SCFM: Social and crowdsourcing factorization machines for recommendation
With the rapid development of social networks, the exponential growth of social information has attracted much attention. Social information has great value in recommender systems to alleviate the sparsity and cold start problem. On the other hand, the crowd computing empowers recommender systems by utilizing human wisdom. Internal user reviews can be exploited as the wisdom of the crowd to contribute information. In this paper, we propose social and crowdsourcing factorization machines, called...[Show more]
|Collections||ANU Research Publications|
|Source:||Applied Soft Computing|
|Access Rights:||Open Access|
|1-s2.0-S1568494617305100-main.pdf||1 MB||Adobe PDF|
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