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Activity-Based Serendipitous Recommendations with the Magitti Mobile Leisure guide

Activity-Based Serendipitous Recommendations with the Magitti Mobile Leisure guide. PARC @ CHI 2008. Conceptualizing Magitti. Using context to infer current and future leisure activities and recommends content about suitable venues. They conducted six types of studies in Tokyo.

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Activity-Based Serendipitous Recommendations with the Magitti Mobile Leisure guide

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  1. Activity-Based Serendipitous Recommendations with the Magitti Mobile Leisure guide PARC @ CHI 2008

  2. Conceptualizing Magitti • Using context to infer current and future leisure activities and recommends content about suitable venues. • They conducted six types of studies in Tokyo. • Design Requirement: • Relaxation, Serendipity, and Spontaneity • Avoidance of Information Overload • Minimize Size • One-handed Operation

  3. System • Activity Prediction Mode (based on demographic patterns and individual pattern): • Predict users are in any of these modes • Eat, buy, see, do, read • Recommender (based on collaborative filtering, preference, distance…etc) • Content Repository • Data Context Detection

  4. User Interface • Every time lists 20 recommendation • Big buttons, making menus • Users can ask for recommendation in specific category • Eat • Buy • See • Do • Read

  5. Field Evaluation • 11 volunteers in the Palo Alto. • Supporting Serendipity • 53% they visited were new, including 38% that they had never heard of. • Predicting User Activity • People still change to Eat (1.8 times), Buy (1.4 times), Do(0.7 times), See(0.5 times), and Read (0.1 times) modes. • Context-Aware Recommendation • Relevant and of interest (3.8/5.0) • Omission, distance, first item, guide, transparency issues • Usability • User Control (they want to sort things by own factors) • Social Use

  6. PROS AND CONS • Pros: • Supporting “serendipitous”, “activity-based” discovery • Simple interface and one-hand operation • Classify likely future activity mode • Cons: • Use Japanese patterns to predict Palo Alto users • Need more data to train the system understanding users’ preference • Five explicit activity modes seem not enough. • “See” or “Do” are vague.

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