TWO-PHASE NEURAL NETWORK ARCHITECTURE FOR USER-SPECIFIC SEARCH RANKING
A method and system for generating a user-specific search ranking score for a document are disclosed. The method includes receiving a user log including a search history of a user, receiving a query from the user, providing the user log and the query to a first phase of an artificial neural network-based encoder to generate a token-based user representation of the user log and the search query, based, at least in part, on determining a relationship between the user log and the search query. The method further includes receiving a document from a set of search result documents associated with the query, and providing the token-based user representation and the document to a second phase of the artificial neural network-based encoder to generate a ranking score for the document, based, at least in part, on determining a relationship between the token-based user representation and the document.
1 . A computer-implemented method for generating a user-specific search ranking score for a document, the method comprising:
receiving a user log including a search history of a user;
receiving a search query from the user;
providing the user log and the search query to a first phase of an artificial neural network-based encoder;
generating, using the first phase of the artificial neural network-based encoder, a token-based user representation of the user log and the search query, based, at least in part, on determining a relationship between the user log and the search query;
receiving a document from a set of search result documents associated with the search query;
providing the token-based user representation and the document to a second phase of the artificial neural network-based encoder; and
generating, using the second phase of the artificial neural network-based encoder a ranking score for the document, based, at least in part, on determining a relationship between the token-based user representation and the document.
2 . The computer-implemented method of claim 1 , further comprising:
providing each document in the set of search result documents and the token-based user representation to the second phase of the artificial neural network-based encoder to generate a set of ranking scores, each ranking score in the set of ranking scores associated with a corresponding document in the set of search result documents; and
using the set of ranking scores to rank the set of search result documents.
3 . The computer-implemented method of claim 2 , wherein using the set of ranking scores to rank the set of search result documents comprises providing the set of ranking scores and the set of search result documents to a machine learning-based model for generating a final ranking.
4 . The computer-implemented method of claim 1 , wherein providing the user log and search query to the first phase of the artificial neural network-based encoder further comprises providing tokens representing the user log and search query.
5 . The computer-implemented method of claim 4 , wherein providing the user log and search query to the first phase of the artificial neural network-based encoder further comprises providing position information associated with the tokens representing the user log and search query.
6 . The computer-implemented method of claim 1 , wherein the first phase of the artificial neural network-based encoder comprises an attention mechanism.
7 . The computer-implemented method of claim 1 , wherein the first phase of the artificial neural network-based encoder comprises a transformer-based encoder.
8 . The computer-implemented method of claim 7 , wherein the first phase of the artificial neural network-based encoder comprises an encoder based on a bidirectional encoder representation from transformers (BERT) model architecture.
9 . The computer-implemented method of claim 7 , wherein the token-based user representation comprises a sequence of classifier tokens.
10 . The computer-implemented method of claim 1 , wherein providing the token-based user representation and the document to the second phase of the artificial neural network-based encoder further comprises providing tokens representing the document.
11 . The computer-implemented method of claim 10 , wherein providing the token-based user representation and the document to the second phase of the artificial neural network-based encoder further comprises providing position information associated with the tokens representing the document.
12 . The computer-implemented method of claim 1 , wherein the second phase of the artificial neural network-based encoder comprises an attention mechanism.
13 . The computer-implemented method of claim 1 , wherein the second phase of the artificial neural network-based encoder comprises a transformer-based encoder.
14 . The computer-implemented method of claim 13 , wherein the second phase of the artificial neural network-based encoder comprises an encoder based on a bidirectional encoder representation from transformers (BERT) model architecture.
15 . The computer-implemented method of claim 1 , wherein a single token-based user representation is used to generate ranking scores for multiple documents by the second phase of the artificial neural network-based encoder.
16 . A ranking system for generating a user-specific search ranking score for a document, the system comprising:
a processor;
a memory coupled to the processor; and
an artificial neural network-based encoder controlled by the processor;
wherein the memory stores machine-readable instructions that, when executed by the processor, cause the processor to:
receive a user log including a search history of a user;
receive a search query from the user;
provide the user log and the search query to a first phase of the artificial neural network-based encoder;
use the first phase of the artificial neural network-based encoder to generate a token-based user representation of the user log and the search query, based, at least in part, on determining a relationship between the user log and the search query;
receive a document from a set of search result documents associated with the search query;
provide the token-based user representation and the document to a second phase of the artificial neural network-based encoder; and
use the second phase of the artificial neural network-based encoder to generate a ranking score for the document, based, at least in part, on determining a relationship between the token-based user representation and the document.
17 . The ranking system of claim 16 , wherein the memory further stores machine-readable instructions that, when executed by the processor, cause the processor to:
provide each document in the set of search result documents and the token-based user representation to the second phase of the artificial neural network-based encoder to generate a set of ranking scores, each ranking score in the set of ranking scores associated with a corresponding document in the set of search result documents; and
use the set of ranking scores to rank the set of search result documents.
18 . The ranking system of claim 16 , wherein the first phase of the artificial neural network-based encoder comprises a transformer-based encoder.
19 . The ranking system of claim 18 , wherein the token-based user representation comprises a sequence of classifier tokens.
20 . The ranking system of claim 16 , wherein the second phase of the artificial neural network-based encoder comprises a transformer-based encoder.