# US 11,113,741 B2 · Arranging content on a user interface of a computing device

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- Number: US 11,113,741 B2
- Document ID: US11113741B2
- Status: Granted
- Kind: B2
- Application number: 16/179,972
- Filed: 2018-11-04
- Published: 2020-05-07
- Granted: 2021-09-07
- Earliest claimed: 2018-11-04
- Assignees: International Business Machines Corporation
- Applicants: International Business Machines Corporation
- Inventors: Yada Zhu; Ajay Deshpande; Brian Quanz; Xuan Liu; Pin-Yu Chen
- Web page: https://patentgrep.com/patent/US11113741B2

## CPC

- G06Q 30/0631: {Recommending goods or services}
- G06F 16/358: Browsing; Visualisation therefor
- G06F 16/9024: {Graphs; Linked lists (G06F16/9027 takes precedence)}
- G06Q 30/0201: Market modelling; Market analysis; Collecting market 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
- G06F: ELECTRIC DIGITAL DATA PROCESSING (computer systems based on specific computational models G06N)

## Abstract

A computer implemented method and system of presenting content on a display of a computing device is provided. Historical data including data of a plurality of customers and data of a plurality of products is received. A hybrid graph is created. The hybrid graph includes one or more customer nodes and product nodes. Between each two customers, a customer weight factor is applied. Between each two products, a product weight factor is applied. One or more products related to a seed product are identified. For each identified related product, a return affinity score towards the requestor customer is determined. A representation of the related products is sent to be displayed on the computing device of the requestor customer, based on the affinity score.

## Claims

### Claim 1

A computing device comprising:
a processor;
a network interface coupled to the processor to enable communication over a network;
a storage device for content and programming coupled to the processor;
a program stored in the storage device, wherein an execution of the program by the processor configures the computing device to perform acts comprising:
receiving historical data comprising (i) data of a plurality of customers and (ii) data of a plurality of products;
creating a hybrid graph based on the received historical data, the hybrid graph comprising:
one or more customer nodes and one or more product nodes;
for each product purchased, a directed edge between a corresponding customer node and a product node; and
for each product returned, a non-directed edge between a corresponding customer node and a product node; and
between each two customers of the plurality of customers, applying a customer weight factor on the hybrid graph, wherein the customer weight factor indicates a degree of similarity between the two customers;
between each two products of the plurality of products, applying a product weight factor on the hybrid graph, wherein the product weight factor indicates a degree of similarity between the two products;
receiving a request for a seed product from a computing device of a requestor customer;
identifying one or more products related to the seed product;
for each identified related product, determining a return affinity score towards the requestor customer by performing a random walk analysis initiated from the seed product to find a cluster around the seed product, wherein the return affinity score is a similarity function used to quantify the similarity between the two products and the two customers by evaluating a customer's return attribute probability for the products based on the hybrid graph;
ranking each related product based on their return affinity score; and
sending a representation of the related products to be displayed on the computing device of the requestor customer based on the ranking, where the related products are emphasized and/or suppressed based on the return affinity score.

### Claim 2 (depends on 1)

The computing device of claim 1, wherein the historical data includes, for each of the plurality of customers: a product purchase information, a product return information, and a cost of each product returned.

### Claim 3 (depends on 1)

The computing device of claim 1, wherein the historical data includes, for each of the plurality of products: a product category, a cost, a purchase history, a return history, and a product to product similarity.

### Claim 4 (depends on 3)

The computing device of claim 3, wherein the historical data further includes, for each of the plurality of products, social media information related to the product.

### Claim 5 (depends on 1)

The computing device of claim 1, wherein the hybrid graph further comprises, a non-directed edge between each two customers of the plurality of customers that have a customer similarity that is above a predetermined threshold.

### Claim 6 (depends on 1)

The computing device of claim 1, wherein the customer weight factor indicates a customer similarity based on one or more customer attributes received in the historical data.

### Claim 7 (depends on 1)

The computing device of claim 1, wherein the product weight factor indicates a product similarity based one or more product attributes received in the historical data.

### Claim 8 (depends on 1)

The computing device of claim 1, wherein the representation of the related products does not include related products that have a return affinity that is above a predetermined threshold.

### Claim 9

A non-transitory computer readable storage medium tangibly embodying a computer readable program code having computer readable instructions that, when executed, causes a computer device to carry out a method of presenting content on a user interface of a computing device, the method comprising:
receiving historical data comprising (i) data of a plurality of customers and (ii) data of a plurality of products;
creating a hybrid graph based on the received historical data, the hybrid graph comprising:
one or more customer nodes and one or more product nodes;
for each product purchased, a directed edge between a corresponding customer node and a product node; and
for each product returned, a non-directed edge between a corresponding customer node and a product node; and
between each two customers of the plurality of customers, applying a customer weight factor on the hybrid graph, wherein the customer weight factor indicates a degree of similarity between the two customers;
between each two products of the plurality of products, applying a product weight factor on the hybrid graph, wherein the product weight factor indicates a degree of similarity between the two products;
receiving a request for a seed product from a computing device of a requestor customer;
identifying one or more products related to the seed product;
for each identified related product, determining a return affinity score towards the requestor customer by performing a random walk analysis initiated from the seed product to find a cluster around the seed product, wherein the return affinity score is a similarity function used to quantify the similarity between the two products and the two customers by evaluating a customer's return attribute probability for the products based on the hybrid graph;
ranking each related product based on their return affinity score; and
sending a representation of the related products to be displayed on the computing device of the requestor customer based on the ranking, where the related products are emphasized and/or suppressed based on the return affinity score.

### Claim 10 (depends on 9)

The non-transitory computer readable storage medium of claim 9, wherein the historical data includes, for each of the plurality of customers: a product purchase information, a product return information, and a cost of each product returned.

### Claim 11 (depends on 9)

The non-transitory computer readable storage medium of claim 9, wherein the historical data includes, for each of the plurality of products: a product category, a cost, a purchase history, a return history, and a product to product similarity.

### Claim 12 (depends on 11)

The non-transitory computer readable storage medium of claim 11, wherein the historical data further includes, for each of the plurality of products, social media information related to the product.

### Claim 13 (depends on 9)

The non-transitory computer readable storage medium of claim 9, wherein the hybrid graph further comprises, a non-directed edge between each two customers of the plurality of customers that have a customer similarity that is above a predetermined threshold.

### Claim 14 (depends on 9)

The non-transitory computer readable storage medium of claim 9, wherein:
the customer weight factor indicates a customer similarity based on one or more customer attributes received in the historical data; and
the product weight factor indicates a product similarity based one or more product attributes received in the historical data.

### Claim 15 (depends on 9)

The non-transitory computer readable storage medium of claim 9, wherein the representation of the related products does not include related products that have a return affinity that is above a predetermined threshold.

### Claim 16

A computer implemented method, comprising:
receiving historical data comprising (i) data of a plurality of customers and (ii) data of a plurality of products;
creating a hybrid graph based on the received historical data, the hybrid graph comprising:
one or more customer nodes and one or more product nodes;
for each product purchased, a directed edge between a corresponding customer node and a product node; and
for each product returned, a non-directed edge between a corresponding customer node and a product node; and
between each two customers of the plurality of customers, applying a customer weight factor on the hybrid graph, wherein the customer weight factor indicates a degree of similarity between the two customers;
between each two products of the plurality of products, applying a product weight factor on the hybrid graph, wherein the product weight factor indicates a degree of similarity between the two products;
receiving a request for a seed product from a computing device of a requestor customer;
identifying one or more products related to the seed product;
for each identified related product, determining a return affinity score towards the requestor customer by performing a random walk analysis initiated from the seed product to find a cluster around the seed product, wherein the return affinity score is a similarity function used to quantify the similarity between the two products and the two customers by evaluating a customer's return attribute probability for the products based on the hybrid graph;
ranking each related product based on their return affinity score; and
sending a representation of the related products to be displayed on the computing device of the requestor customer based on the ranking, where the related products are emphasized and/or suppressed based on the return affinity score.

### Claim 17 (depends on 16)

The computer implemented method of claim 16, wherein:
the customer weight factor indicates a customer similarity based on one or more customer attributes received in the historical data; and
the product weight factor indicates a product similarity based one or more product attributes received in the historical data.