Hybrid Matrix factorization for recommender systems in social networks

Title:

Hybrid Matrix factorization for recommender systems in social networks

Creator:

Zhao, C., Sun, S., Han , L., and Peng, Q.

Identifier:

https://cdk.lib.cas.cz/client/handle/uuid:c8c0bf9a-4482-4ccf-a2a8-b4b823974dbd
uuid:c8c0bf9a-4482-4ccf-a2a8-b4b823974dbd
doi:10.14311/NNW.2016.26.032

Subject:

recomnmender system, matrix factorization, hybrid factors, and recommended interpretation

Type:

model:article and TEXT

Format:

bez média and svazek

Description:

Recommender systems have been well studied and applied both in the academia and industry recently. However, traditional recommender systems assume that all the users and items are independent and identically distributed. This assumption ignores the correlation of explicit attributes of both users and items. Aiming at modeling recommender systems more realistically and interpretably, we propose a novel and efficient hybrid matrix factorization method which combines implicit and explicit attributes, and can be used to solve the problem of cold start and recommender interpretation. Based on the MovieLens datasets, the experimental analysis shows our method is promising and efficient.

Language:

English

Rights:

http://creativecommons.org/publicdomain/mark/1.0/
policy:public

Source:

Neural network world: international journal on neural and mass-parallel computing and information systems | 2016 Volume:26 | Number:6

Harvested from:

CDK

Metadata only:

false