A proactive recommender system pushes recommendations to the user when the current situation seems appropriate, without explicit user request. This is conceivable in mobile scenarios such as restaurant or gas station recommendations. In this paper, we present a model for proactivity in mobile recommender systems. The model relies on domain-dependent context modeling in several categories. The recommendation process is divided into two phases to first analyze the current situation and then examine the suitability of particular Ítems. We have implemented a prototype gas station recommender and conducted a survey for evaluation. Results showed good correlation of the output of our system with the assessment of users regarding the question when to genérate recommendations.

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People complete tasks more quickly when they have concrete plans. However, they often fail to create such action plans. (How) can systems provide these concrete steps automatically? This article demonstrates that these benefits can also be realized when these plans are created by others or reused from similar tasks. Four experiments test these approaches, finding that people indeed complete more tasks when they receive externally-created action plans. To automatically provide plans, we introduce the Genies workflow that combines benefits of crowd wisdom, collaborative refinement, and automation. We demonstrate and evaluate this approach through the TaskGenies system, and introduce an NLP similarity algorithm for reusing plans. We demonstrate that it is possible for people to create action plans for others, and we show that it can be cost effective.