Preference Degree Based Personalized Recommendation Algorithm

doi:10.16183/j.cnki.jsjtu.2018.07.003

Abstract

Abstract: Faced with increasing number of hotels in online tourism, the problem of sparse data is becoming more and more serious. On one hand, it leads to a significant decrease in recommendation accuracy; on the other hand, the computational load of traditional recommendation algorithm is increased, which is difficult to meet the real-time requirement. So, this paper firstly proposed a preference degree based personalized recommendation algorithm which mined the potential correlation between user historical data and recommend items. The novel algorithm utilized users’ historical data to calculate preference degree and then construct new features, and its realization is based on classification algorithm. Besides, the new method is applied to make personalized recommendations in online tourism. Results from real data sets showed that the proposed preference degree based personalized recommendation algorithm is effective and universal.

Key words: content-based recommendation, potential correlation, construction of preference degree, room recommendation

CLC Number:

TP 391

HUANG Jinchao，ZHANG Jiawei，CHEN Ning，CHEN Yihong. Preference Degree Based Personalized Recommendation Algorithm[J]. Journal of Shanghai Jiaotong University, 2018, 52(7): 770-776.

References

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