Exploiting Synergy between Ontology and Recommender Systems Middleton, S. T., Alani, H. & De Roure D. C. (2002) Semantic Web Workship 2002 Presented by Danielle Lee

Introduction Cold-start problem : Recommender systems require an initial learning phase where behavioral information is built up to form an user profile and accordingly the learning phase performance is poor. – Self-destructive, recommender never achieves good performance since users never use it for long enough – New system cold-start problem – New user cold-start problem Interest-acquisition problem – Acquiring user’s expertise and interest and maintaining them are difficult. To investigate the synergy between recommender and ontology.

Recommendation Content-based Recommendation – With feedbacks from each user, the system tries to match recommendation with the negative or positive feedback. Collaborative Recommendation – Based on the ratings of other people, the system generates recommendation for similar users. – Initial difficulties in obtaining a sufficient number of ratings. Content-based and hybrid recommender perform a little better than collaborative in initial stage, but no recommender can cope with a totally cold-start.

Ontology Ontology about academic domain, developed by Southampton’s AKT team (Advanced Knowledge Technonolgies) – Modeling people, projects, papers, events and research interests. – Populated with automatically extracted information from departmental personnel database and publication database – 80 classes, 40 slots and over instances. This term ontology is used for the reference of the classification structure and instances within the knowledge base. They didn’t mention about the topic list ontology.

Ontology and Recommender Integration AKT Ontology QuickstepOntoCoPI User Interest Profiles User Publications Communities Of Practice User and domain knowledge Content-based Recommender Collaborative Recommender

Quickstep World Wide Web Profiles ClassifierRecommender Classified papers Users

Algorithms used in Quickstep Paper classification algorithm User profiling algorithm Recommendation algorithm – For each user, the top three interesting topics are selected with 10 recommendations made in total.

Algorithms in integration New-system initial profile algorithm New-user initial profile algorithm

Empirical Evaluation Nine users and seven weeks of browsing behavior from 3 months. All recorded profiles are compared to the benchmark week 7 profile. – To measure how quickly profiles converge to the stable state existing after a reasonable amount of behavior data has been accumulated Profile precision : how many topics were mentioned in both the current profile and benchmark profile Profile error rate : how many topics appeared in the current profile that did not appear within the benchmark profile Only the relative precision and error rate compared to the week 7 steady state profiles.

Experimental Results

Discussion and possible direction for Proactive Precision performance is good but error rate is not good enough. New user initial profile has irrelevance problem. Grouping users by their genuine properties such as major, skill, or career, collaborative recommendation can be made. Job skill pool based on ontology-based structure.