784 S.E. Middleton et al rated. Often a 5-point scale is used for ratings (very good to very bad) A common user profile representation is a weighted vector if interest with as many dimensions as the domain has classes. Vectors can also be used for item to item similarity. Domains where item metadata is not accessi- ble as ontological terms will usually apply pre-processing techniques to com- pute word/document/metadata term frequencies, remove common words and merge similar words using a thesaurus like WordNet User-based collaborative filtering is the most popular recommendation al- gorithm due to its simplicity and excellent quality of recommendation. First neighbourhoods are formed using a similarity metric, such as a statistical correlation metric like Pearson-r correlation. Second a set of rating predic tions are created using profiles that are within the same neighbourhood as the users own profile. Recommendations are created from the top-M The GroupLens project [10 is an early exploiter of user-based collaborative filtering Item-based collaborative filtering has become popular in the last 5 years ince it decouples the model computation from the prediction process; Amazon have used this technique successfully. Just as in user-based similarity items are compared on the basis of how many users rank them similarly. The neighbourhoods computed are therefore collections of items that are similar. This technique scales well since new items will be added to neighbourhoods s users rate them without the need for explicit ontology maintenance. Sometimes a recommender system will have to compare items from differ ent domain ontologies, such as two product lists. In these cases an ontology can be created for both domains in a common language(such as OWL)and the mapping between them formulated, either manually or using a automated technique [12 such as a Bayesian belief network. Once concepts are success- fully mapped the normal approaches for recommendation can be applied 2.4 Use of the Semantic Web and Web 2.0 Approaches Recent work has also used some of the emerging Web 2.0 resources from the Semantic Web to help identify classes of item. One such system [20 has used an internet movie database that contains extensive information about actors movies, etc, and mapped this semantic information to user behaviour on a movie recommendation website. Tag clouds are created based on the keyword frequencies behind the items they have rated. Data mining techniques [ can also be coupled with ontological knowledge to improve similarity matching 3 Case Study: Two Ontological Recommender Systems For a case study two experimental recommender syste nted, Quic step and Foxtrot, that explored the novel idea of using an ontological approach
784 S.E. Middleton et al. rated. Often a 5-point scale is used for ratings (very good to very bad). A common user profile representation is a weighted vector if interest with as many dimensions as the domain has classes. Vectors can also be used for item to item similarity. Domains where item metadata is not accessible as ontological terms will usually apply pre-processing techniques to compute word/document/metadata term frequencies, remove common words and merge similar words using a thesaurus like WordNet. User-based collaborative filtering is the most popular recommendation algorithm due to its simplicity and excellent quality of recommendation. First neighbourhoods are formed using a similarity metric, such as a statistical correlation metric like Pearson-r correlation. Second a set of rating predictions are created using profiles that are within the same neighbourhood as the user’s own profile. Recommendations are created from the top-N items. The GroupLens project [10] is an early exploiter of user-based collaborative filtering. Item-based collaborative filtering has become popular in the last 5 years since it decouples the model computation from the prediction process; Amazon [13] have used this technique successfully. Just as in user-based similarity items are compared on the basis of how many users rank them similarly. The neighbourhoods computed are therefore collections of items that are similar. This technique scales well since new items will be added to neighbourhoods as users rate them without the need for explicit ontology maintenance. Sometimes a recommender system will have to compare items from different domain ontologies, such as two product lists. In these cases an ontology can be created for both domains in a common language (such as OWL) and the mapping between them formulated, either manually or using a automated technique [12] such as a Bayesian belief network. Once concepts are successfully mapped the normal approaches for recommendation can be applied. 2.4 Use of the Semantic Web and Web 2.0 Approaches Recent work has also used some of the emerging Web 2.0 resources from the Semantic Web to help identify classes of item. One such system [20] has used an internet movie database that contains extensive information about actors, movies, etc., and mapped this semantic information to user behaviour on a movie recommendation website. Tag clouds are created based on the keyword frequencies behind the items they have rated. Data mining techniques [4] can also be coupled with ontological knowledge to improve similarity matching and recommendation within historical usage data. 3 Case Study: Two Ontological Recommender Systems For a case study two experimental recommender systems are presented, Quickstep and Foxtrot, that explored the novel idea of using an ontological approach to user profiling in the context of recommender systems. Representing user
Ontology-Based Recommender Systems 785 interests in ontological terms involves losing some of the fine grained informa- ion held in the raw examples of interest, but in turn allows inference to assist user profiling, communication with other external ontologies and visualization of the profiles using ontological terms understandable to users. Figure l shows the general approach taken by both our recommender systems. Quickstep im- plements only the basic recommendation interface, while Foxtrot implements ll the shown features A research paper topic ontology is shared between all system processes, al- lowing both classifications and user profiles to use a common terminology. The ontology itself contains is-a relationships between appropriate topic classes; a ction from the topic ontology is shown in Fig. 2. The Quickstep ontology was Proler Database Fig. 1. Quickstep and Foxtrot recommender system data flow Artificial Intelligen face Agents Multi-Agent-Systems owledge Representahon atural Language Processing Rule Learning Vision Pattern Recogition Theory (machine leaming Human-Computer Interaction permed .. Data Structures Algorthms& Theory..Graphics& Virtual Reality Operating Syster tecture Fig. 2. Section from the Foxtrot research paper topic ontology
Ontology-Based Recommender Systems 785 interests in ontological terms involves losing some of the fine grained information held in the raw examples of interest, but in turn allows inference to assist user profiling, communication with other external ontologies and visualization of the profiles using ontological terms understandable to users. Figure 1 shows the general approach taken by both our recommender systems. Quickstep implements only the basic recommendation interface, while Foxtrot implements all the shown features. A research paper topic ontology is shared between all system processes, allowing both classifications and user profiles to use a common terminology. The ontology itself contains is-a relationships between appropriate topic classes; a section from the topic ontology is shown in Fig. 2. The Quickstep ontology was Fig. 1. Quickstep and Foxtrot recommender system data flow Fig. 2. Section from the Foxtrot research paper topic ontology