Abstract
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, NSMF, AdaMF and AdaNSMF. 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 AdaNSMF, which is a hybird of NSMF and AdaMF, 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.
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Richong Zhang received his BS and MAS from Jilin University, China in 2001 and 2004, respectively. He received his MS from Dalhousie University, Canada in 2006. He received his PhD form the School of Information Technology and Engineering, University of Ottawa, Canada. He is currently an associate professor in the School of Computer Science and Engineering, Beihang University, China. His research interests include recommender systems, knowledge graph and crowdsourcing.
Han Bao received his BS from Beihang University (BUAA), China in 2014. He is currently a PhD student in BUAA. His research interests include recommender systems, data mining, and machine learning.
Hailong Sun received his BS in computer science from Beijing Jiaotong University, China in 2001. He received his PhD in computer software and theory from Beihang University (BUAA), China in 2008. He is currently an associate professor in the School of Computer Science and Engineering, BUAA. His research interests include software systems, crowdsourcing and distributed computing.
Yanghao Wang received his BE from Beihang University (BUAA), China in 2012. He is currently a PhD student in BUAA. His research interests include crowdsourcing, web services, and machine learning.
Xudong Liu received his PhD in computer application technology from Beihang University (BUAA), China. He is a professor and doctoral supervisor at BUAA. His research interests mainly include middleware technology and applications, service-oriented computing, trusted network computing, and network software development.
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Zhang, R., Bao, H., Sun, H. et al. Recommender systems based on ranking performance optimization. Front. Comput. Sci. 10, 270–280 (2016). https://doi.org/10.1007/s11704-015-4584-1
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DOI: https://doi.org/10.1007/s11704-015-4584-1