important ques- tions over its effective use. Information on the web is largely unstructured with web pages authored by many people on a diverse range of topics. This often makes simple browsing too time consuming to be practical. The emer- gence of e-commerce sites means many vendors are offering potentially great deals on very similar products. Web information filtering has thus become necessary for most web users in order to find the things they really need Recommender systems have emerged as one successful approach that can help tackle the problem of information overload. They exploit patterns in item metadata and reviews posted by groups of people to find new items that might S Staab and R. Studer(eds ) Handbook on Ontologies, International Handbooks 779 on Information Systems, DOI 10.1007/978-3-540-92673-3 C Springer-Verlag Berlin Heidelberg 2009
Ontology-Based Recommender Systems Stuart E. Middleton1, David De Roure2, and Nigel R. Shadbolt2 1 IT Innovation Centre, University of Southampton, Southampton SO16 7NP, UK, sem@it-innovation.soton.ac.uk 2 Intelligence, Agents, Multimedia Group, Department of Electronics and Computer Science, University of Southampton, Southampton SO17 1BJ, UK, dder@ecs.soton.ac.uk, nrs@ecs.soton.ac.uk Summary. We present an overview of the latest approaches to using ontologies in recommender systems and our work on the problem of recommending on-line academic research papers. Our two experimental systems, Quickstep and Foxtrot, create user profiles from unobtrusively monitored behaviour and relevance feedback, representing the profiles in terms of a research paper topic ontology. A novel profile visualization approach is taken to acquire profile feedback. Research papers are classified using ontological classes and collaborative recommendation algorithms used to recommend papers seen by similar people on their current topics of interest. Ontological inference is shown to improve user profiling, external ontological knowledge used to successfully bootstrap a recommender system and profile visualization employed to improve profiling accuracy. In a specific case study we report results from two small-scale experiments, with 24 subjects over 3 months, and a large-scale experiment, with 260 subjects over an academic year, are conducted to evaluate different aspects of our approach. The overall performance of our ontological recommender systems are favourably compared to other systems in the literature. 1 Introduction The mass of content available on the World-Wide Web raises important questions over its effective use. Information on the web is largely unstructured, with web pages authored by many people on a diverse range of topics. This often makes simple browsing too time consuming to be practical. The emergence of e-commerce sites means many vendors are offering potentially great deals on very similar products. Web information filtering has thus become necessary for most web users in order to find the things they really need. Recommender systems have emerged as one successful approach that can help tackle the problem of information overload. They exploit patterns in item metadata and reviews posted by groups of people to find new items that might S. Staab and R. Studer (eds.), Handbook on Ontologies, International Handbooks 779 on Information Systems, DOI 10.1007/978-3-540-92673-3, c Springer-Verlag Berlin Heidelberg 2009
780 S.E. Middleton et al e of interest to a user Ontologies are increasingly being used within the field of recommender systems, allowing knowledge-based techniques to supplement classical machine learning and statistical approaches 1.1 Recommender Systems People find articulating exactly what they want difficult, but they are good at recognizing it when they see it. This insight has led to the utilization of relevance feedback, where people rate items as interesting or not interesting and the system tries to find items that match the"interesting", positive examples and do not match the not interesting", negative examples. With sufficient positive and negative examples, modern machine learning techniques can classify new pages with impressive accuracy. Recommender systems can recommend many types of item, including web pages, new articles, music CDs and books Unobtrusive monitoring provides positive examples of what the user is looking for, without interfering with the user's normal work activity. Heuris- tics can also be applied to infer negative examples from observed behaviour although recommender systems, which unobtrusively watch user behaviour and recom mend new items that correlate with a user's profile Another way to recommend items is based on the ratings provided by other people who have liked the item before Collaborative recommender systems do this by asking people to rate items explicitly and then recommend new items that similar users have rated highly. An issue with collaborative filtering is that there is no direct reward for providing examples since they only help other people. This leads to initial difficulties in obtaining a sufficient number of ratings for the system to be useful, a problem known as the cold-start problem 15 Hybrid systems, attempting to combine the advantages of content-based and collaborative recommender systems, have also proved popular to-date The feedback required for content-based recommendation is shared, allowing collaborative recommendation as well 1.2 User Profiling User profiling is typically either knowledge-based or behaviour-based Knowledge-based approaches use static models of users and dynamically match users to the closest model. Questionnaires and interviews are often employed to obtain this user knowledge. Once a model is selected for a user, specialist domain knowledge for that user type can be applied to help the ser. Behaviour-based approaches use the user's behaviour as a model,com- monly using machine-learning techniques to discover useful patterns in the behaviour. Behavioural logging is employed to obtain the data necessary from which to extract patterns. Kobsa 9 provides a good survey of user modellin technique
780 S.E. Middleton et al. be of interest to a user. Ontologies are increasingly being used within the field of recommender systems, allowing knowledge-based techniques to supplement classical machine learning and statistical approaches. 1.1 Recommender Systems People find articulating exactly what they want difficult, but they are good at recognizing it when they see it. This insight has led to the utilization of relevance feedback, where people rate items as interesting or not interesting and the system tries to find items that match the “interesting”, positive examples and do not match the “not interesting”, negative examples. With sufficient positive and negative examples, modern machine learning techniques can classify new pages with impressive accuracy. Recommender systems can recommend many types of item, including web pages, new articles, music CDs and books. Unobtrusive monitoring provides positive examples of what the user is looking for, without interfering with the user’s normal work activity. Heuristics can also be applied to infer negative examples from observed behaviour, although generally with less confidence. This idea has led to content-based recommender systems, which unobtrusively watch user behaviour and recommend new items that correlate with a user’s profile. Another way to recommend items is based on the ratings provided by other people who have liked the item before. Collaborative recommender systems do this by asking people to rate items explicitly and then recommend new items that similar users have rated highly. An issue with collaborative filtering is that there is no direct reward for providing examples since they only help other people. This leads to initial difficulties in obtaining a sufficient number of ratings for the system to be useful, a problem known as the cold-start problem [15]. Hybrid systems, attempting to combine the advantages of content-based and collaborative recommender systems, have also proved popular to-date. The feedback required for content-based recommendation is shared, allowing collaborative recommendation as well. 1.2 User Profiling User profiling is typically either knowledge-based or behaviour-based. Knowledge-based approaches use static models of users and dynamically match users to the closest model. Questionnaires and interviews are often employed to obtain this user knowledge. Once a model is selected for a user, specialist domain knowledge for that user type can be applied to help the user. Behaviour-based approaches use the user’s behaviour as a model, commonly using machine-learning techniques to discover useful patterns in the behaviour. Behavioural logging is employed to obtain the data necessary from which to extract patterns. Kobsa [9] provides a good survey of user modelling techniques
Ontology-Based Recommender Systems 781 profiling approach used by most recommender systems is behavioural-based, commonly using a binary class model to represent what users find interesting and not interesting. Machine-learning techniques are then used to find potential items of interest in respect to the binary model recommending items that match the positive examples and do not match the negative examples. There are a lot of effective machine learning algorithms based on two classes. a binary profile does not, however, lend itself to sharing examples of interest or integrating any domain knowledge that might be available. Sebastiani [19 provides a good survey of current machine learning An ontology is a conceptualisation of a domain into a human-understandable, but machine-readable format consisting of entities, attributes, relationships and axioms [8. 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 home phone number, or they could represent an activity such as authoring a document, or attending a conference. Part Ill contains examples of the types of ontology that are in use today, such as chapter COMM: A Core Ontology for Multimedia Annotation Ontologies help extend recommender systems to a multi-class environment allowing knowledge-based approaches to be used alongside classical machine learning algorithms. Section 2 provides an in-depth overview of how ontolo- gies are integrated into the techniques used for recommendation. Part IV of this book contains details on the current best practice for supporting infras- tructures and for ontologies, especially chapters"Ontology Repositories"and Ontology Mapping 1. 4 Chapter Structure In this chapter we show how ontologies are used in recommender systems to- day, providing an overview of the technology space and some further reading on specific approaches. We then examine in some depth a case study of two recommender systems that were among the first to adopt ontological tech- niques. In these case studies the problem domain, algorithms and results ar detailed along with a discussion that highlights some of the practical difficul ties experienced running a recommender system for real 2 Ontology Use in Recommender Systems Ontologies are now used routinely in recommender systems in combination with machine learning, statistical correlations, user profiling and domain spe- cific heuristics. Commercial recommender systems generally either maintain
Ontology-Based Recommender Systems 781 The user profiling approach used by most recommender systems is behavioural-based, commonly using a binary class model to represent what users find interesting and not interesting. Machine-learning techniques are then used to find potential items of interest in respect to the binary model, recommending items that match the positive examples and do not match the negative examples. There are a lot of effective machine learning algorithms based on two classes. A binary profile does not, however, lend itself to sharing examples of interest or integrating any domain knowledge that might be available. Sebastiani [19] provides a good survey of current machine learning techniques. 1.3 Ontologies An ontology is a conceptualisation of a domain into a human-understandable, but machine-readable format consisting of entities, attributes, relationships, and axioms [8]. 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. Part III contains examples of the types of ontology that are in use today, such as chapter “COMM: A Core Ontology for Multimedia Annotation”. Ontologies help extend recommender systems to a multi-class environment, allowing knowledge-based approaches to be used alongside classical machine learning algorithms. Section 2 provides an in-depth overview of how ontologies are integrated into the techniques used for recommendation. Part IV of this book contains details on the current best practice for supporting infrastructures and for ontologies, especially chapters “Ontology Repositories” and “Ontology Mapping”. 1.4 Chapter Structure In this chapter we show how ontologies are used in recommender systems today, providing an overview of the technology space and some further reading on specific approaches. We then examine in some depth a case study of two recommender systems that were among the first to adopt ontological techniques. In these case studies the problem domain, algorithms and results are detailed along with a discussion that highlights some of the practical difficulties experienced running a recommender system for real. 2 Ontology Use in Recommender Systems Ontologies are now used routinely in recommender systems in combination with machine learning, statistical correlations, user profiling and domain specific heuristics. Commercial recommender systems generally either maintain
782 S.E. Middleton et al simple product ontologies(e.g. books )that they can then utilize via heuristics or have a large community of users actively rating content(e. g. movies )suit- able for collaborative filtering. More research oriented recommender systems use a much wider variety of techniques that offer advantages such as improved accuracy coupled with constraints such as requiring explicit relevance feedback or intrusive monitoring of user behaviour over prolonged periods of time Recommendation of new items to users can be performed by looking at item to item similarity(content-based filtering), item reviews within a com munity of users(collaborative filtering), semantic relationships between items (heuristic-based recommendation) or a hybrid approach. In many cases the type of approach adopted will depend heavily on how much metadata is avail able about the items and how much user feedback is available, both implicit and explicit Content-based techniques work well if training data is available advance. Collaborative techniques work well when a system has a large community of users. There are, however, no definitive rules to decide on an approach and normally experience and expertise is required to pick the best a given problem doma 2.1 Content-Based Recommendation Early recommender systems used content-based binary classification ap- proaches looking at training sets of what was, and what was not interesting to a specific user. Machine learning techniques were employed to perfor supervised learning based on sets of observed training examples that a user labelled either as"good"or"bad. A classic example of a content-based rec- ommender system is Fab 1, which uses a binary class k-Nearest Neighbour classifier. Other binary class examples include personal assistant agents such as News Dude 2, using a naive Bayes classifier, and News Weeder [11,using a TF-IDF based classifier, which profile individual user interests and try to find items of interest To enhance binary classification domain ontologies were introduced alloy ing multi-class classification and hence multi-class recommendation. Typically the classes in a domain ontology, such as a product ontology defining all the products of an e-commerce website, would be used to classify the previously observed products/web pages a user had purchased / viewed. A good exam- le of multi-class recommendation is RAAP 4, which uses a simple set of categories to represent individual user profile Once a domain has been classified in terms of ontological concepts the relationships defined by the domain ontology can be used to infer interest and relevance of one concept from observed interest in another. A knowledge-based system can use expert system rules to infer probabilistic interest in classes of tem with a semantic connection to an observed item of interest. Typically the semantic distance(number of relationships away one topic is from another)is used to calculate semantic similarity, and this is used to weight likely interest Entre 3 is a restaurant recommender system that uses a knowledge
782 S.E. Middleton et al. simple product ontologies (e.g. books) that they can then utilize via heuristics or have a large community of users actively rating content (e.g. movies) suitable for collaborative filtering. More research oriented recommender systems use a much wider variety of techniques that offer advantages such as improved accuracy coupled with constraints such as requiring explicit relevance feedback or intrusive monitoring of user behaviour over prolonged periods of time. Recommendation of new items to users can be performed by looking at item to item similarity (content-based filtering), item reviews within a community of users (collaborative filtering), semantic relationships between items (heuristic-based recommendation) or a hybrid approach. In many cases the type of approach adopted will depend heavily on how much metadata is available about the items and how much user feedback is available, both implicit and explicit. Content-based techniques work well if training data is available in advance. Collaborative techniques work well when a system has a large community of users. There are, however, no definitive rules to decide on an approach and normally experience and expertise is required to pick the best approach for a given problem domain. 2.1 Content-Based Recommendation Early recommender systems used content-based binary classification approaches looking at training sets of what was, and what was not interesting to a specific user. Machine learning techniques were employed to perform supervised learning based on sets of observed training examples that a user labelled either as “good” or “bad”. A classic example of a content-based recommender system is Fab [1], which uses a binary class k-Nearest Neighbour classifier. Other binary class examples include personal assistant agents such as NewsDude [2], using a naive Bayes classifier, and NewsWeeder [11], using a TF-IDF based classifier, which profile individual user interests and try to find items of interest. To enhance binary classification domain ontologies were introduced allowing multi-class classification and hence multi-class recommendation. Typically the classes in a domain ontology, such as a product ontology defining all the products of an e-commerce website, would be used to classify the previously observed products / web pages a user had purchased / viewed. A good example of multi-class recommendation is RAAP [4], which uses a simple set of categories to represent individual user profiles. Once a domain has been classified in terms of ontological concepts the relationships defined by the domain ontology can be used to infer interest and relevance of one concept from observed interest in another. A knowledge-based system can use expert system rules to infer probabilistic interest in classes of item with a semantic connection to an observed item of interest. Typically the semantic distance (number of relationships away one topic is from another) is used to calculate semantic similarity, and this is used to weight likely interest. Entre [3] is a restaurant recommender system that uses a knowledge-base
Ontology-Based Recommender Systen and heuristic rules for recommendation. Where users articulate queries via a web interface the query criteria can drive a knowledge-based decision tree for dvanced query refinement. The CWAdvisor 5 system is an example of such an approach where a finite state model is used to refine queries for available financial service products that match the users stated requirements 2.2 Clustering and Topic Diversification Some domains do not have well identified classes of item from which content can be classified In these cases recommender systems have employed cluster ing techniques to identify within groups of items potentially similar classes. Hierarchical clustering has been used to categorize document collections for recommender algorithms [18 and sub-divides into either distance-based clus- tering or concept-based clustering Distance-based clustering [21 takes either a top-down(partitioning)or bottom-up(agglomerative) approach to building a hierarchical class tree. A distance function is defined to compute similarity between documents, often based on the similarity of frequency of the words within the document. The clustering algorithm iterates, either dividing super-clusters or merging small clusters into larger ones, until the final concept tree is formed. Concept-based clustering takes items represented as attribute-pairs and builds relationships based on the probability of occurrence of attribute-pairs within nodes. An early example of concept-b ased clustering is the COBWeb [6 algorithm. Nodes are created in a top-down approach where nodes are split or merged according to a category utility value; category utility is a measure of differentiation power of that node Often recommender systems will recommend clusters of items that are very similar, or variants of the same item(e. g. different formats of the film/DVD) To avoid this topic diversification 22 can be employed to ensure each rec- ommendation is on a well defined concept, hopefully increasing the useful- ness of a set of recommendations to the e user Algorithms to perform topic diversification will compute a dissimilarity ranking and merge this with the recommendation ranking. Semantic distance and super-class relationships can e used to compute dissimilarity between item sets 2.3 Collaborative Filtering Collaborative filtering works by using the ratings provided by a community of users to recommend items for a specific user. There are two complementary approaches available, user-based or item-based collaborative filtering. User- based collaborative filtering is where similar users are found and items recom mended that these similar users also liked. Item-based collaborative filtering is where items are grouped if people rate them similarly. In order to perform collaborative filtering a user profile must be created rom the available historical records of what items people have reviewed and
Ontology-Based Recommender Systems 783 and heuristic rules for recommendation. Where users articulate queries via a web interface the query criteria can drive a knowledge-based decision tree for advanced query refinement. The CWAdvisor [5] system is an example of such an approach where a finite state model is used to refine queries for available financial service products that match the user’s stated requirements. 2.2 Clustering and Topic Diversification Some domains do not have well identified classes of item from which content can be classified. In these cases recommender systems have employed clustering techniques to identify within groups of items potentially similar classes. Hierarchical clustering has been used to categorize document collections for recommender algorithms [18] and sub-divides into either distance-based clustering or concept-based clustering. Distance-based clustering [21] takes either a top-down (partitioning) or bottom-up (agglomerative) approach to building a hierarchical class tree. A distance function is defined to compute similarity between documents, often based on the similarity of frequency of the words within the document. The clustering algorithm iterates, either dividing super-clusters or merging small clusters into larger ones, until the final concept tree is formed. Concept-based clustering takes items represented as attribute-pairs and builds relationships based on the probability of occurrence of attribute-pairs within nodes. An early example of concept-based clustering is the COBWEB [6] algorithm. Nodes are created in a top-down approach where nodes are split or merged according to a category utility value; category utility is a measure of differentiation power of that node. Often recommender systems will recommend clusters of items that are very similar, or variants of the same item (e.g. different formats of the film/DVD). To avoid this topic diversification [22] can be employed to ensure each recommendation is on a well defined concept, hopefully increasing the usefulness of a set of recommendations to the user. Algorithms to perform topic diversification will compute a dissimilarity ranking and merge this with the recommendation ranking. Semantic distance and super-class relationships can be used to compute dissimilarity between item sets. 2.3 Collaborative Filtering Collaborative filtering works by using the ratings provided by a community of users to recommend items for a specific user. There are two complementary approaches available, user-based or item-based collaborative filtering. Userbased collaborative filtering is where similar users are found and items recommended that these similar users also liked. Item-based collaborative filtering is where items are grouped if people rate them similarly. In order to perform collaborative filtering a user profile must be created from the available historical records of what items people have reviewed and