Name
Abstract | ||
|---|---|---|
The rapid development of Internet technology has ushered in the era of information overload. How to pick out information with excellent quality and reduce unnecessary browsing time is a problem to be solved urgently. In order to recommend information that users might be interested in, this paper presents a new personalized recommendation algorithm with the quality of service (QoS) constraints based on latent factor model (LFM). Compared with the traditional recommendation algorithms, this algorithm is capable of effectively improving the recall rate, accuracy rate and coverage rate of the personalized recommendation system. |
Year | DOI | Venue |
|---|---|---|
2017 | 10.1109/BigDataCongress.2017.71 | 2017 IEEE International Congress on Big Data (BigData Congress) |
Keywords | Field | DocType |
latent factor model (LFM),quality of service (QoS) constraint,recommendation algorithm | Recommender system,Data mining,Data modeling,Information overload,Algorithm design,Recall rate,Computer science,Quality of service,Algorithm,Multimedia,Database,The Internet | Conference |
ISSN | ISBN | Citations |
2379-7703 | 978-1-5386-1997-1 | 0 |
PageRank | References | Authors |
0.34 | 9 | 2 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Ying Jin | 1 | 66 | 19.74 |
| Yiwen Zhang | 2 | 28 | 5.81 |