Enhancing User Interest based on Stream Clustering and Memory Networks in Large-Scale Recommender Systems

Recommender Systems (RSs) provide personalized recommendation Recommender Systems (RSs) [1, 2] which provide personalized service based on user interest, which are widely used in various recommendation service based on user interest are widely used in platforms. However, there are lots of users with sparse interest various platforms such as short video platforms [3, 7, 14], video due to lacking consumption behaviors, which leads to poor recommendation platforms [4, 5], E-commerce platforms [6, 8-11] and social networks results for them. This problem is widespread in [12, 13], serving billions of users. In RSs, Ranking typically large-scale RSs and is particularly difficult to address. To solve uses a Multi-Task Learning model (MTL) [4, 8, 16-21] and lots this problem, we propose a novel solution named User Interest of features to finely predict the scores of various user behaviors Enhancement (UIE) which enhances user interest including user such as click, watching time, fast slide, like and sharing for thousands profile and user history behavior sequences using the enhancement of candidates. The accuracy of the scores outputted by MTL vectors and personalized enhancement vector generated with is crucial for RSs [4]. In RSs, user interest includes user profile the help of other similar users and relevant items based on stream and user history behavior sequences, as shown in Figure 1 and clustering and memory networks from different perspectives. UIE Figure 2, which determines the upper limit of ranking model's not only remarkably improves model performance on the users performance. However, lots of users only have sparse interest due with sparse interest but also significantly enhance model performance to lacking consumption behaviors.