Journal of Digital Contents Society (디지털콘텐츠학회 논문지)

Digital Contents Society (한국디지털콘텐츠학회)
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App Recommendation Based on Characteristic Similarity

특성 유사도 기반 앱 추천

  • 김형일 (나사렛대학교 멀티미디어학과)
  • Received : 2012.11.06
  • Accepted : 2012.12.14
  • Published : 2012.12.31
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Abstract

The remarkable development of IT is contributed to popularization of smart phones, which in turn creates a new domain called app store. Smartphone apps have grown fast because they can be easily purchased through an app store. As the volume of apps traded in app stores is so huge that it is extremely hard for users to find the exact app they want. In general, an app store recommends an app to users based on the search words they entered. In terms of recommendation of app, this kind of content-based method is not effective. To increase accuracy in recommending app, this paper proposes a characteristic similarity-based app recommendation method. This method creates attributes on the app based on the related information such as genre, functionality and number of downloads and then compares them with the propensity to use the app. According to diverse simulations, the method proposed in this paper improved the performance of app recommendation by 33% in average, compared to the conventional method.

정보통신의 발달로 스마트폰은 대중화를 이루었으며, 스마트폰의 대중화는 앱스토어라는 새로운 영역을 생성하였다. 스마트폰에서 사용되는 응용소프트웨어인 앱은 앱스토어를 통해 편리하게 거래될 수 있다는 장점으로 빠른 성장을 이루었다. 앱스토어에서 거래되는 앱들의 수량이 방대해짐에 따라 사용자가 원하는 앱을 정확히 추출하기란 매우 어렵다. 앱스토어에서 사용하는 일반적인 앱 추천 방식은 사용자가 입력한 질의어에 따라 앱을 추천하는 방식이다. 이러한 내용 기반 방식은 디지털 형태로 이루어진 앱을 추천할 때는 효과적인 기법이 아니다. 앱 추천의 정확성을 높이기 위해 본 논문에서는 특성 유사도 기반 앱 추천 기법을 제안한다. 본 논문에서 제안한 기법은 앱의 장르, 기능성, 다운로드 수 등을 이용하여 앱에 대한 속성을 생성한 후, 사용자의 앱 사용에 대한 성향과 비교하여 앱을 추천하는 방식을 따른다. 다양한 실험에서 본 논문에서 제안한 기법이 기본적인 앱 추출 기법보다 평균 33%의 성능 향상을 보였다.

Keywords

References

  1. T. Chen, W. Han, H. Wang, Y. Zhou, B. Xu, and B. Zang, "Content Recommendation System Based on Private Dynamic User Profile," International Conference on Machine Learning and Cybernetics, pp.2112-2118, 2007.
  2. C. Jian, Y. Jian,H. Jin, "Automatic content-based recommendation in e-commerce," The 2005 IEEE International Conference on e-Technology, e-Commerce and e-Service, pp.748-753, 2005.
  3. S. K. Ray and S. Singh, "Blog content based recommendation framework using WordNet and multiple Ontologies," 2010 International Conference on Computer Information Systems and Industrial Management Applications, pp.432-437, 2010.
  4. M. N. Uddin, J. Shrestha, and G. Jo, "Enhanced Content-Based Filtering Using Diverse Collaborative Prediction for Movie Recommendation," 2009 First Asian Conference on Intelligent Information and Database Systems, pp.132-137, 2009.
  5. T. Chen and L. He, "Collaborative Filtering Based on Demographic Attribute Vector," International Conference on Future Computer and Communication, pp.225-229, 2009.
  6. M. Pazzani, "A Framework for Collaborative, Content-Based, and Demographic Filtering," Artificial Intelligence Review, pp.393-408, 1999.
  7. L. Sebastia, I. Garcia, E. Onaindia, and C. Guzman, "e-Tourism: A Tourist Recommendation and Planning Application," International Conference on Tools with Artificial Intelligence, 2008.
  8. C. Stiller, F. Ross, and C. Ament, "Demographic recommendations for WEITBLICK, an assistance system for elderly," International Symposium on Communications and Information Technologies, pp.406-411, 2010.
  9. M. Pazzani, J. Muramatsu, and D. Billsus, "Syskill & Webert: Identifying interesting web sites," Proceedings of the 13th National Conference on Artificial Intelligence, 1996.
  10. B. Krulwich, "Lifestyle Finder: Intelligent user profiling using large-scale demographic data," Artificial Intelligence Magazine, Vol.18, No.2, 1997.
  11. D. Billsus and M. Pazzani, "Learning Collaborative Information Filters," Proceedings of the 15th International Conference on Machine Learning, 1998.
  12. U. Shardanand and P. Maes, "Social information filtering: Algorithms for automating 'word of mouth'," Proceedings of the Conference on Human Factors in Computing Systems, 1995.
  13. J. Han and M. Kamber, Data Mining Concepts and Techniques, Morgan Kaufmann, 2001.
  14. J. Z. Huang, M. K. Ng, H. Rong, and Z. Li, "Automated variable weighting in k-means type clustering," IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 27, No. 5, pp.657-668, May 2005. https://doi.org/10.1109/TPAMI.2005.95
  15. S. H. Al-Harbi, "Adapting k-means for supervised clustering," Applied Intelligence, Vol. 24, No. 3, pp. 219-226, Jun. 2006. https://doi.org/10.1007/s10489-006-8513-8
  16. P. N. Tan, M. Steinbach, and V. Kumar, Introduction to Data Mining, Addison Wesley, 2006.

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