Aiming at the fact that traditional linear regression recommendation algorithms don't take into account influencing factors such as user interest drift, activity, and credibility, this paper proposed a linear regression recommendation algorithm incorporated credibility ratings to further improve the accuracy of the algorithm and the fit to user preferences. Firstly, this paper comprehensively considered the user's interest drift, activity and user evaluation information in the calculation method of user scoring credibility. Then it integrated this algorithm into the coefficient matrix solution process of traditional linear regression recommendation algorithm. Finally, it used the optimized linear regression recommendation algorithm to predict user scoring. In order to verify the accuracy of the algorithm, this paper compared the proposed algorithm with the traditional linear regression recommendation algorithms on Hadoop cluster and Amazon product scoring dataset. The experimental results show that the algorithm has significantly improved the processing efficiency, recommendation effect and fitting degree.

[2021-02-05] Printed Article /doc/2019.12.0667/v2