Activity-based serendipitous recommendations with the Magitti mobile leisure guide


Event CHI 2008


Victoria Bellotti
Bo Begole
Chi, Ed H.
Ducheneaut, Nicolas
Fang, Ji
Isaacs, Ellen
Partridge, K
Roberts, Michael
Schiano, Diane J.
Walendowski, Alan
Technical Publications
April 5th 2008
This paper presents a context-aware mobile recommender system, codenamed Magitti. Magitti is unique in that it infers user activity from context and patterns of behavior and, without its user having to issue a query, generates matching recommendations for suitable leisure activities. Extensive field studies of leisure time practices in an urban setting (Tokyo) motivated the idea and shaped the details of its design. Magitti filters a large database of city-guide-style leisure information to find the most relevant items based on the users profile, history, context, and predicted activity. The paper describes the fieldwork, user interface, system components, and evaluation of the Magitti prototype.


Bellotti, V.; Begole, J.; Chi, E. H.; Ducheneaut, N.; Fang, J.; Isaacs, E.; King, T. H.; Newman, M.; Partridge, K.; Price, R.; Rasmussen, P.; Roberts, M.; Schiano, D. J.; Walendowski, A. Activity-based serendipitous recommendations with the Magitti mobile leisure guide. Proceedings of the 26th Annual ACM Conference on Human Factors in Computing Systems (CHI 2008); 2008 April 5-10; Florence; Italy. NY: ACM; 2008; 1157-1166.

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