基于多重隐语义表示模型的旅游路线挖掘
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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.
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.
引文
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[9]YANG D Q,ZHANG D Q,YU Z Y,et al.Fine-Grained Preference-Aware Location Search Leveraging Crowdsourced Digital Footprints from LBSNs//Proc of the ACM International Joint Conference on Pervasive and Ubiquitous Computing.New York,USA:ACM,2013:479-488.
[10]YIN P F,YE M,LEE W C,et al.Mining GPS Data for Trajectory Recommendation//Proc of the 18th Pacific-Asia Conference on Advances in Knowledge Discovery and Data Mining.Berlin,Germany:Springer,2014:50-61.
[11]FENG S S,LI X T,ZENG Y F,et al.Personalized Ranking Metric Embedding for Next New POI Recommendation//Proc of the24th International Joint Conference on Artificial Intelligence.Palo Alto,USA:AAAI Press,2015:2069-2075.
[12]LEVANDOSKI J J,SARWAT M,ELDAWY A,et al.LARS:A Location-Aware Recommender System//Proc of the 28th IEEE International Conference on Data Engineering.Washington,USA:IEEE,2012:450-461.
[13]YE M,YIN P F,LEE W C,et al.Exploiting Geographical Influence for Collaborative Point-of-Interest Recommendation//Proc of the 34th International ACM SIGIR Conference on Research and Development in Information Retrieval.New York,USA:ACM,2011:325-334.
[14]LIAN D F,ZHAO C,XIE X,et al.Geo MF:Joint Geographical Modeling and Matrix Factorization for Point-of-Interest Recommendation//Proc of the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining.New York,USA:ACM,2014:831-840.
[15]YIN H Z,SUN Y Z,CUI B,et al.LCARS:A Location-ContentAware Recommender System//Proc of the 19th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining.New York,USA:ACM,2013:221-229.
[16]YUAN Q,CONG G,MA Z Y,et al.Time-Aware Point-of-Interest Recommendation//Proc of the 36th International ACM SIGIR Conference on Research and Development in Information Retrieval.New York,USA:ACM,2013:363-372.