Time Series Forecasting by Recommendation: An Empirical Analysis on Amazon Marketplace

  • Álvaro Gómez-LosadaEmail author
  • Néstor Duch-Brown
Conference paper
Part of the Lecture Notes in Business Information Processing book series (LNBIP, volume 353)


This study proposes a forecasting methodology for univariate time series (TS) using a Recommender System (RS). The RS is built from a given TS as only input data and following an item-based Collaborative Filtering approach. A set of top-N values is recommended for this TS which represent the forecasts. The idea is to emulate RS elements (the users, items and ratings triple) from the TS. Two TS obtained from Italy’s Amazon webpage were used to evaluate this methodology and very promising performance results were obtained, even the difficult environment chosen to conduct forecasting (short length and unevenly spaced TS). This performance is dependent on the similarity measure used and suffers from the same problems that other RSs (e.g., cold-start). However, this approach does not require high computational power to perform and its intuitive conception allows for being deployed with any programming language.


Collaborative Filtering Time series Forecasting Data science 



The views expressed are purely those of the authors and may not in any circumstances be regarded as stating an official position of the European Commission.


  1. 1.
    Goldberg, D., Nichols, D., Oki, B.M., Terry, D.: Using collaborative filtering to weave an information tapestry. Commun. ACM Spec. Issue Inf. Filter. 35, 61–70 (1992). Scholar
  2. 2.
    Sharma, R., Gopalani, D., Meena, Y.: Collaborative filtering-based recommender system: approaches and research challenges. In: 3rd International Conference on Computational Intelligence & Communication Technology, pp. 1–6. IEEE Press (2017).
  3. 3.
    Sarwar, B., Karypis, G., Konstan, J., Reidl, J.: Item-based collaborative filtering recommendation algorithms. In: Proceedings of the 10th International Conference on World Wide Web, Hong Kong, 2001, pp. 285–295. ACM, New York (2001).
  4. 4.
    Desrosiers, C., Karypis, G.: A comprehensive survey of neighborhood-based recommendation methods. In: Ricci, F., Rokach, L., Shapira, B., Kantor, P.B. (eds.) Recommender Systems Handbook, pp. 107–144. Springer, Boston, MA (2011). Scholar
  5. 5.
    Bobadilla, J., Ortega, F., Hernando, A., GutiéRrez, A.: Recommender Systems Survey. Knowl. Based Syst. 46, 109–132 (2013). Scholar
  6. 6.
    Ekstrand, M.D., Riedl, J.T., Konstan, J.A.: Collaborative filtering recommender systems. Found. Trends Hum. Comput. Interact. 4(2), 81–173 (2010). Scholar
  7. 7.
    Haslher, M., Vereet, B.: recommenderlab: A Framework for Developing and Testing Recommendation Algorithms (2018).
  8. 8.
    Ochiai, A.: Zoogeographical studies on the soleoid fishes found in Japan and its neighhouring regions-II. Bull. Japan. Soc. Sci. Fish 22, 526–530 (1957). Scholar
  9. 9.
    Jacobi, J.A., Benson, E.A., Linden, G.D.: Personalized recommendations of items represented within a database. US Patent US7113917B2 (to Amazon Technologies Inc.) (2006).
  10. 10.
    Breese, J.S, Heckerman, D., Kadie, C.: Empirical analysis of predictive algorithms for collaborative filtering. In: Proceedings of the 14th Conference on Uncertainty in Artificial Intelligence, pp. 43–52. Morgan Kaufmann Publishers (1998)Google Scholar

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© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Joint Research Centre, European CommissionSevilleSpain

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