Recommendation algorithms can be divided into many types, among which collaborative filtering recommendation, content-based recommendation and mixed recommendation are common. Collaborative filtering recommendation is a recommendation method based on user behavior data. It will look for other users with similar interests to the current users, and then recommend the products that these users like to the current users. Content-based recommendation is to match the characteristics of goods with the interests of users, and recommend related goods by analyzing the attributes, labels and other information of goods. Hybrid recommendation is to combine various recommendation algorithms to improve the accuracy and effect of recommendation.
Besides recommendation algorithms, shopping websites can also use other strategies to recommend products to users. For example, personalized recommendations are made according to users' purchase history and browsing behavior, and products that they may be interested in are recommended to users. Shopping websites can also make use of users' social network information, such as concerned products, concerned brands and associated users, to make recommendations.
To sum up, shopping websites use recommendation algorithms when recommending products to users, and make predictions according to users' behaviors and interests, thus providing personalized recommendation experience.