Improvement of Business Analysis Method of E-Commerce System from the Perspective of Intelligent Recommendation System

Algorithm 1

Content filter recommendation algorithm.

	Input: Current user ID, user item rating matrix URL, K nearest neighbor, maximum recommended value Top-N;
	Output: List of recommended items L;
	Step l: In the recommendation system based on the UserCF implementation, enter the scoring data of all users for all items, and construct a matrix R of M ∗ N as shown in Table 1, that is, M users and N items, Rj represents i users rate the jth item. The rating of a certain user is regarded as a vector on the n-dimensional item space, and if the i user does not score the j item, it is expressed as Rij-0 in the matrix.
	Step 2: Calculate Nearest Neighbor: That is, calculate the user similarity value between the current user and all M users, and select the K users with the largest user similarity as the current user’s nearest neighbor. As mentioned earlier, here are multiple methods to calculate user similarity, and the more commonly used ones are:
(1)	Cosine-based Similarity based on cosine
	If the set of items that users i and j have a common evaluation of is represented by 1, the similarity between the two users is sim (i, j) as:
	Among them, Ric and Rjc represent the user’s i and j scores on item c, respectively.
(2)	Adjusted Cosine Similarity
	The formula calculation method does not take into account the difference between user rating averages. For example, some users prefer to score high scores, and some users prefer to score low scores, so the corrected cosine similarity calculation method is to subtract the user’s rating of a certain item by subtracting the user’s average rating of all items, thereby solving the scoring scale problem between different users, and the calculation formula is:
	where I and I represent the set of recommended objects rated by the user i and the user j, respectively; R and R represent the average ratings of user i and user j, respectively.
	Person Correlation Coefficient
	The similarity sim (i, j) between users i and j is also measured by Pearson correlation coefficients, as follows:
	Among them, R and R represent the average score of the user i and j on the project, respectively.
	Step 3: Through the above methods to get the current user’s K nearest neighbors, the next step is to produce the corresponding recommendation results. If the nearest neighbor set of user u is represented by N, the prediction score of user u on item i Pa.i is calculated as follows:
	where in sim (u, n) indicates the similarity between user u and n, Rn,j indicates the user n’s score of the item i, ru, Rn represents the average score of the user u.n on the item, respectively.
	Step 4: Through the above method, the current user can calculate the pre-evaluation score of all the products that have not interacted with the current user, and finally select the list L of the Top-N recommended items with the highest prediction score to the current user.