Improvement of Business Analysis Method of E-Commerce System from the Perspective of Intelligent Recommendation System
Algorithm 3
User consumption preference mining algorithm.
Input: The user’s consumption preferences on the t-category goods <ai, , >.
Step l: Data preprocessing. Extract the user’s historical consumption records on the t-category goods, sorted by the chronological order of the user’s consumption. For each attribute a of the t-class goods, the set of its values is obtained , and n is the number of times the user consumes the t-class goods. For each attribute a, execute step 2 through step 4 and then go to step 5
Step 2: Attribute value distribution probability calculation. If the value of a is a continuous value, it is discretized by interval division, and the divided interval is represented by b, and will be mapped to each interval; If ai
The value is discrete, and its value is expressed in b. The probability that the property a takes the value b: is , where n, nbk indicates that the product consumed by the user takes the value of bs on attribute a or the number of times it is located in the interval bk.
Step 3: Calculate attribute weights from information. The information for attribute aI is construed
, the information is normalized, and the weight of genera a is = 1−H(a).
Step 4: Adjust the attribute weight according to the frequency of change of attribute values. The frequency of change of the attribute value is = n(n ≥ 2), where n is the number of times the adjacent attribute takes the actual change in value. Adjust the attribute weight to n − 1
= × (1 − B × q), β is the adjustment amplitude parameter, and the larger the β, the greater the adjustment amplitude.
Step 5: Attribute weight normalization. The normalization formula is: =
Step 6: Attribute value preference calculation. User ratings are on a ten-point scale, with the reflective relationship between a score of 0 and the math of the score system being the lowest of all the products consumed by the user. The numerical attribute value preference adopts the forgetting factor adjustment, and the forgetting factor calculation formula is the value of the product that the user has recently consumed on attribute a, p() is the probability of attribute a taking , and the adjustment formula is . The preference value of a nonnumeric attribute value is stored at most 1 element, and the preference attribute value is updated according to the first-in, first-out principle, and the size of 1 is taken from the experience value of 5.
