# US 2021/0241349 A1 · Automatically determining in real-time a triggering model for personalized recommendations

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- Number: US 2021/0241349 A1
- Document ID: US20210241349A1
- Status: Published application
- Kind: A1
- Application number: 16/779,541
- Filed: 2020-01-31
- Published: 2021-08-05
- Earliest claimed: 2020-01-31
- Assignees: Walmart Apollo, LLC
- Applicants: Walmart Apollo, LLC
- Inventors: Aditya Mantha; Rahul Radhakrishnan Iyer; Shashank Kedia; Shubham Gupta; Praveenkumar Kanumala; Stephen Dean Guo; Kannan Achan
- Web page: https://patentgrep.com/patent/US20210241349A1

## CPC

- G06Q 30/0631: {Recommending goods or services}
- G06N 7/01: Probabilistic graphical models, e.g. probabilistic networks
- G06Q 30/0633: {Managing shopping lists, e.g. compiling or processing purchase lists (shipping orders G06Q10/083; order filling G06Q10/087)}
- G06N 3/044: Recurrent networks, e.g. Hopfield networks
- G06N 3/045: Combinations of networks
- G06N 5/025: Extracting rules from data
- 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
- G06N: COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS

## Abstract

A method including building a recommendation triggering model. The method can include receiving, via a user device of a user through a network, an add-to-cart command associated with an anchor item in a session by the user. The method further can include determining, in real-time after receiving the add-to-cart command, a recommendation for one or more complementary items based at least in part on: (a) the anchor item; and (b) a user profile of the user. The method also can include determining, in real-time after determining the recommendation, a recommendation confidence for the recommendation based at least in part on one or more of: (a) the user profile; (b) the anchor item; (c) the one or more complementary items; or (d) one or more feedbacks from the user associated with one or more prior recommendations in the session. The method additionally can include after determining the recommendation confidence, when the recommendation confidence is positive, transmitting, in real-time through the network, the one or more complementary items to be presented to the user via the user device. The method likewise can include after determining the recommendation confidence, when the recommendation confidence is not positive, refraining from transmitting the one or more complementary items to the user. Other embodiments are disclosed.

## Claims

### Claim 1

A system comprising:
one or more processors; and
one or more non-transitory computer-readable media storing computing instructions configured to run on the one or more processors and perform:
receiving, via a user device of a user through a network, an add-to-cart command associated with an anchor item in a session by the user;
determining, in real-time after receiving the add-to-cart command, a recommendation for one or more complementary items based at least in part on: (a) the anchor item; and (b) a user profile of the user;
determining, in real-time after determining the recommendation, a recommendation confidence for the recommendation based at least in part on one or more of:
the user profile;
the anchor item;
the one or more complementary items; or
one or more feedbacks from the user associated with one or more prior recommendations in the session; and
after determining the recommendation confidence, when the recommendation confidence is positive, transmitting, in real-time through the network, the one or more complementary items to be presented to the user via the user device; and
after determining the recommendation confidence, when the recommendation confidence is not positive, refraining from transmitting the one or more complementary items to the user.

### Claim 2 (depends on 1)

The system in claim 1, wherein the computing instructions are further configured to perform:
prior to determining the recommendation confidence for the recommendation, removing a duplicate item of the one or more complementary items when at least one of:
one or more prior declined recommendations comprise the duplicate item; or
a cart for the user comprises the duplicate item.

### Claim 3 (depends on 1)

The system in claim 1, wherein determining the recommendation confidence further comprises:
determining an approximate entropy for one or more joint probability distributions, each respective joint probability distribution of the one or more joint probability distributions being associated with the user, the anchor item, and each of the one or more complementary items;
after determining the approximate entropy, when the approximate entropy is less than a predetermined uncertainty threshold, the recommendation confidence is positive; and
after determining the approximate entropy, when the approximate entropy is not less than the predetermined uncertainty threshold, the recommendation confidence is negative.

### Claim 4 (depends on 3)

The system in claim 3, wherein:
the user profile comprises a transactional history; and
a respective joint probability distribution for the user, the anchor item, and each of the one or more complementary items is determined based at least in part on one or more of:
a recommendation add-to-cart score associated with one or more of: the transactional history of the user profile, the one or more prior recommendations, or the one or more feedbacks;
an add-to-cart score for the anchor item associated with the user; or
a respective likelihood score for each of the one or more complementary items associated with the anchor item and the user.

### Claim 5 (depends on 4)

The system in claim 4, wherein:
each of the one or more complementary items is associated with one or more respective attributes;
the user profile further comprises one or more user attribute preferences for one or more respective attributes associated with each of the one or more complementary items; and
the respective likelihood score for each of the one or more complementary items is determined based at least in part on the one or more user attribute preferences.

### Claim 6 (depends on 1)

The system in claim 1, wherein:
determining the recommendation further comprises:
determining each of the one or more complementary items based at least in part on one or more of:
one or more complementary item types for an item type of the anchor item;
a respective item-item complementarity score for the each of the one or more complementary items associated with the anchor item; or
a respective user-item compatibility score for the each of the one or more complementary items associated with one or more system users, the one or more system users comprising the user.

### Claim 7 (depends on 6)

The system in claim 6, wherein:
each of the one or more complementary items is associated with a respective co-bought score determined based at least in part on one of:
the respective item-item complementarity score;
the respective user-item compatibility score; or
a respective item-type compatibility score; and
determining the recommendation further comprises:
determining a respective rank for each of the one or more complementary items based on the respective co-bought score.

### Claim 8 (depends on 1)

The system in claim 1, wherein:
determining the recommendation further comprises re-ranking the one or more complementary items based on a respective likelihood score for each of the one or more complementary items associated with the anchor item and the user.

### Claim 9 (depends on 1)

The system in claim 1, wherein the computing instructions are further configured to perform:
prior to determining the recommendation confidence for the recommendation, re-ranking the one or more complementary items by boosting a boosted item of the one or more complementary items, when at least one of:
one or more favorites of the user profile comprises the boosted item; or
one or more promotional items of a retailer comprises the boosted item.

### Claim 10 (depends on 1)

The system in claim 1, wherein:
transmitting the one or more complementary items to be presented to the user via the user device is performed when one or more of:
at a check out by the user;
in real-time after determining the recommendation confidence for the recommendation; or
at a session-change by the system.

### Claim 11

A method being implemented via execution of computing instructions configured to run at one or more processors and stored at one or more non-transitory computer-readable media, the method comprising:
receiving, via a user device of a user through a network, an add-to-cart command associated with an anchor item in a session by the user;
determining, in real-time after receiving the add-to-cart command, a recommendation for one or more complementary items based at least in part on: (a) the anchor item; and (b) a user profile of the use;
determining, in real-time after determining the recommendation, a recommendation confidence for the recommendation based at least in part on one or more of:
the user profile;
the anchor item;
the one or more complementary items; or
one or more feedbacks from the user associated with one or more prior recommendations in the session;
after determining the recommendation confidence, when the recommendation confidence is positive, transmitting, in real-time through the network, the one or more complementary items to be presented to the user via the user device; and
after determining the recommendation confidence, when the recommendation confidence is not positive, refraining from transmitting the one or more complementary items to the user.

### Claim 12 (depends on 11)

The method in claim 11 further comprising:
prior to determining the recommendation confidence for the recommendation, removing a duplicate item of the one or more complementary items when at least one of:
one or more prior declined recommendations comprise the duplicate item; or
a cart for the user comprises the duplicate item.

### Claim 13 (depends on 11)

The method in claim 11, wherein determining the recommendation confidence further comprises:
determining an approximate entropy for one or more joint probability distributions, each respective joint probability distribution of the one or more joint probability distributions being associated with the user, the anchor item, and each of the one or more complementary items;
after determining the approximate entropy, when the approximate entropy is less than a predetermined uncertainty threshold, the recommendation confidence is positive; and
after determining the approximate entropy, when the approximate entropy is not less than the predetermined uncertainty threshold, the recommendation confidence is negative.

### Claim 14 (depends on 13)

The method in claim 13, wherein:
the user profile comprises a transactional history; and
a respective joint probability distribution for the user, the anchor item, and each of the one or more complementary items is determined based at least in part on one or more of:
a recommendation add-to-cart score associated with one or more of the transactional history of the user profile, the one or more prior recommendations, or the one or more feedbacks;
an add-to-cart score for the anchor item associated with the user; or
a respective likelihood score for each of the one or more complementary items associated with the anchor item and the user.

### Claim 15 (depends on 14)

The method in claim 14, wherein:
each of the one or more complementary items is associated with one or more respective attributes;
the user profile further comprises one or more user attribute preferences for one or more respective attributes associated with each of the one or more complementary items; and
the respective likelihood score for each of the one or more complementary items is determined based at least in part on the one or more user attribute preferences.

### Claim 16 (depends on 11)

The method in claim 11, wherein:
determining the recommendation further comprises:
determining each of the one or more complementary items based at least in part on one or more of:
one or more complementary item types for an item type of the anchor item;
a respective item-item complementarity score for the each of the one or more complementary items associated with the anchor item; or
a respective user-item compatibility score for the each of the one or more complementary items associated with one or more system users, the one or more system users comprising the user.

### Claim 17 (depends on 16)

The method in claim 16, wherein:
each of the one or more complementary items is associated with a respective co-bought score determined by one of:
the respective item-item complementarity score;
the respective user-item compatibility score; or
a respective item-type compatibility score; and
determining the recommendation further comprises:
determining a respective rank for each of the one or more complementary items based on the respective co-bought.

### Claim 18 (depends on 11)

The method in claim 11, wherein:
determining the recommendation further comprises re-ranking the one or more complementary items based on a respective likelihood score for each of the one or more complementary items associated with the anchor item and the user.

### Claim 19 (depends on 11)

The method in claim 11 further comprising:
prior to determining the recommendation confidence for the recommendation, re-ranking the one or more complementary items by boosting a boosted item of the one or more complementary items, when at least one of:
one or more favorites of the user profile comprises the boosted item; or
one or more promotional items of a retailer comprises the boosted item.

### Claim 20 (depends on 11)

The method in claim 11, wherein:
transmitting the one or more complementary items to be presented to the user via the user device is performed when one or more of:
at a check out by the user;
in real-time after determining the recommendation confidence for the recommendation; or
at a session-change.