Recommender System Based On Deep Collaborative Cross Network

In the age of information explosion,all kinds of information can make our head spin.This inevitably leads to information overload and makes it difficult for people to retrieve information they are interested in.Therefore,the importance of personalized recommendation system is self-evident.Most of the existing recommendation algorithms are based on collaborative filtering.The main disadvantages of collaborative filtering algorithms are their difficulties in solving the problem of data sparsity.Moreover,collaborative filtering algorithms only use the interaction between users and items for modeling,and the implicit information mined by them is still insufficient.Most of the related methods only focus on one of the explicit and implicit information,which results in the poor recommendation accuracy.To solve the problems above,a recommendation algorithm based on data integration and deep collaborative cross network is proposed.First,the user's preferences for tags are calculated by utilizing the user's ratings and tags attached to items,and predicts the preference-based impression score of users for items.Then the user's social relationship information is mined from the existing data.The concrete approach is to calculate the similarity among different users,and then the trusted user set is defined for every user.The social-relationship-based impression score of users for items are predicted by calculating the similarity between the third-party scorer and the trusted user set.Finally,the two kinds of impression score are weighted and averaged to obtain the comprehensive rating of social and preference to implement data integration.In terms of network structure,the existing collaborative cross network is fused with the neural network based on deep matrix factorization.The fused network implements the knowledge transfer and sharing between the two base networks via the task relation matrix,and bridges the source network and the target network through the identical user set and the mutually disjoint item set.Finally,under the premise of non-repetitive recommended results,the two basic networks can cooperate with each other in their training.The experiments are conducted on MovieLens and Book-Crossing data sets which are widely used for recommendation systems.It evaluates the hit ratio in top-K recommendation and the sorting quality of recommendation list.In addition,the performance of rating prediction is evaluated,and it is proved that the metrics on the tasks of score prediction and top-K recommendation of the proposed method are all better than the current state-of-the-art recommendation algorithm.Finally,the sensitivity of the hyper-parameters involved in the method is analyzed by experiments,and the values of the hyper-parameters that make the method perform best are obtained.