Tcl Research America Inc.

Dae Hoon Park

Lifan Guo

Mengwen Liu

Haohong Wang

Wanying Ding

A method for an app search engine leveraging user reviews is provided. The method includes receiving an app search query from a user, determining a plurality of relevant apps based on the received app search query, and extracting app descriptions and user reviews associated with the plurality of relevant apps from an app database. The method also includes preprocessing the extracted app descriptions and user reviews of each of the plurality of relevant apps to generate a text corpus and creating a topic-based language model for each of the plurality of relevant apps based on the generated text corpus. Further, the method includes ranking a list of relevant apps using the topic-based language model and providing the ranked app list for the user.

Method and system for app search engine leveraging user reviews

In accordance with some embodiments of the disclosed subject matter, a method and a system for App page recommendation are provided. The method includes: based on social media contents generated by a user, establishing a parallel corpora comprising an implicit query database including a plurality of user implicit query texts, and an explicit query database including a plurality of user explicit query texts, wherein the plurality of user implicit query texts correspond to the plurality of user explicit query texts; receiving a voice user query from the user; converting the voice user query to a text query; extracting implicit intent text from the text query; inferring a user intent using the parallel corpora based on the extracted implicit intent text; ranking a plurality of App pages according to the inferred user intent; and recommending a subset of the plurality of App pages to the user based on the ranking.

Method and system for App page recommendation via inference of implicit intent in a user query

The present invention provides a method for providing augmented product specifications based on user reviews. The method obtains input data of specifications and user reviews on a plurality of products, each specification including at least a pair of a feature and a feature-value of the product. The method concatenates the user reviews of the products to form product-documents, each product-document corresponding to the concatenated user reviews of a product. The method further employs a topic model to process the input data and learn topic distributions in the product-documents and word distributions in topics. The topics include specifications of the products. The topic model is a type of statistical model for discovering topics that occur in a collection of product-documents. Based on the topic model, the method can provide augmented specifications including one or more of relevant sentences of the feature-value, feature importance information, and product-specific words of the product.

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San Jose, CA, United States of America

Dae Hoon Park