Design of Hybrid Recommendation Algorithm in Online Shopping System

In order to improve user satisfaction and loyalty on e-commerce websites,recommendation algorithms are used to recommend products that may be of interest to users.Therefore,the accuracy of the recommendation algorithm is a primary issue.So far,there are three mainstream recommendation algorithms,content-based recommendation algorithms,collaborative filtering algorithms and hybrid recommendation algorithms.Content-based recommendation algorithms and collaborative filtering algorithms have their own shortcomings.The content-based recommendation algorithm has the problem of the diversity of recommended items,while the collaborative filtering algorithm has the problem of data sparsity and scalability.On the basis of these two algorithms,the hybrid recommendation algorithm learns from each other’s strengths and combines the advantages of the two algorithms to provide people with better services.This article will focus on the use of a content-based recommendation algorithm to mine the user’s existing interests,and then combine the collaborative filtering algorithm to establish a potential interest model,mix the existing and potential interests,and calculate with the candidate search content set.The similarity gets the recommendation list.

Bayesian dual neural networks for recommendation

Most traditional collaborative filtering(CF)methods only use the user-item rating matrix to make recommendations,which usually suffer from cold-start and sparsity problems.To address these problems,on the one hand,some CF methods are proposed to incorporate auxiliary information such as user/item profiles;on the other hand,deep neural networks,which have powerful ability in learning effective representations,have achieved great success in recommender systems.However,these neural network based recommendation methods rarely consider the uncertainty of weights in the network and only obtain point estimates of the weights.Therefore,they maybe lack of calibrated probabilistic predictions and make overly confident decisions.To this end,we propose a new Bayesian dual neural network framework,named BDNet,to incorporate auxiliary information for recommendation.Specifically,we design two neural networks,one is to learn a common low dimensional space for users and items from the rating matrix,and another one is to project the attributes of users and items into another shared latent space.After that,the outputs of these two neural networks are combined to produce the final prediction.Furthermore,we introduce the uncertainty to all weights which are represented by probability distributions in our neural networks to make calibrated probabilistic predictions.Extensive experiments on real-world data sets are conducted to demonstrate the superiority of our model over various kinds of competitors.