In a personalized product recommendation system, when the amount of log data is large or
sparse, the accuracy of model recommendation will be greatly affected. To solve this …

In recent years, deep learning has been applied to the field of recommendation, which can
learn complex user interaction features and make better recommendations. However, deep …

In the traditional recommendation algorithms, due to the rapid development of deep learning
and Internet technology, user-item rating data is becoming increasingly sparse. The simple …

E-commerce systems experience poor quality of performance when the number of records in
the customer database increases due to the gradual growth of customers and products …

Abstract Latent Factor Model (LFM), as an effective feature mapping method, is widely
applied in recommender systems. One challenge of LFM is previous methods usually use …

Aiming at data sparsity and timeliness in traditional E‐commerce collaborative filtering
recommendation algorithms, when constructing user‐item rating matrix, this paper utilizes …

Recommender system has recently attracted a lot of attention in the information service
community. Currently, most recommendation models use deep neural networks to learn user …

In recent years, the models combining traditional machine learning with the deep learning
are applied in many commodity recommendation practices. It has been proved better …

In recent years, recommendation systems have become more complex with increasing
research on user preferences. Recommendation algorithm based on deep learning has …

With a sharp improvement in E-commerce and data, the precise rating prediction of
recommended items under user preferences has been a hot research topic in the EC …