Advertisement

Collaborative Filtering in an Offline Setting Case Study: Indonesia Retail Business

  • Hamid Dimyati
  • Ramdisa Agasi
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 845)

Abstract

In the past decade, most modeling efforts to date have been focused on the application of recommender systems in an online setting. However, only a few studies have exclusively addressed the actual challenges that arise from implementing it in an offline system. Although the principles of recommender systems implementation between the online and offline commerce are almost identical to some extent, applying the algorithm in the offline environment has its own unique challenges such as lack of product rating and description. Furthermore, most of the customers in the offline retail tend to purchase favorite products repeatedly in short periods. Overcoming such shortcomings could help offline retail to identify the right product that has a higher likelihood to be purchased by a specific customer, and hence increasing revenue. This paper proposes the use of Item-based Collaborative Filtering algorithm as recommender systems to address the limitations of the offline setting.

Keywords

Recommender system Collaborative Filtering Offline commerce 

References

  1. 1.
    Hahsler, M.: Developing and testing top-n recommendation algorithms for 0–1 data using recommenderlab. NSF Industry University Cooperative Research Center for Net-Centric Software and System (2011)Google Scholar
  2. 2.
    Hahsler, M.: Recommenderlab: a framework for developing and testing recommendation algorithms. R package version 0.1-5 (2014)Google Scholar
  3. 3.
    Hu, Y., Koren, Y., Volinsky, C.: Collaborative filtering for implicit feedback datasets. In: 8th IEEE International Conference on Data Mining, pp. 263–272. IEEE, Pisa (2008).  https://doi.org/10.1109/icdm.2008.22
  4. 4.
    Karypis, G.: Evaluation of item-based top-n recommendation algorithms. In: 10th ACM International Conference on Information and Knowledge Management, pp. 247–254. ACM, Georgia (2001).  https://doi.org/10.1145/502585.502627
  5. 5.
    Melville, P., Mooney, R., Nagarajan, R.: Content-boosted collaborative filtering for improved recommendations. In: 18th AAAI National Conference on Artificial Intelligence, pp. 187–192. AAAI, Edmonton (2002)Google Scholar
  6. 6.
    Nichols, D.: Implicit rating and filtering. In: 5th DELOS Workshop on Filtering and Collaborative Filtering, vol. 12. ERCIM, Budapest (1997)Google Scholar
  7. 7.
    Oard, D., Kim, J.: Implicit feedback for recommender systems. In: 15th AAAI Workshop on Recommender Systems, pp. 81–83. AAAI, Wisconsin (1998)Google Scholar
  8. 8.
    Pan, R., Zhou, Y., Cao, B., et al.: One-class collaborative filtering. In: 8th IEEE International Conference on Data Mining, pp. 502–511. IEEE, Pisa (2008).  https://doi.org/10.1109/icdm.2008.16
  9. 9.
    Sarwar, B.: Analysis of recommendation algorithms for e-commerce. In: 2nd ACM International Conference on Electronic Commerce, pp. 285–295. ACM, Minnesota (2000).  https://doi.org/10.1145/352871.352887
  10. 10.
    Sarwar, B., Karypis, G., Konstan, J., et al.: Item-based collaborative filtering recommendation algorithms. In: 10th ACM International Conference on World Wide Web, pp. 285–295. ACM, Hong Kong (2001).  https://doi.org/10.1145/371920.372071
  11. 11.
    Su, X., Khoshgoftaar, T.: A survey of collaborative filtering techniques. Adv. Artif. Intell. 2009, 19 (2009).  https://doi.org/10.1155/2009/421425. Article ID 421425CrossRefGoogle Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2018

Authors and Affiliations

  1. 1.Stream IntelligenceJakarta SelatanIndonesia

Personalised recommendations