Coterie是一种异步的组模式,要求在不等时间间隔约束下,找出具有相似轨迹行为的组模式.而传统的轨迹组模式挖掘算法往往处理具有固定时间间隔采样约束的GPS数据,因此无法直接用于Coterie模式挖掘.同时,传统组模式挖掘存在语义信息缺失问...Coterie是一种异步的组模式,要求在不等时间间隔约束下,找出具有相似轨迹行为的组模式.而传统的轨迹组模式挖掘算法往往处理具有固定时间间隔采样约束的GPS数据,因此无法直接用于Coterie模式挖掘.同时,传统组模式挖掘存在语义信息缺失问题,降低了个性化旅游路线推荐的完整度和准确度.为此,提出基于语义的距离敏感推荐策略DRSS(distance-aware recommendation strategy based on semantics)和基于语义的从众性推荐策略CRSS(conformity-aware recommendation strategy based on semantics).此外,随着社交网数据规模的不断增大,传统组模式聚类算法的效率受到极大的挑战,因此,为了高效处理大规模社交网轨迹数据,使用带有优化聚类的MapReduce编程模型来挖掘Coterie组模式.实验结果表明:MapReduce编程模型下带优化聚类和语义信息的Coterie组模式挖掘,在个性化旅游路线推荐上优于传统组模式旅游路线推荐质量,且能够有效处理大规模社交网轨迹数据.展开更多
Abstract Instagram is a popular photo-sharing social ap- plication. It is widely used by tourists to record their journey information such as location, time and interest. Consequently, a huge volume of get-tagged phot...Abstract Instagram is a popular photo-sharing social ap- plication. It is widely used by tourists to record their journey information such as location, time and interest. Consequently, a huge volume of get-tagged photos with spatio-temporal in- formation are generated along tourist＇s travel trajectories. Such Instagram photo trajectories consist of travel paths, travel density distributions, and traveller behaviors, prefer- ences, and mobility patterns. Mining Instagram photo trajec- tories is thus very useful for many mobile and location-based social applications, including tour guide and recommender systems. However, we have not found any work that extracts interesting group-like travel trajectories from Instagram pho- tos asynchronously taken by different tourists. Motivated by this, we propose a novel concept： coterie, which reveals representative travel trajectory patterns hidden in Instagram photos taken by users at shared locations and paths. Our work includes the discovery of （1） coteries, （2） closed co- teries, and （3） the recommendation of popular travel routes based on closed coteries. For this, we first build a statistically reliable trajectory database from Instagram get-tagged pho- tos. These trajectories are then clustered by the DBSCAN method to find tourist density. Next, we transform each raw spatio-temporal trajectory into a sequence of clusters. All dis- criminative closed coteries are further identified by a Cluster- Growth algorithm. Finally, distance-aware and conformity- aware recommendation strategies are applied on closed co- teries to recommend popular tour routes. Visualized demosand extensive experimental results demonstrate the effective- ness and efficiency of our methods.展开更多
文摘Coterie是一种异步的组模式,要求在不等时间间隔约束下,找出具有相似轨迹行为的组模式.而传统的轨迹组模式挖掘算法往往处理具有固定时间间隔采样约束的GPS数据,因此无法直接用于Coterie模式挖掘.同时,传统组模式挖掘存在语义信息缺失问题,降低了个性化旅游路线推荐的完整度和准确度.为此,提出基于语义的距离敏感推荐策略DRSS(distance-aware recommendation strategy based on semantics)和基于语义的从众性推荐策略CRSS(conformity-aware recommendation strategy based on semantics).此外,随着社交网数据规模的不断增大,传统组模式聚类算法的效率受到极大的挑战,因此,为了高效处理大规模社交网轨迹数据,使用带有优化聚类的MapReduce编程模型来挖掘Coterie组模式.实验结果表明:MapReduce编程模型下带优化聚类和语义信息的Coterie组模式挖掘,在个性化旅游路线推荐上优于传统组模式旅游路线推荐质量,且能够有效处理大规模社交网轨迹数据.
文摘Abstract Instagram is a popular photo-sharing social ap- plication. It is widely used by tourists to record their journey information such as location, time and interest. Consequently, a huge volume of get-tagged photos with spatio-temporal in- formation are generated along tourist＇s travel trajectories. Such Instagram photo trajectories consist of travel paths, travel density distributions, and traveller behaviors, prefer- ences, and mobility patterns. Mining Instagram photo trajec- tories is thus very useful for many mobile and location-based social applications, including tour guide and recommender systems. However, we have not found any work that extracts interesting group-like travel trajectories from Instagram pho- tos asynchronously taken by different tourists. Motivated by this, we propose a novel concept： coterie, which reveals representative travel trajectory patterns hidden in Instagram photos taken by users at shared locations and paths. Our work includes the discovery of （1） coteries, （2） closed co- teries, and （3） the recommendation of popular travel routes based on closed coteries. For this, we first build a statistically reliable trajectory database from Instagram get-tagged pho- tos. These trajectories are then clustered by the DBSCAN method to find tourist density. Next, we transform each raw spatio-temporal trajectory into a sequence of clusters. All dis- criminative closed coteries are further identified by a Cluster- Growth algorithm. Finally, distance-aware and conformity- aware recommendation strategies are applied on closed co- teries to recommend popular tour routes. Visualized demosand extensive experimental results demonstrate the effective- ness and efficiency of our methods.
