A Context Aware Recommender System for Mobile Phone Selection Using Combination of Elimination Method and Analytic Hierarchy Processing

Iranian Journal of Information Processing & Management. 2017;32(4):1203-1228


Journal Homepage

Journal Title: Iranian Journal of Information Processing & Management

ISSN: 2251-8223 (Print); 2251-8231 (Online)

Publisher: Iranian Research Institute for Information and Technology

LCC Subject Category: Bibliography. Library science. Information resources

Country of publisher: Iran, Islamic Republic of

Language of fulltext: Persian

Full-text formats available: PDF



Jalal Rezaeenour ( University of Qom. )

Fatemeh Sadat Lesani ( University of Qom. )


Double blind peer review

Editorial Board

Instructions for authors

Time From Submission to Publication: 18 weeks


Abstract | Full Text

Recommender systems suggest proper items to customers based on their preferences and needs. Needed time to search is reduced and the quality of customer’s choice is increased using recommender systems. The context information like time, location and user behaviors can enhance the quality of recommendations and customer satisfication in such systems. In this paper a context aware recommender system is designed and implemented in android smart phones to help customers select mobile phones. The system removes ineffective criteria on user’s purcheses using customer mobile phones’ sensor data. Then creates analytic hierarchy processing tree and computes weights. Finally the recommender system recommends proper mobile phone to user. The system selects and recommends suitable phones using combination of elimination method and analytic hierarchy processing (AHP). The context aware recommender system is used by mobile phone customers to assess recomendation satisfication and user interface design satisfication. In addition a traditional non-context aware recommender system is used by users to compare the recommendation results in two different systems. The article concludes that using context information can improve the recommendation quality and user satisfication. Because of decreasing criteria and pair-wised comparisions, the user interface design satisfication improves a little too.