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The role of smartphone in recommender systems: opportunities and challenges

The role of smartphone in recommender systems: opportunities and challenges

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The popularity of smartphones in people's daily life brings new opportunities as well as challenges in recommender system. New opportunities include new available context data, e.g., user interaction time (usually from native mobile app) and geo-location data (from equipped GPS sensors). These metainformation provides different ways of inferring user preference, which ultimately improves the recommendation performance. For instance, with record of tap-in and tap-out timestamp, the dwell time can be estimated. It thus provides an opportunity to address the “silent viewing” issue by inferring people's implicit rating, which will benefit conventional recommender systems that suffer from rating-sparsity. At the meantime, new challenges are mainly in two-fold. First, such side information is not included in conventional recommendation model, and thus it is not easy for integration. Also, recommendation services via smartphones is itself a scenario different from traditional PC-based one, which leads to “pitfalls” where existing techniques may fail. Particularly, we focus on two representative recommendation scenarios in smartphones, i.e., app and point-of-interest (POI) recommendation. For the former one, conventional model may recommend apps that users would never download due to the ignorance of potential conflict between candidate apps and installed ones. To recommend POI, failure of modeling physical location may lead to candidates that are too far away. In this chapter, we reveal these issues and describe corresponding solutions.

Chapter Contents:

  • 20.1 Introduction
  • 20.2 Silence is also evidence: interpret dwell time
  • 20.2.1 Modeling the silence behavior
  • 20.2.2 Modeling the dwell time
  • 20.2.3 Model inference and application
  • 20.3 App recommendation: contest between temptation and satisfaction
  • 20.3.1 Failure of recommendation
  • 20.3.2 Modeling the contest—actual-tempting model
  • 20.3.3 Insights of the model
  • 20.4 POI recommendation: geographical, social and temporal
  • 20.4.1 Geographical influence
  • 20.4.2 Social influence
  • 20.4.3 Temporal influence
  • 20.5 Conclusion
  • References

Inspec keywords: mobile computing; recommender systems; smart phones

Other keywords: physical location modeling; smartphones; candidate apps; opportunity; point-of-interest recommendation; conventional model; user preference; POI recommendation; silent viewing issue; representative recommendation scenarios; conventional recommendation model; GPS sensors; recommendation services; native mobile app; geo-location data; conventional recommender systems; rating-sparsity; user interaction time; recommendation performance

Subjects: Information networks; Ubiquitous and pervasive computing

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