融合偏好传播的多任务推荐模型研究OA

To address the problem that the knowledge graph can effectively reduce the triadic relationships of entities from multi-source het-erogeneous data,but is not conducive to recommendation tasks and it is difficult to explore the potential association relationships of data using single-task learning,a multi-task recommendation model with fused preference propagation(MAPKR)is proposed.Firstly,the user's prefer-ence feature set is extracted from the knowledge graph using ripple propagation;secondly,the potential features are shared based on the simi-lar nearest neighbor structure,and the higher-order feature representations of items and entities are extracted by cross-compression units;fi-nally,the recommendation module and the knowledge graph embedding module are trained alternately with multi-task learning,and the ex-tracted feature vectors are predicted and recommended after normalized inner product operation.Experiments are conducted on three publicly available datasets and compared with five baseline models.Compared with MKR and Ripple Net,the AUC and ACC are improved by 0.68%,0.31%and 0.77%,0.54%on MovieLens-1M dataset;3.48%,2.66%and 4.51%,7.21%on Book-Crossing,respectively;on Last.FM,AUC and ACC improved by 3.44%,6.25%and 2.70%,2.62%,respectively.The experimental results show that the proposed model has good recommendtion performance compared with other baseline models such as MKR and RippleNet.