# US 2026/0064708 A1 · Ranking item objects

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- Number: US 2026/0064708 A1
- Document ID: US20260064708A1
- Status: Published application
- Kind: A1
- Application number: 19/314,576
- Filed: 2025-08-29
- Published: 2026-03-05
- Earliest claimed: 2024-08-30
- Assignees: Walmart Apollo, LLC
- Applicants: Walmart Apollo, LLC
- Inventors: Yijie Sun; Chittaranjan Tripathy; Asheem Sinha; Nita Himanshu Malani; He Wen
- Web page: https://patentgrep.com/patent/US20260064708A1

## CPC

- G06F 16/248: Presentation of query results
- G06Q 30/0603: {Catalogue creation or management}
- G06F: ELECTRIC DIGITAL DATA PROCESSING (computer systems based on specific computational models G06N)
- G06Q: INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR

## Abstract

Examples relate to ranking item objects based on a query. An example includes receiving a query including a plurality of tokens, selecting at least one or more item objects including item object titles, ranking the one or more item objects using a ranking module, scoring each respective selected item object title and the query using a discriminative text module, receiving the scores for at least two categories for the one or more item object titles and the query, and applying a trained machine learning model to the query, the titles of each of the one or more item objects and the feature data for each respective item object of the one or more item objects, such that the trained machine learning model outputs an evaluation of the scores of the one or more item objects selected for the query.

## Claims

### Claim 1

A system, comprising:
a processor; and
a non-transitory memory having instructions stored thereon that when executed by the processor, cause the processor to:
receive a query including a plurality of tokens;
select a plurality of item objects based on the query, wherein each item object of the plurality of item objects has a corresponding title;
obtain feature data of the plurality of item objects;
rank the plurality of item objects to generate a ranked list of item objects based at least by:
determining a plurality of text similarity metrics,
generating, for each item object having the corresponding title, similarity scores based on the plurality of text similarity metrics, respectively, wherein each of the similarity scores indicates a text similarity between the corresponding title and the query based on a respective one of the plurality of text similarity metrics, and
executing a machine learning model to the similarity scores of the plurality of item objects based on the feature data of the plurality of item objects to generate the ranked list of item objects; and
provide the ranked list of item objects in response to the query.

### Claim 2 (depends on 1)

The system of claim 1, wherein the feature data of the plurality of item objects comprises at least one of:
product metadata including price, ratings, and availability for each of the plurality of item objects;
engagement data including click through rate (CTR), add to cart (ATC) rate and order rate (OR) for each item object, each leaf category of item objects, or each item brand; or
query understanding signals including brand extraction, department classification, and category classification of the plurality of item objects.

### Claim 3 (depends on 1)

The system of claim 1, wherein generating the similarity scores comprises:
performing a plurality of natural language processes on the query and the corresponding title to create a modified query and a modified title, wherein the plurality of natural language processes comprise: tokenization, normalization, lemmatization, accent removal, and stop word removal; and
generating each of the similarity scores to represent a text similarity between the modified query and the modified title based on a respective one of the plurality of text similarity metrics.

### Claim 4 (depends on 1)

The system of claim 1, wherein the plurality of text similarity metrics comprises at least one of:
a difference of token count between the corresponding title and the query;
a difference of string length between the corresponding title and the query;
a per character Levenshtein distance between the corresponding title and the query;
a matching contiguity score based on indices of matched tokens between the corresponding title and the query; or
a starting index of the matched tokens in the corresponding title.

### Claim 5 (depends on 1)

The system of claim 1, wherein the plurality of text similarity metrics comprises an asymmetrical matching similarity based on:
N-grams constructed from characters, tokens, or words in the corresponding title and the query; or
a Monge-Elkan similarity between the corresponding title and the query.

### Claim 6 (depends on 1)

The system of claim 1, wherein the plurality of text similarity metrics comprises an exact matching similarity based on at least one of:
a number of matching tokens between the corresponding title and the query;
a percentage of tokens matched in the corresponding title; or
a percentage of tokens matched in the query.

### Claim 7 (depends on 1)

The system of claim 1, wherein the plurality of text similarity metrics comprises a normalized TF-IDF score based on:
a Boolean term frequency for each token in the query;
an inverse document frequency for each token in the query; and
a cosine normalization performed based on a length of the corresponding title.

### Claim 8 (depends on 1)

The system of claim 1, wherein the instructions, when executed by the processor, further cause the processor to train the machine learning model based at least by:
generating a training dataset including training features and training targets based on historical search data and item object data, wherein:
the training features include text similarity features, product metadata, engagement data, and query understanding signals,
the training targets are generated based on a weighted combination of click through rate (CTR), add to cart (ATC) rate and order rate (OR) within a recent time period;
training the machine learning model using the training dataset with a pointwise objective function; and
finetuning at least one model hyperparameter of the machine learning model using an offline evaluation score.

### Claim 9

A computer implemented method, comprising:
receiving a query including a plurality of tokens;
selecting a plurality of item objects based on the query, wherein each item object of the plurality of item objects has a corresponding title;
obtaining feature data of the plurality of item objects;
ranking the plurality of item objects to generate a ranked list of item objects based at least by:
determining a plurality of text similarity metrics,
generating, for each item object having the corresponding title, similarity scores based on the plurality of text similarity metrics, respectively, wherein each of the similarity scores indicates a text similarity between the corresponding title and the query based on a respective one of the plurality of text similarity metrics, and
executing a machine learning model to the similarity scores of the plurality of item objects based on the feature data of the plurality of item objects to generate the ranked list of item objects; and
providing the ranked list of item objects in response to the query.

### Claim 10 (depends on 9)

The computer implemented method of claim 9, wherein the feature data of the plurality of item objects comprises at least one of:
product metadata including price, ratings, and availability for each of the plurality of item objects;
engagement data including click through rate (CTR), add to cart (ATC) rate and order rate (OR) for each item object, each leaf category of item objects, or each item brand; or
query understanding signals including brand extraction, department classification, and category classification of the plurality of item objects.

### Claim 11 (depends on 9)

The computer implemented method of claim 9, wherein generating the similarity scores comprises:
performing a plurality of natural language processes on the query and the corresponding title to create a modified query and a modified title, wherein the plurality of natural language processes comprise: tokenization, normalization, lemmatization, accent removal, and stop word removal; and
generating each of the similarity scores to represent a text similarity between the modified query and the modified title based on a respective one of the plurality of text similarity metrics.

### Claim 12 (depends on 9)

The computer implemented method of claim 9, wherein the plurality of text similarity metrics comprises at least one of:
a difference of token count between the corresponding title and the query;
a difference of string length between the corresponding title and the query;
a per character Levenshtein distance between the corresponding title and the query;
a matching contiguity score based on indices of matched tokens between the corresponding title and the query; or
a starting index of the matched tokens in the corresponding title.

### Claim 13 (depends on 9)

The computer implemented method of claim 9, wherein the plurality of text similarity metrics comprises an asymmetrical matching similarity based on:
N-grams constructed from characters, tokens, or words in the corresponding title and the query; or
a Monge-Elkan similarity between the corresponding title and the query.

### Claim 14 (depends on 9)

The computer implemented method of claim 9, wherein the plurality of text similarity metrics comprises an exact matching similarity based on at least one of:
a number of matching tokens between the corresponding title and the query;
a percentage of tokens matched in the corresponding title; or
a percentage of tokens matched in the query.

### Claim 15 (depends on 9)

The computer implemented method of claim 9, wherein the plurality of text similarity metrics comprises a normalized TF-IDF score based on:
a Boolean term frequency for each token in the query;
an inverse document frequency for each token in the query; and
a cosine normalization performed based on a length of the corresponding title.

### Claim 16 (depends on 9)

The computer implemented method of claim 9, further comprising training the machine learning model based at least by:
generating a training dataset including training features and training targets based on historical search data and item object data, wherein:
the training features include text similarity features, product metadata, engagement data, and query understanding signals,
the training targets are generated based on a weighted combination of click through rate (CTR), add to cart (ATC) rate and order rate (OR) within a recent time period;
training the machine learning model using the training dataset with a pointwise objective function; and
finetuning at least one model hyperparameter of the machine learning model using an offline evaluation score.

### Claim 17

A non-transitory computer-readable storage medium comprising executable instructions that, when executed by one or more processors of a computing device, cause the one or more processors to:
receive a query including a plurality of tokens;
select a plurality of item objects based on the query, wherein each item object of the plurality of item objects has a corresponding title;
obtain feature data of the plurality of item objects;
rank the plurality of item objects to generate a ranked list of item objects based at least by:
determining a plurality of text similarity metrics,
generating, for each item object having the corresponding title, similarity scores based on the plurality of text similarity metrics, respectively, wherein each of the similarity scores indicates a text similarity between the corresponding title and the query based on a respective one of the plurality of text similarity metrics, and
executing a machine learning model to the similarity scores of the plurality of item objects based on the feature data of the plurality of item objects to generate the ranked list of item objects; and
provide the ranked list of item objects in response to the query.

### Claim 18 (depends on 17)

The non-transitory computer-readable storage medium of claim 17, wherein the feature data of the plurality of item objects comprises at least one of:
product metadata including price, ratings, and availability for each of the plurality of item objects;
engagement data including click through rate (CTR), add to cart (ATC) rate and order rate (OR) for each item object, each leaf category of item objects, or each item brand; or
query understanding signals including brand extraction, department classification, and category classification of the plurality of item objects.

### Claim 19 (depends on 17)

The non-transitory computer-readable storage medium of claim 17, wherein the plurality of text similarity metrics comprises at least one of:
a difference of token count between the corresponding title and the query;
a difference of string length between the corresponding title and the query;
a per character Levenshtein distance between the corresponding title and the query;
a matching contiguity score based on indices of matched tokens between the corresponding title and the query;
a starting index of the matched tokens in the corresponding title;
an asymmetrical matching similarity between the corresponding title and the query;
an exact matching similarity between the corresponding title and the query; or
a normalized TF-IDF score.

### Claim 20 (depends on 17)

The non-transitory computer-readable storage medium of claim 17, wherein the executable instructions, when executed by the one or more processors, further cause the one or more processors to train the machine learning model based at least by:
generating a training dataset including training features and training targets based on historical search data and item object data, wherein:
the training features include text similarity features, product metadata, engagement data, and query understanding signals,
the training targets are generated based on a weighted combination of click through rate (CTR), add to cart (ATC) rate and order rate (OR) within a recent time period;
training the machine learning model using the training dataset with a pointwise objective function; and
finetuning at least one model hyperparameter of the machine learning model using an offline evaluation score.