基于多重隐语义表示模型的旅游路线挖掘

摘要

针对用户个性化旅游行为过程的挖掘与景点推荐问题,提出多重隐语义旅游路线表示模型(MLSTR-RM).MLSTR-RM考虑不同上下文对用户旅游路线的影响,高效挖掘旅游路线中丰富的隐语义.首先确定模型中不同上下文包含的隐语义信息,然后通过负采样的方式训练模型参数,最后基于MLSTR-RM模型设计个性化景点推荐方法.在真实数据集上的实验表明文中模型的有效性.%Aiming at mining and recommending the personalized travel behavior of tourists, a multiple latent semantic travel route representation model ( MLSTR-RM ) is proposed. With the consideration of the influence of different contexts on the travel route, the efficient representation of different latent semantics in travel routes is studied in MLSTR-RM. Firstly, the latent semantic contained by the different contexts in model is determined. Then, the negative sampling is applied to train parameters in the model, and a personalized attraction recommendation method is designed based on MLSTR-RM model. Experiments on real data sets show the effectiveness of the proposed model.