PAWS meeting - Sep 8, 2009
The second meeting of the Fall 2009 semester is devoted to discussing selected papers from the UMAP 2009 conference. The two papers presented are focusing on mobile recommender systems.
Paper 1. PBohnert, F. and Zukerman, I. (2009). Non-intrusive personalisation of the museum experience. In Houben, G.-J., McCalla, G. I., Pianesi, F., and Zancanaro, M., (Eds.), 17th International Conference on User Modeling, Adaptation, and Personalization (UMAP 2009), pp. 307–318, Trento, Italy.
Authors utilize a special hand-operated tool - Geckotracker to obtain tracking data from visitors of Melbourne Museum. In particular exhibits of interest and viewing times are collected. The data is then analyzed in order to build a prediction model, capable of recommending new unvisited exhibits to see. Actual log viewing times are used as a primary measure of user interest. Several competing models are built. Leave one out method is used to estimate models' performance.
Paper 2. Partridge, K. and Price, B. (2009). Enhancing mobile recommender systems with activity inference. In Houben, G.-J., McCalla, G. I., Pianesi, F., and Zancanaro, M., (Eds.), 17th International Conference on User Modeling, Adaptation, and Personalization (UMAP 2009), pp. 307–318, Trento, Italy.
Paper focuses on mobile activity recommender system Magitti. Magitti recommends 5 classes of activities: eat, shop, do, see, and read. The data used for building several alternative recommender models was provided by the Japan Statistics Bureau and has data from 10 000 people reporting their activity every 15 min during one whole day. Recommender models take into account several factors, including location, surrounding venues, time of the day, personal calendar, etc. Magitti has gone though a small scale evaluation by 11 researchers and administrative staff users. Results show that a combination of location-based and personal-activity-pattern models works best.
Also discussed:
- UMUAI Special Issue on Educational Data Mining
- UMAP 2009 Proceedings
Paper 1. PBohnert, F. and Zukerman, I. (2009). Non-intrusive personalisation of the museum experience. In Houben, G.-J., McCalla, G. I., Pianesi, F., and Zancanaro, M., (Eds.), 17th International Conference on User Modeling, Adaptation, and Personalization (UMAP 2009), pp. 307–318, Trento, Italy.
Authors utilize a special hand-operated tool - Geckotracker to obtain tracking data from visitors of Melbourne Museum. In particular exhibits of interest and viewing times are collected. The data is then analyzed in order to build a prediction model, capable of recommending new unvisited exhibits to see. Actual log viewing times are used as a primary measure of user interest. Several competing models are built. Leave one out method is used to estimate models' performance.
Paper 2. Partridge, K. and Price, B. (2009). Enhancing mobile recommender systems with activity inference. In Houben, G.-J., McCalla, G. I., Pianesi, F., and Zancanaro, M., (Eds.), 17th International Conference on User Modeling, Adaptation, and Personalization (UMAP 2009), pp. 307–318, Trento, Italy.
Paper focuses on mobile activity recommender system Magitti. Magitti recommends 5 classes of activities: eat, shop, do, see, and read. The data used for building several alternative recommender models was provided by the Japan Statistics Bureau and has data from 10 000 people reporting their activity every 15 min during one whole day. Recommender models take into account several factors, including location, surrounding venues, time of the day, personal calendar, etc. Magitti has gone though a small scale evaluation by 11 researchers and administrative staff users. Results show that a combination of location-based and personal-activity-pattern models works best.
Also discussed:
- UMUAI Special Issue on Educational Data Mining
- UMAP 2009 Proceedings
posted by Methodius at 11:57 AM
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