In recent years,deep learning has made tremendous achievements in computer vision,natural language processing,man-machine games and so on,where artificial intelligence can reach or go beyond the level of human beings....In recent years,deep learning has made tremendous achievements in computer vision,natural language processing,man-machine games and so on,where artificial intelligence can reach or go beyond the level of human beings.However,behind so many glories,some serious challenges exist in the bottom hardware,hindering the further development of Artificial Intelligence.展开更多
The rapid development of online services and information overload has inspired the fast development of recommender systems, among which collaborative filtering algorithms and model-based recommendation approaches are ...The rapid development of online services and information overload has inspired the fast development of recommender systems, among which collaborative filtering algorithms and model-based recommendation approaches are wildly exploited. For instance, matrix factorization （MF） demonstrated successful achievements and advantages in assisting internet users in finding interested information. These existing models focus on the prediction of the users＇ ratings on unknown items. The performance is usually evaluated by the metric root mean square error （RMSE）. However, achieving good performance in terms of RMSE does not always guarantee a good ranking performance. Therefore, in this paper, we advocate to treat the recommendation as a ranking problem. Normalized discounted cumulative gain （NDCG） is chosen as the optimization target when evaluating the ranking accuracy. Specifically, we present three ranking-oriented recommender algorithms, NSME AdaMF and AdaNSME NSMF builds a NDCG approximated loss function for Matrix Factorization. AdaMF is based on an algorithm by adaptively combining component MF recommenders with boosting method. To combine the advantages of both algorithms, we propose AdaNSME which is a hybird of NSMF and AdaME and show the superiority in both ranking accuracy and model generalization. In addition, we compare our proposed approaches with the state-of-the-art recommendation algorithms. The comparison studies confirm the advantage of our proposed approaches.展开更多
基金the National Key R&D Program of China(2017YFA0207600)National Outstanding Youth Science Fund Project of National Natural Science Foundation of China(61925401)+2 种基金PKU-Baidu Fund Project(2019BD002)National Natural Science Foundation of China(92064004,61927901,61421005,61674006)the 111 Project(B18001).
文摘In recent years,deep learning has made tremendous achievements in computer vision,natural language processing,man-machine games and so on,where artificial intelligence can reach or go beyond the level of human beings.However,behind so many glories,some serious challenges exist in the bottom hardware,hindering the further development of Artificial Intelligence.
文摘The rapid development of online services and information overload has inspired the fast development of recommender systems, among which collaborative filtering algorithms and model-based recommendation approaches are wildly exploited. For instance, matrix factorization （MF） demonstrated successful achievements and advantages in assisting internet users in finding interested information. These existing models focus on the prediction of the users＇ ratings on unknown items. The performance is usually evaluated by the metric root mean square error （RMSE）. However, achieving good performance in terms of RMSE does not always guarantee a good ranking performance. Therefore, in this paper, we advocate to treat the recommendation as a ranking problem. Normalized discounted cumulative gain （NDCG） is chosen as the optimization target when evaluating the ranking accuracy. Specifically, we present three ranking-oriented recommender algorithms, NSME AdaMF and AdaNSME NSMF builds a NDCG approximated loss function for Matrix Factorization. AdaMF is based on an algorithm by adaptively combining component MF recommenders with boosting method. To combine the advantages of both algorithms, we propose AdaNSME which is a hybird of NSMF and AdaME and show the superiority in both ranking accuracy and model generalization. In addition, we compare our proposed approaches with the state-of-the-art recommendation algorithms. The comparison studies confirm the advantage of our proposed approaches.
