A hybrid recommendation algorithm based on user comment sentiment and matrix decomposition

In order to improve recommendation quality of recommendation algorithms, this paper proposes a hybrid recommendation algorithm based on user comments sentiment and matrix decomposition (abbreviate as RACSMD). This algorithm first calculates the sentiment tendency towards the user's comment through the LSTM algorithm, and then integrates the sentiment value of the user's rating to increase the accuracy of the user's actual rating before combining the matrix decomposition recommendation algorithm to improve recommendation quality. This paper theoretically verifies the feasibility of RACSMD through an algorithm example. Moreover, corresponding experimental analysis is conducted on the basis of three data sets of Beeradvocate, Modcloth and Amazon. Experimental results show that the introduction to sentiment tendencies towards user comments can effectively improve recommendation quality of recommendation algorithms.