International Business Machines Corporation

Google Inc.

Beijing Realai Technology Co., Ltd

Tian Tian

Qi Liang

Weixiong Rao

Qi Ye

Guohua Li

Hong Min

Gong Su

Chi Liu

Ke Wei Wei

Jie Xu

Chu Yun Tong

Jesse Shieh

Qingxuan Yang

Liyuan Liu

Xinke Wang

Kunpeng Wang

Jean-François Puget

Qi Wen

Shizhen Xu

Peng Zhao

Jiayu Tang

A computer-implemented method, a computer system, and a computer program product for trajectory similarity search is provided. The present invention may include, in response to receiving, by one or more processors, a search request for at least one trajectory similar to a query trajectory, determining, by one or more processors, a respective similarity between a query trajectory and a plurality of trajectories by calculating, in a synchronized way, a spatial distance measure and a time difference measure between the query trajectory and the plurality of trajectories. The present invention may further include, identifying, by one or more processors, the at least one trajectory from the plurality of trajectories based on the respective similarity between the query trajectory and the plurality of trajectories.

Trajectory similarity search

Aspects of the invention include a computer-implemented method that receives, by a processor, an ensemble decision tree and generates, by the processor, native code from the ensemble decision tree. The method compiles, by the processor, the native code into machine language and scores, by the processor, the execution time of the native code. The method dynamically reoptimizes, by the processor, portions of the native code corresponding to the most traversed portion of the ensemble decision tree.

Generating native code with dynamic reoptimization for ensemble tree model prediction

Systems, methods, and computer-readable media for generating group-based sequential point-of-interest (POI) recommendations are disclosed. A primitive graph or sparse transition matrix is constructed that represents relationships between users, between POIs, and between users and POIs. A loss data prediction algorithm is executed on the primitive graph to obtain a filled graph, which may be a filled user-POI bipartite graph. A group sequential recommendation engine then performs an iterative sequential recommendation process that involves performing, at each iteration, various manipulations on the filled graph to obtain first a homogenous node type graph and then a minimum spanning tree. A next POI to recommend in a sequence of POIs is then determined from the minimum spanning tree. The iterative process continues until stopping criteria is met.

Group-based sequential recommendations

In one embodiment of the present disclosure, an original graph including nodes is obtained. The nodes of the original graph are reordered to generate a reordered graph. Non-zero elements in an adjacency matrix for the reordered graph are clustered as compared with an adjacency matrix for the original graph. The adjacency matrix for the reordered graph is encoded with integers. The integers correspond to non-empty blocks in the adjacency matrix for the reordered graph.

Directed graph compression

Methods, systems, and apparatus, including computer program products, for generating search query suggestions are provided. In one aspect, a method includes receiving in a data processing device a first textual input entered in a search engine query input field by a user; automatically sending from the device, before the user submits a request for a search and after waiting a predetermined amount of time after receiving each token of the first textual input, the first textual input to a first search service and a second search service; receiving from the first search service a set of first input suggestions and from the second search service a set of second input suggestions that is different from the set of first input suggestions; and displaying the first input suggestions in a first portion of a user interface and the second input suggestions in a distinct second portion of the user interface.

Generating search query suggestions

Growing community of inventors

Beijing, China

Tian Tian