A personalized recommendation algorithm based on large-scale real micro-blog data
| Autor: | Chaoyi Li, Yangsen Zhang |
|---|---|
| Rok vydání: | 2020 |
| Předmět: |
0209 industrial biotechnology
Social network Computer science Microblogging business.industry User modeling media_common.quotation_subject Big data 02 engineering and technology Local community 020901 industrial engineering & automation Artificial Intelligence 0202 electrical engineering electronic engineering information engineering 020201 artificial intelligence & image processing Social media Scale (map) business Function (engineering) Algorithm Software media_common |
| Zdroj: | Neural Computing and Applications. 32:11245-11252 |
| ISSN: | 1433-3058 0941-0643 |
| DOI: | 10.1007/s00521-020-05042-y |
| Popis: | With the arrival of the big data era, the amount of micro-blog users and texts is constantly increasing, and research on personalized recommendation algorithm for micro-blog texts is becoming more and more urgent. In consideration of the impact of user’s interests, trust transfer, time factor and social network, we proposed a new method for personalized recommendation. The method is based on community discovery, and recommends personalized micro-blog texts for users with the improved user model, which can use the social network of micro-blog platform effectively and optimize the utility function for micro-blog recommendation. Firstly, we used a multidimensional vector to represent the stereoscopic user model. Secondly, we proposed the improved k-means algorithm to extract the local community of users, which was also used to get the recommend micro-blog texts. Finally, the top-n micro-blog contents sorted by the effect function were recommended. We used a large number of real data to verify the algorithm proposed in this paper, and compared our method with some existing algorithms. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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