摘要
传统协同过滤推荐模型仅处理稀疏的评分数据,未深入挖掘用户及对象的潜在语义,且用户喜好信息也未充分利用.围绕旅游景点推荐这一热点问题,提出全新的混合分层抽样统计与贝叶斯个性化排序的推荐模型:采用分层抽样统计及主观赋值评价法刻画用户旅游喜好;基于矩阵分解算法(Matrix Factorization,简称MF)分析用户及对象(景点)的潜在语义,运用贝叶斯个性化排序算法(Bayesian Personalized Ranking,简称BPR)对推荐模型进行优化;综合用户旅游喜好信息及BPR优化结果,生成混合推荐列表.在新的"Wisdom Tourism"数据集上进行仿真实验.实验表明:推荐模型的RMSE、MAE、F1值较最强基线分别提升16.59%、10.05%、5.04%;相比于分层抽样统计方法,BPR算法在推荐过程中发挥更显著的作用.
Traditional recommendation system based on collaborative filtering only processes the sparse rating matrix.It doesn't extract the deep-level semantics of users(or items)as well as the users'preferences.To alleviate the above issues,a novel recommendation system for tourist spots based on Hierarchical Sampling Statistics(HSS)and Bayesian Personalized Ranking(BPR)is proposed.Users'preferences are generated and described firstly by the HSS algorithm and a subjective evaluation method.Then,deep-level semantics of users(or items)are extracted fully by the Matrix Factorization(MF)algorithm.And the state-of-art BPR algorithm is utilized in turn to optimize the entire recommendation model.Based on the users'preferences and the optimization results of the BPR algorithm,agroup of hybrid recommendation results are acquired and supplied to users.We demonstrate the effectiveness of our proposed model via extensive experiments on a novel"smart-travel"dataset created by ourselves.Experimental results show opposed to the best competitor,the RMSE,MAE and F1 value of the presented model improves about 16.59%,10.05% and 5.04%respectively.Compared against the HSS algorithm,the BPR algorithm has a more prominent role in the recommendation procedure.
引文
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