Hybrid Recommendation Scheme Based on Deep Learning

Algorithm 1

Hybrid collaborative filtering, pretreatment.

	Input: User feature mount feature. product feature matrix feature, user rating matrix , user list and product list
	Output: Residual data set
(1)	Init Empty similarity matrix sim and sim //REviewed users and products similarity matrix;
(2)	Search the user list and the product List in ;
(3)	Construct. List and ListI into pairwise tuples set and
(4)	for in and i in set do
(5)	Compute sim and sim ;
(6)	sim add in sim , sim ;
(7)	Calculate all users similarity matrix Sim and all items similarity matrix sim
(8)	for in do
(9)	Define polarity score:
(10)	Search the K-clustering U and T-clustering-I in SimRU SimRI
(11)	Compute the rating of all users of uS on the product It//user collaboration;
(12)	User Uk’s rating on the products in the iS collection of all fields//item collaboration;
(13)	Query , construct and respectively;
(14)	Select relevant users and products are selected to construct a real score matrix
(15)	repeact//after multiple iterations, find the Wrong samplesby updating the learning r ate and weight coefficients
(16)	Package ;
(17)	Add data in Data;
(18)	until Get all data
(19)	final;
(20)	return Data;