Kirman, Ben (2012) “Get lost, GetLostBot!” annoying people by offering recommendations when they are not wanted. Workshop on the Personalising the Local Mobile Experience at ACM Recommender Systems 2012 . ISSN UNSPECIFIED

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Abstract

This brief paper outlines the experience of releasing a purposefully contrary recommendation service for Foursquare called GetLostBot. GetLostBot works differently than most recommender systems since it is responsive to behaviours rather than user requests. The system automatically monitors Foursquare behaviour and intervenes with suggestions when users fall into a routine. These interventions take the form of mysterious walking directions that challenge the user to visit somewhere new. Importantly, these suggestions are explicitly not informed by traditional metrics such as popularity, high ratings, or friend activity, and instead act as prompts to explore unknown places. This paper discusses the reception to the application, highlighting the apparent disconnect between users’ good intentions around becoming more serendipitous, and the reality of those interventions as they are experienced in the wild.

Item Type:	Article
Additional Information:	This brief paper outlines the experience of releasing a purposefully contrary recommendation service for Foursquare called GetLostBot. GetLostBot works differently than most recommender systems since it is responsive to behaviours rather than user requests. The system automatically monitors Foursquare behaviour and intervenes with suggestions when users fall into a routine. These interventions take the form of mysterious walking directions that challenge the user to visit somewhere new. Importantly, these suggestions are explicitly not informed by traditional metrics such as popularity, high ratings, or friend activity, and instead act as prompts to explore unknown places. This paper discusses the reception to the application, highlighting the apparent disconnect between users’ good intentions around becoming more serendipitous, and the reality of those interventions as they are experienced in the wild.
Keywords:	serendipity, foursquare, location-sharing, recommendation, recommender systems, intervention, bmjtype
Subjects:	G Mathematical and Computer Sciences > G440 Human-computer Interaction
Divisions:	College of Sciences > Faculty of Science > Lincoln School of Computer Science
Depositing User:	Ben Kirman
Date Deposited:	15 Sep 2012 14:52
Last Modified:	13 Mar 2013 09:13
URI:	http://eprints.lincoln.ac.uk/id/eprint/6153

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