The DOI matches its Crossref record; 1 of the 1 references it cites were found in a registry or an index.
Analysis of recommendation algorithms for e-commerce
Open accessAbstract
Recommender systems apply statistical and knowledge discovery techniques to the problem of making product recommendations during a live customer interaction and they are achieving widespread success in E-Commerce nowadays. In this paper, we investigate several techniques for analyzing large-scale purchase and preference data for the purpose of producing useful recommendations to customers. In particular, we apply a collection of algorithms such as traditional data mining, nearest-neighbor collaborative filtering, and dimensionality reduction on two different data sets. The first data set was derived from the web-purchasing transaction of a large E-commerce company whereas the second data set was collected from MovieLens movie recommendation site. For the experimental purpose, we divide the recommendation generation process into three sub processes - representation of input data, neighborhood formation, and recommendation generation. We devise different techniques for different sub processes and apply their combinations on our data sets to compare for recommendation quality and performance.
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Cites
Mining frequent patterns without candidate generation
Han, J., Pei, J., & Yin, Y. (2000) · ACM SIGMOD Record · doi:10.1145/335191.335372 · Cited by 6,402
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Sarwar, B., Karypis, G., Konstan, J., & Riedl, J. (2001) · Proceedings of the 10th international conference on World Wide Web · doi:10.1145/371920.372071 · Cited by 9,135
Self-citationEvaluating collaborative filtering recommender systems
Herlocker, J. L., Konstan, J. A., Terveen, L. G., & Riedl, J. T. (2004) · ACM Transactions on Information Systems · doi:10.1145/963770.963772 · Cited by 5,798
Self-citationAmazon.com recommendations: item-to-item collaborative filtering
Linden, G., Smith, B., & York, J. (2003) · IEEE Internet Computing · doi:10.1109/mic.2003.1167344 · Cited by 5,403
The business model ontology a proposition in a design science approach
Osterwalder, A. (2004) · Unknown · doi:10.22005/bcu.15985 · Cited by 1,932
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Xiao, B., & Benbasat, I. (2007) · MIS Quarterly · doi:10.2307/25148784 · Cited by 1,081
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CommunityCommands: command recommendations for software applications
Matejka, J., Li, W., Grossman, T., & Fitzmaurice, G. (2009) · Proceedings of the 22nd annual ACM symposium on User interface software and technology · doi:10.1145/1622176.1622214 · Cited by 125
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Personalised Reranking of Paper Recommendations Using Paper Content and User Behavior
Li, X., Chen, Y., Pettit, B., & Rijke, M. D. (2019) · ACM Transactions on Information Systems · doi:10.1145/3312528 · Cited by 46
Algorithmic and Ethical Aspects of Recommender Systems in e-Commerce
Paraschakis, D. (2018) · doi:10.24834/2043/24268 · Cited by 35
Personalization and probabilities: Impersonal propensities in online grocery shopping
Mackenzie, A. (2018) · Big Data & Society · doi:10.1177/2053951718778310 · Cited by 31
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Metadata
- Authors
- Sarwar, B., Karypis, G., Konstan, J., & Riedl, J.
- Venue
- Proceedings of the 2nd ACM conference on Electronic commerce
- Publisher
- ACM
- Published
- 2000-10-17
- Pages
- 158-167
- OpenAlex
- W1996283866
- Cited by
- 1,827
- References
- 1
- FWCI
- 40.78
- Type
- conference paper
- Language
- en
- Open access
- green
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