Understanding collaborative filtering parameters for personalized recommendations in e-commerce

Cited 0 time in webofscience Cited 0 time in scopus
  • Hit : 429
  • Download : 0
Collaborative Filtering (CF) is a popular method for personalizing product recommendations for e-Commerce and customer relationship management (CRM). CF utilizes the explicit or implicit product evaluation ratings of customers to develop personalized recommendations. However, there has been no in-depth investigation of the parameters of CF in relation to the number of ratings on the part of an individual customer and the total number of ratings for an item. We empirically investigated the relationships between these two parameters and CF performance, using two publicly available data sets, EachMovie and MovieLens. We conducted three experiments. The first two investigated the relationship between a particular customers number of ratings and CF recommendation performance. The third experiment evaluated the relationship between the total number of ratings for a particular item and CF recommendation performance. We found that there are ratings thresholds below which recommendation performance increases monotonically, i.e., when the numbers of customer and item ratings are below threshold levels, CF recommendation performance is affected. In addition, once rating numbers surpass threshold levels, the value of each rating decreases. These results may facilitate operational decisions when applying CF in practice. ? Springer Science+Business Media, LLC 2007.
Kluwer Academic Publishers
Issue Date

ELECTRONIC COMMERCE RESEARCH, v.7, no.3-4, pp.293 - 314

Appears in Collection
MT-Journal Papers(저널논문)
Files in This Item
There are no files associated with this item.


  • mendeley


rss_1.0 rss_2.0 atom_1.0