Complementary product recommendation systems

Patent metadata

Field	Value
Publication number	US-2019370879-A1
Application number	US-201916417443-A
Country	US
Kind code	A1
Filing date	May 20, 2019
Priority date	May 18, 2018
Publication date	Dec 5, 2019
Grant date	—

Abstract

Generally, the present disclosure relates to methods and systems for generating complementary product recommendations. In some example aspects, human-identified complementarity of a subset of products can be used to train a neural network, which is in turn used to generate pairwise complementarity values for items. Based on such values, complementary items can be identified.

First claim

1 . A method of generating a set of one or more complementary items associated with an item, the method comprising:
receiving data identifying one or more pairs of complementary items from within an item collection, the one or more pairs of complementary items being a subset of all items included in the item collection; generating a model of item complementarity based at least in part on the one or more pairs of complementary items and item descriptions of each of the items included in the item collection; receiving a selection of a seed item from within the item collection; and in response to selection of the seed item, identifying one or more modeled complementary items; wherein at least one of the seed item or the one or more modeled complementary items is not included in the one or more pairs of complementary items included in the received data.

2 . The method of claim 1 , further comprising:
generating complementarity scores between the seed item and each of a plurality of items within the item collection; and based on a complementarity score between the seed item and an item from among the plurality of items being above a predetermined complementarity threshold, including the item from among the plurality of items within the one or more modeled complementary items.

3 . The method of claim 1 , wherein generating a model of item complementarity comprises:
generating text embeddings from item descriptions of each of the items included in the item collection; and training a Siamese model using the text embeddings from the item descriptions of the one or more pairs of complementary items.

4 . The method of claim 3 , wherein training the Siamese model further comprises assigning a predetermined complementarity score to each of the one or more pairs of complementary items.

5 . The method of claim 4 , wherein training the Siamese model further includes using text embeddings from item descriptions of one or more pairs of known non-complementary items.

6 . The method of claim 5 , wherein training the Siamese model further comprises assigning a second predetermined complementarity score to each of the one or more pairs of non-complementary items that is different from the predetermined complementarity score.

7 . The method of claim 3 , wherein the Siamese model utilizes a Contrastive Loss function according to the following:
L ( W ,( Y,X 1 ,X 2 ) i )= Y i ( D W i ) 2 +(1− Y i )(max(0,1− D W i )) 2 ,
wherein (Y, X 1 , X 2 )′ is a labeled sample pair, Y i is a label value of the sample pair, and D i is a Euclidean distance between the pair of points.

8 . The method of claim 7 , wherein the Y i label value is a selected value of 0 for known non-complementary items and 1 for known complementary items.

9 . The method of claim 1 , further comprising outputting a listing of the one or more modeled complementary items.

10 . The method of claim 9 , wherein outputting the listing comprises providing the listing to a retail web server.

11 . The method of claim 9 , wherein outputting the listing comprises generating a user interface including a display of the one or more modeled complementary items and the seed item from a retail web site server.

12 . A system for generating a set of one or more complementary items associated with an item, the system comprising:
a computing system comprising a processor operatively connected to a memory, the memory storing computing instructions which, when executed by the processor, cause the computing system to: generate a model of item complementarity based at least in part on data identifying one or more pairs of complementary items and item descriptions of each of the items included in an item collection, the one or more pairs of complementary items being a subset of all items included in the item collection; receive a selection of a seed item from within the item collection; and in response to selection of the seed item, identify one or more modeled complementary items; wherein at least one of the seed item or the one or more modeled complementary items is not included in the one or more pairs of complementary items included in the received data.

13 . The system of claim 12 , wherein the computing system comprises a plurality of computing devices including a retail web site server.

14 . The system of claim 13 , wherein the instructions comprise a complementary products generation application accessible from the retail web site server via an application programming interface (API).

15 . The system of claim 14 , wherein the complementary products generation application includes instructions which cause the computing system to:
generate complementarity scores between the seed item and each of a plurality of items within the item collection; and based on a complementarity score between the seed item and an item from among the plurality of items being above a predetermined complementarity threshold, include the item from among the plurality of items within the one or more modeled complementary items.

16 . The system of claim 14 , wherein the retail web site server is further configured to generate a user interface displaying the one or more modeled complementary items.

17 . The system of claim 14 , wherein the retail web site server is configured to provide, to the complementary products generation application via the API, an item identifier of the seed item.

18 . The system of claim 15 , wherein generating a model of item complementarity comprises:
generating text embeddings from item descriptions of each of the items included in the item collection; and training a Siamese model using the text embeddings from the item descriptions of the one or more pairs of complementary items.

19 . A system for generating a set of one or more complementary items associated with an item, the system comprising:
a computing system comprising a plurality of computing devices, the plurality of computing devices including a retail web site server and a complementary item computing device, the computing system storing instruction which, when executed cause the computing system to: generate a model of item complementarity based at least in part on data identifying one or more pairs of complementary items and item descriptions of each of the items included in an item collection, the one or more pairs of complementary items being a subset of all items included in an item collection; receive a user input identifying a seed item from within the item collection; provide the selection of the seed item to the complementary item computing device via an API; in response to receiving of the seed item, identify, at the complementary item computing device, one or more modeled complementary items, at least one of the seed item or the one or more modeled complementary items not being included in the one or more pairs of complementary items included in the received data; and generate, at the retail web site server, a user interface displaying the one or more modeled complementary items.

20 . The system of claim 19 , wherein the user interface further displays the seed item.

Assignees

• Target Brands Inc

Inventors

• Bhattacharjee Kasturi

Classifications

• G06N3/08

  Learning methods · CPC title

• G06N3/045

  Combinations of networks · CPC title

• G06Q30/0631 Primary

  Recommending goods or services · CPC title

• G06Q30/0641

  utilising user interfaces specially adapted for shopping · CPC title

• G06N20/00

  Machine learning · CPC title