Latent Group Recommendation Based on Dynamic Probabilistic Matrix Factorization Model Integrated with CNN

Group recommendation has recently received great attention in the academic sector due to its significant utility in real applications. However, the available group recommendation methods mainly aggregate individual recommendation results or personal preferences directly based on an analysis of rating matrix. The relationship among users, groups, and services has not been taken into comprehensive consideration during group recommendation, which will interfere with the accuracy of recommendation results. Inspired by latent factor model and state space model, we propose a latent group recommendation (LGR) based on dynamic probabilistic matrix factorization model integrated with convolutional neural network (DPMFM-CNN), which comprehensively investigates rating matrix, service description documents and time factor and makes a joint analysis of the relationship among those three entities. The proposed LGR method firstly obtains a prior distribution for service latent factor model with the employment of text representation method based on convolutional neural network (CNN). Secondly, it integrates state space model with probabilistic matrix factorization model and draws user latent vector together with service latent vector. Thirdly, latent groups are detected through the use of multiple clustering algorithms on user latent vectors. Finally, group latent vectors are aggregated with average strategy and group rating can be generated. In addition, simulation on MovieLens is performed and comparison results demonstrate that LGR has better performance in efficiency and accuracy for group recommendation.