• 中国精品科技期刊
  • CCF推荐A类中文期刊
  • 计算领域高质量科技期刊T1类
Advanced Search
Gao Yukai, Wang Xinhua, Guo Lei, Chen Zhumin. Learning to Recommend with Collaborative Matrix Factorization for New Users[J]. Journal of Computer Research and Development, 2017, 54(8): 1813-1823. DOI: 10.7544/issn1000-1239.2017.20170188
Citation: Gao Yukai, Wang Xinhua, Guo Lei, Chen Zhumin. Learning to Recommend with Collaborative Matrix Factorization for New Users[J]. Journal of Computer Research and Development, 2017, 54(8): 1813-1823. DOI: 10.7544/issn1000-1239.2017.20170188

Learning to Recommend with Collaborative Matrix Factorization for New Users

More Information
  • Published Date: July 31, 2017
  • Location-based service (LBS) as an information sharing platform can help people obtain more useful information. But with the increasing number of users, LBS is faced with a serious problem of information overload. Using the recommender system to filter information and help users to find valuable information has become a hot research topic in recent years. In LBS, only positive implicit feedback is available and user cold-start problem in this scenario is not well studied. Based on the observations, we consider the characteristics of location-based services platform and propose a recommender algorithm, which combines collaborative PMF (probabilistic matrix factorization) with GBDT (gradient boosting decision tree), to solve the cold start problem. The algorithm first use multi probabilistic matrix factorization to learn user latent feature in different dimension, and then use gradient boosting decision tree to train the factor and label to learn the user’s preference, finally use the improved top-N recommender which considers the budget problem to produce the recommendation list. The experimental results on the real data show that the proposed algorithm can achieve better results in accuracy and F1 than other popular methods, and can solve the cold-start problem in LBS recommendation.
  • Related Articles

    [1]Lei Xiangxin, Yang Zhiying, Huang Shaoyin, Hu Yunfa. Mining Frequent Subtree on Paging XML Data Stream[J]. Journal of Computer Research and Development, 2012, 49(9): 1926-1936.
    [2]Chen Honglong, Li Renfa, Li Rui, Edwin Sha. An Assignment Model and Algorithm for Self-Adaptive Software Based on Architecture[J]. Journal of Computer Research and Development, 2011, 48(12): 2300-2307.
    [3]Han Donghong, Gong Pizhen, Xiao Chuan, Zhou Rui. Load Shedding Strategies on Sliding Window Joins over Data Streams[J]. Journal of Computer Research and Development, 2011, 48(1): 103-109.
    [4]Yu Jiong, Tian Guozhong, Cao Yuanda, Sun Xianhe. A Resource Allocating Algorithm in Grid Workflow Based on Critical Regions Reliability[J]. Journal of Computer Research and Development, 2009, 46(11): 1821-1829.
    [5]Yu Kun, Wu Guoxin, Xu Libo, Wu Peng. Optimal Path Based Geographic Routing in Ad Hoc Networks[J]. Journal of Computer Research and Development, 2007, 44(12): 2004-2011.
    [6]Wang Tao, Li Zhoujun, Yan Yuejin, Chen Huowang. A Survey of Classification of Data Streams[J]. Journal of Computer Research and Development, 2007, 44(11): 1809-1815.
    [7]Yang Xuemei, Dong Yisheng, Xu Hongbing, Liu Xuejun, Qian Jiangbo, Wang Yongli. Online Correlation Analysis for Multiple Dimensions Data Streams[J]. Journal of Computer Research and Development, 2006, 43(10): 1744-1750.
    [8]Wang Yongli, Xu Hongbing, Dong Yisheng, Qian Jiangbo, Liu Xuejun. Algorithms for Incremental Aggregation over Distributed Data Stream[J]. Journal of Computer Research and Development, 2006, 43(3): 509-515.
    [9]Liu Xuejun, Xu Hongbing, Dong Yisheng, Wang Yongli, Qian Jiangbo. Mining Frequent Patterns in Data Streams[J]. Journal of Computer Research and Development, 2005, 42(12): 2192-2198.
    [10]Qian Jiangbo, Xu Hongbing, Wang Yongli, Liu Xuejun, Dong Yisheng. Simultaneous Sliding Window Join Approach over Multiple Data Streams[J]. Journal of Computer Research and Development, 2005, 42(10): 1771-1778.

Catalog

    Article views (2018) PDF downloads (835) Cited by()

    /

    DownLoad:  Full-Size Img  PowerPoint
    Return
    Return