Name
Title | ||
|---|---|---|
Personalized Commodity Recommendations of Retail Business Using User Feature Based Collaborative Filtering |
Abstract | ||
|---|---|---|
Collaborative filtering is an extensively adopted approach for commodity recommendation. This paper proposes a user feature based collaborative filtering algorithm named UFCF for personalized commodity recommendations of retail business. It adopts matrix factorization and user features that are extracted from users' behaviors to improve the accuracy of recommendation result and alleviate the impact of sparse data. Experiments with real datasets from a supermarket marketing group demonstrate the effectiveness of the algorithm. |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1109/BDCloud.2018.00051 | 2018 IEEE Intl Conf on Parallel & Distributed Processing with Applications, Ubiquitous Computing & Communications, Big Data & Cloud Computing, Social Computing & Networking, Sustainable Computing & Communications (ISPA/IUCC/BDCloud/SocialCom/SustainCom) |
Keywords | Field | DocType |
recommendation, collaborative filtering, user feature | Collaborative filtering,Information retrieval,Computer science,Commodity,Matrix decomposition,Human–computer interaction,Feature based,Sparse matrix | Conference |
ISSN | ISBN | Citations |
2158-9178 | 978-1-7281-1141-4 | 1 |
PageRank | References | Authors |
0.37 | 0 | 5 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Feiran Wang | 1 | 2 | 2.08 |
| Yiping Wen | 2 | 25 | 8.59 |
| Tianhang Guo | 3 | 1 | 0.37 |
| Jinjun Chen | 4 | 130 | 14.37 |
| Buqing Cao | 5 | 200 | 23.96 |