## 2 Related Work
### Traditional Recommendation Systems  
The recommendation system is a widely used information filtering tool to provide customers with product information and suggestions to help users to decide which products they should purchase. [PERSON] et al. [22] classified recommendation methods into three categories: collaborative filtering, content-based filtering, and hybrid recommender systems. Collaborative filtering is one of the earliest and the most successful techniques used in recommender systems. It generally uses the preference of user groups with similar tastes to the target user to predict the target user's preference for a specific product. Generally speaking, there are two types of collaborative filtering techniques. The first is user-based collaborative filtering [23; 24], and the second is item-based collaborative filtering [25; 26]. User-based collaborative filtering mainly considers the similarity between users. It predicts the target user's rating for a particular item based on the ratings of items liked by similar users. The basic idea of item-based filtering is to calculate the similarity between items based on the historical preference data of all users, and then to recommend items similar to the user's favorite item to the target user. Currently, a large number of scholars focus on utilizing machine learning models to improve the performance of collaborative filtering technique. Matrix factorization [27], neural network [28], and graphic models [29] are commonly used in combination with collaborative filtering. The most difficult challenge faced by the collaborative filtering technique is the cold start problem when a new user arrives. Since the recommender system does not have any data of new users, it cannot effectively recommend items for new users. In addition, collaborative filtering cannot understand different scenarios, which is unable to capture the specific consumption purpose of users at a specific moment. Content-based filtering works by evaluating the similarity between items that the user has not seen and items that the user has liked in the past. To generate meaningful recommendation results, content-based filtering uses different models to find similarities between items. It typically uses a vector space model (e.g., term frequency inverse document frequency) or a probabilistic model (naive Bayes classifier, decision tree, and neural network) to model relationships between different items [30; 31; 32]. Content-based filtering technology does not need to refer to other user portraits because other user portraits will not affect the final recommendations. Moreover, content-based filtering technology can still adjust the recommendation results in a very short period of time if the user profile changes. The main disadvantage of this technique is that it requires the system to have a deep understanding of the characteristics of the item. Since content-based filtering depends only on the user's past preferences for certain items, users can only get recommendations similar to items in their own profile, hardly getting diversified options. Hybrid recommender systems combine multiple recommendation algorithms to avoid the problems of a single technique. [PERSON] [33] distinguished hybrid recommender systems into three basic design ideas: monolithic, parallelized, and pipelined. The monolithic paradigm integrates multiple recommendation algorithms into the same algorithm system, and the integrated recommendation algorithm provides a unified recommendation service. The parallelized paradigm utilizes multiple independent recommendation algorithms, each of which generates its own recommendation results, and fuses these recommendation results in the mixing stage to generate the final recommendation result. In the pipelined paradigm, the recommendation result generated by one algorithm is given to another recommendation algorithm as input, and then the recommendation result is generated, which is input to the next recommendation algorithm, and so on.