1-6 基于用户线下和线上行为的出行目的地预测.pdf

1、旅行目的地预测飞猪行业智能算法团队李良玥|SIGIR22 When online meets offline:exploring periodicity fortravel destination predictionWanjie Tao,Liangyue Li,Chen Chen,Zulong Chen,Hong Wen01背景介绍背景介绍02相关工作相关工作03研究方案研究方案04实验结果与结论实验结果与结论目录目录CONTENT|背景介绍01|飞猪介绍|首页酒店交通旅行场景的特质|行为属性用户旅行是超低频需求，行为数据稀疏用户访问频次低、间隔长，上一次的访问信息可能失效决策属性旅行具有明

2、显的行前-行中-行后的状态转移过程，并且不同状态下存在明显的差异用户对旅行的决策期较长，会有明显的前瞻规划需求旅行目的地预测应用场景|频道入口信息流推荐研究挑战|Offline spatial-temporal periodicity时空周期性：用户一般周末或者假期出行，更经常进行短途游，偶尔长途游Online multi-interest exploration多兴趣探索：用户会探索多个目的地的旅行商品，而且倾向于去没有访问过的城市相关工作02|Next-POI RecommendationWhere You Like to Go Next:Successive Point-of-Inter

3、est Recommendation,IJCAI,2013|Next-POI RecommendationGeography-Aware Sequential Location Recommendation,KDD,2020|Next-POI RecommendationGeography-Aware Sequential Location Recommendation,KDD,2020|Next-POI RecommendationSTAN:Spatio-Temporal Attention Network for Next Location Recommendation,WWW,2021|

4、Sequential RecommendationDeep Interest Network for Click-Through Rate Prediction,KDD,2018|Sequential RecommendationDeep Interest Network for Click-Through Rate Prediction,KDD,2018|Sequential RecommendationDeep Session Interest Network for Click-Through Rate Prediction,IJCAI,2019|Sequential Recommend

5、ationSparse-Interest Network for Sequential Recommendation,WSDM,2021|Sequential RecommendationSpatial-Temporal Deep Intention Destination Networks for Online Travel Planning,TIST,2021|解决方案03|我们的方案 Online-offline periodicity-aware information gain networkOOPIN|Offline Mobility Pattern Extractor|提取离线行

6、为的时空周期性离线行为矩阵基于CNN的时空周期性提取Periodicity-aware GRU Layer|提取离线行为的序列演变时空周期性感知的GRUDistance-aware Self-Attention Net(DSN)|用户线上探索的城市通常会呈现多个聚类例如长三角地区，或者京津冀地区Target-aware Attention Net(TAN)|从在线行为中提取出跟离线行为相似的信息Information Gain Net(IGN)|从在线行为中提取出信息增益即跟离线行为不一样的信息Final Prediction Layer|对每一个候选城