Author: Zen and the Art of Computer Programming
Recommendation System (Recommendation System) is a technology that can improve user satisfaction, improve product quality and promote sales conversion rate. Its core task is to provide personalized product recommendations according to user needs. It is usually composed of search engines, advertising recommendations, collaborative It is composed of multiple subsystems such as filtering. Among them, the search engine retrieves the keywords entered by the user, and selects documents from the massive information that are helpful for the user to obtain relevant content; the advertisement recommendation finds suitable advertisements and displays them to the user by analyzing the user's behavior habits and interest preferences; and Collaborative filtering relies on the interaction between items to infer user preferences, and makes recommendations based on these preferences. Multi-Granularity Model (Multi-Granularity Model), that is, an item can be divided into features of different granularities, and each feature corresponds to a recommendation subsystem. At the same time, each feature can also be organized into different subspaces to better match user interests. Therefore, the multi-granularity model can capture user interests at different granularities, balance and coordinate between different recommendation subsystems, and form the optimal recommendation results. With the explosive growth of Internet information, the traditional single-dimensional recommendation method has been unable to meet the diverse needs of users. Recommendation based on user portrait has become the mainstream method, but for some user groups with different attributes or interests, the portrait model still has certain limitations. On the other hand, the multi-granularity model is a novel recommendation method with the potential to break through the traditional single-dimensional model. Chi-square statistics, also known as Pearson correlation coefficient, is a statistical indicator used to measure the degree of linear correlation and independence between two variables. The chi-square statistic uses probability distributions to describe correlations between random variables, and if two variables are highly linearly correlated, their distributions will take on a similar form. From another perspective, if the distribution of two variables is very similar, then they are not significantly correlated. When user groups are divided according to different interest levels, chi-square statistics can be used as an effective tool to measure the degree of heterogeneity and dispersion in different subspaces. In addition, chi-square statistics can also be used as one of the important indicators for evaluating recommendation results, because recommendation algorithms usually output results most relevant to user interests&#x