Understanding collaborative filtering parameters for personalized recommendations in e-commerce

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-de...

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Bibliographic Details
Published in:Electronic commerce research 2007-12, Vol.7 (3-4), p.293-314
Main Authors: Lee, Hong Joo, Kim, Jong Woo, Park, Sung Joo
Format: Article
Language:English
Subjects:
Algorithms
Collaboration
Customer relationship management
Customers
Customization
Datasets
Electronic commerce
Investigations
Literature reviews
Preferences
Product development
Ratings & rankings
Recommender systems
Studies
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Summary: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 customer's 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. [PUBLICATION ABSTRACT]
ISSN:1389-5753
1572-9362
DOI:10.1007/s10660-007-9004-7