A Novel Personalized Business Recommendation Analysis Method Based on Big Data Intelligence

With the rapid development of e-commerce technology, e-commerce platform has become the main place for enterprises to recommend goods and users to consume goods. Countless enterprises present tens of thousands of goods on e-commerce platforms for users to choose, but this brings inconvenience to users in selecting and purchasing goods, as well as problems such as information overload. To solve this problem, most e-commerce platforms have applied personalized recommendation technologies and algorithms, but in actual application process, personalized recommendation systems also have problems such as sparse matrix, cold start and user differences. In order to effectively improve the existing problems and improve the recommendation quality, based on the basic theory of big data and e-commerce personalized recommendation. This paper constructs an optimized collaborative filtering recommendation algorithm using the value of commodity attributes and the Paasche coefficient, and compares the recommendation effect of the constructed recommendation algorithm with four common recommendation algorithms, verifying the effectiveness of the constructed recommendation algorithm.