When Recommendation Meets Mobile: Contextual and Personalized Recommendation On The Go

Jinfeng Zhuang, Tao Mei, Steven C. H. Hoi, Ying-Qing Xu, and Shipeng Li


Mobile devices are becoming ubiquitous. People use their phones as a personal concierge discovering and making decisions anywhere and anytime. Understanding user intent on the go therefore becomes important for task completion on the phone. While existing efforts have predominantly focused on understanding the explicit user intent expressed by a textual or voice query, this paper presents an approach to context-aware and personalized entity recommendation which understands the implicit intent without any explicit user input on the phone. The approach, highly motivated from a large-scale mobile click-through analysis, is able to rank both the entity types and the entities within each type (here an entity is a local business, e.g., "I love sushi,” while an entity type is a category, e.g., "restaurant”). The recommended entity types and entities are relevant to both user context (past behaviors) and sensor context (time and geo-location). Specifically, it estimates the generation probability of an entity by a given user conditioned on the current context in a probabilistic framework. A random-walk propagation is then employed to refine the estimated probability by mining the temporal patterns among entities. We deploy a recommendation application based on the proposed approach on Window Phone 7 devices. We evaluate recommendation performance on 10 thousand mobile clicks, as

well as user experience through subjective user studies. We show that the application is effective to facilitate the exploration and discovery of surroundings for mobile users.


Publication typeInproceedings
Published inProceedings of ACM Ubicomp
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