软件导刊2024，Vol.23Issue(6)：9-17,9.DOI:10.11907/rjdk.231526
融合偏好传播的多任务推荐模型研究
Research on Multi-task Recommendation Model Fused with Preference Propagation
摘要
Abstract
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.关键词
推荐系统/深度学习/知识图谱/偏好传播/多任务学习Key words
recommendation system/deep learning/knowledge graph/preference propagation/multi-task learning分类
信息技术与安全科学引用本文复制引用
杨本臣,叶洪宇,孟祥福..融合偏好传播的多任务推荐模型研究[J].软件导刊,2024,23(6):9-17,9.基金项目
国家自然科学基金面上项目(61772249) （61772249）
