## 1 Introduction  
As mobile Internet and information technology has achieved great technological progress, consumers can browse products and make purchase through mobile devices from anywhere at any time [1]. The rapid development of mobile e-commerce has intensified competition between e-commerce companies. E-commerce enterprises maintain their competitive advantage by implementing product differentiation strategies, offering consumers more products and discounts, as well as exploiting intelligent information filtering systems to assist online users in quickly finding products that are in line with their preferences [2]. Online retailers need to provide customers with targeted goods and services according to their different needs to avoid homogeneous competition [3]. The recommender system is a classical type of information filtering system that attempts to recommend products to users that can conform to their different hobbies and personal experience [4, 5]. However, the traditional recommendation method only uses ratings to reflect the user's overall preference for items, but it is difficult to depict users' relative preferences for multiple dimensions of product features [6]. E-commerce platforms hope to help consumers to quickly find the right product that satisfies the heterogeneous needs of consumers and delivers their business ideas and product information to these potential consumers in a targeted manner [7, 8]. E-commerce enterprises are committed to developing a more powerful personalized recommender system to enhance shopping experience of online consumers. Although traditional personalized recommendation methods (such as collaborative filtering and content-based recommendation algorithms) are widely used, they all have their own shortcomings. Therefore, hybrid recommender systems are proposed to deal with these shortcomings by combining different recommendation algorithms. Recently, the most widely used hybrid recommender systems are based on collaborative filtering algorithms and content-based algorithms, while other types of combinations have also been developed. The main idea of collaborative filtering is to use the preferences of user groups with similar tastes to the target user to predict what the target user might like. The data sparsity problem and the cold start problem are considered as two key problems faced by collaborative filtering techniques [9; 10; 11; 12]. The data sparsity problem seriously restricts the performance of collaborative filtering. For large business websites, due to the large number of products and users, the user rating products generally do not exceed 1% of the total number of products. The cold start problem usually occurs when new users arrive. As there is no user behavior data when a new user enters the system, it is difficult to make effective recommendations. The basic idea of content-based filtering is to recommend other items similar to the items that the user liked in the past. The content-based filtering technology relies on user portraits. Therefore, even if the database does not contain user interests, it will not affect the accuracy of the recommendation results [13]. However, the content-based filtering technique depends on the item's metadata. That is to say, the system needs rich item content descriptions and complete user portraits. Hence, users can only get recommendations similar to items in their own profile, hardly getting diversified options. One of the ways to build a hybrid recommender system is to independently apply collaborative filtering, content-based and other algorithms, for recommendations by combining the recommendation results of two or more systems and using the linear combination of prediction scores to make recommendations. Some hybrid recommender systems are content-based collaborative filtering algorithms. That is, the similarity of users is calculated through content-based profiles, rather than the information of products that are rated together. This can overcome the sparsity problem in collaborative filtering systems.