Tcl Research America Inc.

Yue Shang

Haohong Wang

Lifan Guo

Mengwen Liu

Wanying Ding

Hsin-Tai Wu

Weilun Cheng

A method for scalable user intent mining is provided. The method includes detecting named entities from a plurality of query logs in a public query log dataset and generating features of the plurality of query logs based on the detected named entities. The method also includes applying a multimodal restricted boltzmann machine (RBM) on the generated features of the plurality of query logs to train a public multimodal RBM and generating a plurality of public query representations. Further, the method includes receiving a search query from a user, determining whether there are a plurality of history queries of the user. When there is no history query, user intent is predicted using the public multimodal RBM. When there are the history queries, the public multimodal RBM is applied on the plurality of history queries to train a personalized multimodal RBM, and the user intent is predicted using the personalized multimodal RBM.

Scalable user intent mining using a multimodal restricted boltzmann machine

A unified attractiveness prediction method is provided. The method includes receiving a plurality of videos and extracting at least one of metadata and view data from each of the plurality of received videos, wherein the metadata is information for describing video contents, and the view data is a total number of users who watch the video. The method also includes obtaining potential view amounts of the plurality of received videos and calculating impact factor scores of the plurality of received videos based on the potential view amounts, each impact factor score including a numerical score for indicating a degree of effectiveness of a corresponding video. Further, the method includes providing a video with a highest impact factor score based on the calculated impact factor scores.

Unified attractiveness prediction framework based on content impact factor

A method for multimodal clue based personalized app function recommendation is provided. The method includes receiving an app search query from a user, obtaining a plurality of real-time clues collected from the user and extracting a plurality of features to represent the collected clues, wherein the plurality of features include structured features and text features. The method also includes generating a joint representation of the multimodal clues based on the plurality of extracted features and creating a logistic regression model based on the joint representation of the multimodal clues. Further, the method includes ranking a list of app functions using the logistic regression model and displaying the ranked app function list for the user.

Method and system for multimodal clue based personalized app function recommendation

A method is provided for recommending a desired func sequence to a user. The method includes obtaining a user intention list including at least one user intention; separating the user intention into a plurality of tasks; and creating a task flow graph for the plurality of tasks based on user usage data. Each vertex in the task flow graph represents a task and indicating an importance of the task. The method also includes creating a func flow graph based on the user usage data and temporal sequences of the tasks and funcs, and each vertex in the func flow graph represents a func and indicating an importance of the func. Further, the method includes determining a desired func sequence to fulfill the user intention based on the user usage data, the task flow graph, and the func flow graph; and recommending the desired func sequence to the user.

Growing community of inventors

San Jose, CA, United States of America

Yue Shang