Abstract
Multi-task learning (MTL) is a learning paradigm which can improve generalization performance by transferring knowledge among multiple tasks. Traditional collaborative filtering recommendation methods suffer from cold start, sparsity and scalability problems. The latest research has shown that applying side information of knowledge graph can not only solve the problems above, but also improve the accuracy of recommendation. However, existing multi-task methods for knowledge graph enhanced recommendation expose obvious issues of disclosing the private information of training samples. In order to solve these problems, we put forward a privacy-preserving multi-task framework for knowledge graph enhanced recommendation. In specific, Laplacian noise is added into the recommendation module to guarantee the privacy of sensitive data and knowledge graph is utilized to improve the accuracy of recommendation. Extensive experimental results on three datasets demonstrate that the proposed method can not only preserve the privacy of sensitive training data, but also have little effect on the prediction accuracy of the model.
Highlights
Recommendation system (RS) is a branch of information filtering systems, which can find out connections between users and items [1], and is widely used in mobile applications, e-commerce, and even robotics
2) RESULTS ON THE ENHANCEMENT OF KNOWLEDGE GRAPH The aim of the proposed multi-task model is to use the auxiliary information of knowledge graph to assist privacypreserving recommendation system maintain a good prediction accuracy
We found that the method of alternative training multi-task model can reduce the prediction RMSE of the recommendation system by 0.88% - 11.56%
Summary
Recommendation system (RS) is a branch of information filtering systems, which can find out connections between users and items [1], and is widely used in mobile applications, e-commerce, and even robotics. It seeks to predict the rating or preference that a user would give to an item. In the process of obtaining user behavior, the training process is easy to be cracked because the large dataset involves a large number of personal privacy information, and the framework of training recommendation system is mostly based on a singletask mode.
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
