Abstract: The use of tags provides a way for the system to divide and manage users and items,while personalized tag recommendations not only facilitate users input,but also help to improve the quality of system tags.In turn,the system can obtain more information about users and items,improve the accuracy of subsequent recommendations,improve the user experience.Therefore,it plays an important role in similar business scenarios such as Taobao and Didi.However,most existing tag recommendations do not pay attention to the ranking issues in the recommendation list.The tag that is too late in the list is easy to lose the opportunity for user use,resulting in the lack of information about users and items,and hindering the subsequent accurate recommendation.Aiming at the above problems,a personalized tag recommendation algorithm based on tensor Tucker decomposition and list-wise learning to rank is proposed.The algorithm is trained by optimizing MAP,and the simulation experiment is carried out on Last.fm dataset,which not only verified the effectiveness of the algorithm,but also fully explored the influence of learning rate,the dimension of core tensor and other parameters on the algorithm.Experimental results show that the algorithm can optimize the ranking problem of the recommendation list greatly,and its performance decreases linearly with the increase of the length of the list.The implementation of the algorithm is conducive to better recommendation services according to the user preferences.

Key words: Learning to rank, Tag recommendation, Tensor decomposition, Tucker decomposition