When the user scores items, sometimes there are unreasonable factors that cause the user to make an unreasonable score on items, which may cause bias in the recommendation process. In order to correct this deviation, multiple dimensions of the scoring matrix are used to compare the similarity to correct the unreasonable score, and then the revised score is used for collaborative filtering recommendation. When using the variable dimension scoring matrix for similarity comparison, the similarity of the same user's scoring similar items is used, and the cosine similarity of two users' similarity of multiple similar items in different dimensions is compared. Firstly, multiple scores are constructed into several arrays of equal dimensions, and the similarity of each score array of the two users is compared. When a similarity differs greatly from other similarities, it is considered that the similarity corresponds to at least one of the two user arrays containing an unreasonable score. Secondly, the two arrays are divided into smaller arrays in the same way, and the reset can be done in the same manner, finally determining the unreasonable score. Finally, the similarity mean of all reasonable score arrays is used as the similarity of the corresponding array of unreasonable scores, thereby correcting the unreasonable score. Experiments using the MovieLens and Bookcrossing datasets show that the collaborative filtering algorithm with revised scoring has higher recommendation accuracy than the unmodified scoring, and its recommendation score MAE is reduced significantly. Compared with the comparison algorithm, the proposed algorithm can obtain better recommendation performance on MAE, Precision and Coverage.
