Time Series Forecasting by Recommendation: An Empirical Analysis on Amazon Marketplace
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.
KeywordsCollaborative 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.
- 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). https://doi.org/10.1109/CIACT.2017.7977363
- 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). https://doi.org/10.1145/371920.372071
- 7.Haslher, M., Vereet, B.: recommenderlab: A Framework for Developing and Testing Recommendation Algorithms (2018). https://CRAN.R-project.org/package=recommenderlab
- 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). https://patents.google.com/patent/US7113917B2/en
- 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