ISSN 1000-1239 CN 11-1777/TP

Journal of Computer Research and Development ›› 2017, Vol. 54 ›› Issue (8): 1813-1823.doi: 10.7544/issn1000-1239.2017.20170188

Special Issue: 2017人工智能前沿进展专题

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Learning to Recommend with Collaborative Matrix Factorization for New Users

Gao Yukai1, Wang Xinhua1, Guo Lei2, Chen Zhumin3   

  1. 1(School of Information Science & Engineering, Shandong Normal University, Jinan 250358);2(School of Management Science & Engineering, Shandong Normal University, Jinan 250358);3(School of Computer Science and Technology, Shandong University, Jinan 250101)
  • Online:2017-08-01

Abstract: 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.

Key words: recommender system, location-based service, probabilistic matrix factorization, cold-start problem, budget

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