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US 10,592,555 B1Granted
Intelligent customer services based on a vector propagation on a click graph model
AssigneeAlibaba Group Holding Limited
InventorsWangshu Zhang, Zhiwei Shi, Junhong Liu, Jie Cai, Xiang Hu, Defeng Mao
Life of this patent
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Abstract
A query is received from a user at a data service engine. The query includes a string of characters. A number of candidate topics are identified by the data service engine based on the query. A similarity score is determined between the query and each of the plurality of candidate topics based on a Vector Propagation On a Click Graph (VPCG) model trained based on user click data. A number of candidate topics are ranked based on the similarity scores. One or more topics are selected from the ranked candidate topics. The selected topics are outputted via a user interface (UI).
Claims17
A computer-implemented method, comprising:
- receiving, at a data service engine, a query from a user, wherein the query comprises a string of characters;
- identifying, by the data service engine, a plurality of candidate topics based on the query;
- determining a similarity score between the query and each of the plurality of candidate topics based on a Vector Propagation On a Click Graph (VPCG) model trained based on user click data, wherein the VPCG model is trained by:
- identifying a plurality of queries from a plurality of users in the user click data;
- identifying a plurality of user selections in response to the plurality of queries from the plurality of users in the user click data;
- determining a vector representation of each of the plurality of queries in terms of a plurality of word elements in a vocabulary, wherein the determining the vector representation of each of the plurality of queries in terms of the plurality of word elements in the vocabulary comprises representing the each of the plurality of queries based on a vector representation of each of plurality of candidate topics, a total number of user clicks or selections of a candidate topic for a query in the user click data and a total number of topics in the user click data;
- and
- determining a vector representation of each of the plurality of candidate topics in terms of the plurality of word elements in the vocabulary, wherein the determining the vector representation of each of the plurality of candidate topics in terms of the plurality of word elements in the vocabulary comprises representing the each of the plurality of candidate topics based on a vector representation of each of the plurality of queries, a total number of user clicks or selections of a candidate topic for a query in the user click data and a total number of user queries in the user click data;
- and
- ranking the plurality of candidate topics based on the similarity scores;
- selecting one or more topics from the ranked candidate topics;
- and
- outputting the topics via a user interface (UI).
10 more elements
A computer-implemented system comprising:
- one or more computers;
- and
- one or more computer memory devices interoperably coupled with the one or more computers and having tangible, non-transitory, machine-readable media storing instructions, that when executed by the one or more computers, perform operations comprising:
- receiving, at a data service engine, a query from a user, wherein the query comprises a string of characters;
- identifying, by the data service engine, a plurality of candidate topics based on the query;
- determining a similarity score between the query and each of the plurality of candidate topics based on a Vector Propagation On a Click Graph (VPCG) model trained based on user click data, wherein the VPCG model is trained by:
- identifying a plurality of queries from a plurality of users in the user click data;
- identifying a plurality of user selections in response to the plurality of queries from the plurality of users in the user click data;
- determining a vector representation of each of the plurality of queries in terms of a plurality of word elements in a vocabulary, wherein the determining the vector representation of each of the plurality of queries in terms of the plurality of word elements in the vocabulary comprises representing the each of the plurality of queries based on a vector representation of each of plurality of candidate topics, a total number of user clicks or selections of a candidate topic for a query in the user click data and a total number of topics in the user click data;
- and
- determining a vector representation of each of the plurality of candidate topics in terms of the plurality of word elements in the vocabulary, wherein the determining the vector representation of each of the plurality of candidate topics in terms of the plurality of word elements in the vocabulary comprises representing the each of the plurality of candidate topics based on a vector representation of each of the plurality of queries, a total number of user clicks or selections of a candidate topic for a query in the user click data and a total number of user queries in the user click data;
- and
- ranking the plurality of candidate topics based on the similarity scores;
- selecting one or more topics from the ranked candidate topics;
- and
- outputting the topics via a user interface (UI).
13 more elements
A non-transitory, computer-readable medium storing one or more instructions executable by a computer-implemented system to perform operations comprising:
- receiving, at a data service engine, a query from a user, wherein the query comprises a string of characters;
- identifying, by the data service engine, a plurality of candidate topics based on the query;
- determining a similarity score between the query and each of the plurality of candidate topics based on a Vector Propagation On a Click Graph (VPCG) model trained based on user click data, wherein the VPCG model is trained by:
- identifying a plurality of queries from a plurality of users in the user click data;
- identifying a plurality of user selections in response to the plurality of queries from the plurality of users in the user click data;
- determining a vector representation of each of the plurality of queries in terms of a plurality of word elements in a vocabulary, wherein the determining the vector representation of each of the plurality of queries in terms of the plurality of word elements in the vocabulary comprises representing the each of the plurality of queries based on a vector representation of each of plurality of candidate topics, a total number of user clicks or selections of a candidate topic for a query in the user click data and a total number of topics in the user click data;
- and
- determining a vector representation of each of the plurality of candidate topics in terms of the plurality of word elements in the vocabulary, wherein the determining the vector representation of each of the plurality of candidate topics in terms of the plurality of word elements in the vocabulary comprises representing the each of the plurality of candidate topics based on a vector representation of each of the plurality of queries, a total number of user clicks or selections of a candidate topic for a query in the user click data and a total number of user queries in the user click data;
- and
- ranking the plurality of candidate topics based on the similarity scores;
- selecting one or more topics from the ranked candidate topics;
- and
- outputting the topics via a user interface (UI).
10 more elements
Dependencies are inferred from the claim text.