A case study on the effectiveness of recommendations in the mobile internet

ABSTRACT

This paper summarizes the initial findings of an experimental evaluation of how recommender systems affect the buying behavior of online customers. The study was conducted in the context of a large-scale, commercial Mobile Internet platform, from which end users can download games to their mobile phones. Item recommendations were presented to platform visitors in different navigational situations; the recommendation lists were either determined with the help of different recommendation algorithms or based on nonpersonalized ranking techniques. The study is based on a sample of more than 155,000 different customers who visited the portal during a four week evaluation period. The analysis revealed that the use of personalized recommendations instead of non-personalized ones leads to a significant increase in viewed and sold items in different navigational situations and to an overall sales increase.

REFERENCES

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INDEX TERMS

Primary Classification:
  H. Information Systems
  H.3 INFORMATION STORAGE AND RETRIEVAL
      H.3.3 Information Search and Retrieval
          Subjects: Selection process

Additional Classification:
  H. Information Systems
  H.4 INFORMATION SYSTEMS APPLICATIONS
      H.4.2 Types of Systems
          Subjects: Decision support (e.g., MIS)

General Terms:
Algorithms, Experimentation, Measurement

Keywords:
case study, collaborative filtering, recommender systems