A Study of Building Peripheral Product Recommendation System
Luyu Zeng · 2021
Although the current recommendation system has been very mature and widely used, the recommendation system of peripheral products has been relatively seldom explored. This paper studies the analysis based on the historical behavior data of a large number of users, as well as the classification and processing of the information of the peripheral products purchased by the users, and finally recommends the peripheral products of the most popular products of the related products to the users, which effectively solves the problem that the users are searching again Peripheral products of related products, which also improve the shopping experience of users’ secondary consumption and reduce users’ purchase of counterfeit and shoddy products that increase their visibility by swiping orders. This study uses the Python language. Benefiting from the powerful spark, the system can handle large data sets. The results of this experiment show that the system can achieve high accuracy in large data sets.