Convolution Serialization Recommendation with Time Characteristics and User Preferences

The recommendation system has been widely used in life, which greatly facilitates people's life. The traditional recommendation method is mainly used to analyze the interaction between users and items. analyze the history of users and items, and get only the users' preferences for items in the past. The serialization recommendation system analyzes the sequence of users interacting with objects in a recent period of time. To consider the relevance of the user's before and after behavior, can obtain the user's preference for items in the short term. However, the serialization method emphasizes the user's connection with the item in the short term. Ignoring the relationship between the properties of objects. In view of the above problems, the convolution serialization recommendation of fusion time characteristics and user preferences is proposed Convolutional Embedding Recommendation with Time and User Preference. CERTU model. The model is able to analyze the diversity relationships between items, thus capturing the user's dynamic preference for items over time. Otherwise, the model further considers the influence of a single item and multiple items present in the item sequence on the next item recommendation. The experimental results show that the No. The CERTU model outperforms the current baseline method.