Activity-based serendipitous recommendations with the Magitti mobile leisure guide

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 user behavior and, without its user having to issue a query, automatically generates recommendations for content matching. Extensive field studies of leisure time practices in an urban setting (Tokyo) motivated the idea, shaped the details of its design and provided data describing typical behavior patterns. The paper describes the fieldwork, user interface, system components and functionality, and an evaluation of the Magitti prototype. Author Keywords Field studies, user experience design, interaction, contextaware computing, mobile recommendation systems, leisure. ACM Classification Keywords H5.m. Information interfaces and presentation (e.g., HCI): Miscellaneous.

• Type: Conference
• Year: 2008
• Where: CHI
• Authors: Victoria Bellotti, James Bo Begole, Ed Huai-hsin Chi, Nicolas Ducheneaut, Ji Fang, Ellen Isaacs, Tracy H. King, Mark W. Newman, Kurt Partridge, Bob Price, Paul Rasmussen, Michael Roberts, Diane J. Schiano, Alan Walendowski
• Keywords: CHI 2008, Context-aware Mobile Recommender, Human Computer Interaction, Keywords Field Studies, User Behavior