Name
Title | ||
|---|---|---|
Research on entropy-based collaborative filtering algorithm and personalized recommendation in e-commerce |
Abstract | ||
|---|---|---|
Based on the introduction to the user-based and item-based collaborative filtering algorithms, the problems related to the
two algorithms are analyzed, and a new entropy-based recommendation algorithm is proposed. Aiming at the drawbacks of traditional
similarity measurement methods, we put forward an improved similarity measurement method. The entropy-based collaborative
filtering algorithm contributes to solving the cold-start problem and discovering users’ hidden interests. Using the data
selected from Movielens and Book-Crossing datasets and MAE accuracy metric, three different collaborative filtering recommendation
algorithms are compared through experiments. The experimental scheme and results are discussed in detail. The results show
that the entropy-based algorithm provides better recommendation quality than user-based algorithm and achieves recommendation
accuracy comparable to the item-based algorithm. At last, a solution to B2B e-commerce recommendation applications based on
Web services technology is proposed, which adopts entropy-based collaborative filtering recommendation algorithm. |
Year | DOI | Venue |
|---|---|---|
2009 | 10.1007/s11761-008-0034-3 | Service Oriented Computing and Applications |
Keywords | Field | DocType |
personalized recommendation · entropy · collaborative filtering algorithm · similarity measure · b2b e-commerce · web services · mae,collaborative filtering,web service,e commerce | Recommender system,Data mining,Collaborative filtering,Information retrieval,Similarity measure,Computer science,MovieLens,Algorithm,Web service,E-commerce | Journal |
Volume | Issue | ISSN |
3 | 2 | 18632394 |
Citations | PageRank | References |
16 | 0.74 | 14 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Chunhui Piao | 1 | 72 | 10.33 |
| Jing Zhao | 2 | 16 | 0.74 |
| Lijuan Zheng | 3 | 61 | 9.56 |