User oriented trajectory search for trip recommendation

Abstract

Trajectory sharing and searching have received significant attentions in recent years. In this paper, we propose and investigate a novel problem called User Oriented Trajectory Search (UOTS) for trip recommendation. In contrast to conventional trajectory search by locations (spatial domain only), we consider both spatial and textual domains in the new UOTS query. Given a trajectory data set, the query input contains a set of intended places given by the traveler and a set of textual attributes describing the traveler's preference. If a trajectory is connecting/close to the specified query locations, and the textual attributes of the trajectory are similar to the traveler'e preference, it will be recommended to the traveler for reference. This type of queries can bring significant benefits to travelers in many popular applications such as trip planning and recommendation.There are two challenges in the UOTS problem, (i) how to constrain the searching range in two domains and (ii) how to schedule multiple query sources effectively. To overcome the challenges and answer the UOTS query efficiently, a novel collaborative searching approach is developed. Conceptually, the UOTS query processing is conducted in the spatial and textual domains alternately. A pair of upper and lower bounds are devised to constrain the searching range in two domains. In the meantime, a heuristic searching strategy based on priority ranking is adopted for scheduling the multiple query sources, which can further reduce the searching range and enhance the query efficiency notably. Furthermore, the devised collaborative searching approach can be extended to situations where the query locations are ordered. The performance of the proposed UOTS query is verified by extensive experiments based on real and synthetic trajectory data in road networks.

On Generalizing Collective Spatial Keyword Queries

“…A top-k kNN query [8], [18], [15], [19], [20], [9], [25] adopts the ranking function considering both the spatial proximity and the textual relevance of the objects and returns top-k objects based on the ranking function. This type of queries has been studied on Euclidean space [8], [18], [15], road network databases [19], trajectory databases [20], [9] and moving object databases [25].…”

“…This type of queries has been studied on Euclidean space [8], [18], [15], road network databases [19], trajectory databases [20], [9] and moving object databases [25]. Usually, the methods for this kind of queries adopt an index structure called the IR-tree [8], [23] capturing both the spatial proximity and the textual information of the objects to speed up the keyword-based nearest neighbor (NN) queries and range queries.…”

Inherent-Cost Aware Collective Spatial Keyword Queries

“…A top-k kNN query [9,19,16,20,21,10,26] adopts the ranking function considering both the spatial proximity and the textual relevance of the objects and returns top-k objects based on the ranking function. This type of queries has been studied on Euclidean space [9,19,16], road network databases [20], trajectory databases [21,10] and moving object databases [26].…”

“…This type of queries has been studied on Euclidean space [9,19,16], road network databases [20], trajectory databases [21,10] and moving object databases [26]. Usually, the methods for this kind of queries adopt an index structure called the IR-tree [9,24] capturing both the spatial proximity and the textual information of the objects to speed up the keyword-based nearest neighbor (NN) queries and range queries.…”

Finding regions of interest using location based social media

“…In contrast, the RD query takes both spatial distance and density distribution into account. A linear combination method [18,17] is adopted to combine the spatial and density domains.…”