Name
Abstract | ||
|---|---|---|
A recommendation algorithm based on fuzzy clustering is proposed in this paper. The idea of this system is as follows: Firstly, based on examples of hotel users' reviews and movie reviews collected by website crawler, the high-frequency valueless words and the low-frequency unimportant words are removed by means of term frequency-inverse document frequency (TF-IDF) algorithm of textual word segmentation. After then, term vector representation is used for preparation of subsequent related algorithms. Finally, fuzzy clustering is conducted to the review word segmentation dataset. Based on the clustering dataset mentioned above, the terms, sentences and users are separately clustered. The input of recommendation in each stage is used as the output of next recommendation to finally form an organic whole. Fuzzy clustering method adopted by the system greatly reduces the size of the dataset in the iterative process of the recommended algorithm and improves the precision of the recommendation. |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1109/ICMLC.2018.8527026 | 2018 International Conference on Machine Learning and Cybernetics (ICMLC) |
Keywords | Field | DocType |
Fuzzy Clustering,Term Frequency,Term Vector,TF-IDF,Word Segmentation,word2vec | Recommender system,Fuzzy clustering,Iterative and incremental development,Computer science,Algorithm,Feature extraction,Text segmentation,Artificial intelligence,Cluster analysis,Web crawler,Machine learning | Conference |
Volume | ISSN | ISBN |
1 | 2160-133X | 978-1-5386-5215-2 |
Citations | PageRank | References |
0 | 0.34 | 6 |
Authors | ||
6 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Huihui Zhan | 1 | 0 | 0.34 |
| Wei-Xing Zhou | 2 | 206 | 15.05 |
| Xiao-Hui Hu | 3 | 10 | 5.55 |
| Qianhua Cai | 4 | 0 | 0.34 |
| Tao Zhang | 5 | 220 | 69.03 |
| Long Yang | 6 | 0 | 0.34 |