基于轨迹挖掘模型的旅游景点推荐

Travel Attractions Recommendation Based on Trajectory Mining Representation Model

针对旅游推荐系统中基于内容的推荐和基于协同过滤的推荐方法的数据稀疏性和冷启动问题,以及现有轨迹挖掘方法忽略旅游轨迹中高级语义的问题,提出基于门控循环单元轨迹挖掘模型的推荐方法.为了充分利用旅游轨迹的高级语义信息,基于循环神经网络设计轨迹挖掘表示模型,对游客的旅游轨迹进行建模,在利用游客历史轨迹建模后向游客提供个性化旅游景点推荐.在真实旅游轨迹数据集上的实验表明,相比广泛使用的基线方法,文中方法在景点推荐的准确性和质量上都有一定提高.

A recommendation method based on the gated recurrent unit trajectory mining representation model(GRU-TMRM)is proposed to solve the problems of data sparsity and cold start in content based and collaborate filter based recommendation method,as well as the problem of ignoring rich semantics of travel track in track mining method.To take full advantage of semantics information contained in travel track,GRU-TMRM is designed.With GRU-TMRM,historical tracks of visitors can be modeled for providing personalized attractions recommendation.Experiments on real travel track dataset show that the proposed method effectively improves the accuracy and quality of recommendation compared with the widely used baseline method.