Amazon Technologies, Inc.

A9.com, Inc.

Vishwanathan Swaminathan

Sriram Srinivasan

Houssam Nassif

Vijai Mohan

Santosh Rajagopalan

Narayanan Sadagopan

Trishul Amit Madhukar Chilimbi

Hyokun Yun

Yi Liu

Jian Liu

Tao Hu

Choon Hui Teo

Iman Keivanloo

Sankalp Nayak

Anand Mahadeva Iyer

Weicong Ding

Dinesh Govindaraj

Daniel Hill

Mitchell Howard Goodman

Devices and techniques are generally described for search using a micro-partitioned catalog. In some examples, a search system can determine a plurality of items in an online catalog. The search system can generate a similarity graph based on vector representations of the plurality of items and determine an item as a representative item for a cluster of items. The search system can receive a query and determine the cluster of items as a candidate cluster based on a comparison between the query and the representative item. The search system can determine a candidate item of the cluster of items based on a comparison between the query and the cluster of items.

Micro-partitioning based search

An optimal combination of content items may be determined and served to increase the likelihood of a predefined user interaction. A user can request content which may include an entity (such as a web page, document, advertisement, and the like) that has multiple components through which content items may be provided. For example, a web page may include multiple slots where content items may be displayed on the web page. Each component may be associated with multiple possible content items, resulting in many combinations of layouts for an entity. A content server may determine which layout to provide using a content selection model that is weighted based on a likelihood of groupings of content items resulting in a user interaction that satisfies a success condition (e.g., selecting a hyperlink, selecting a content item, initiating a transaction, etc.).

Artificial intelligence system for optimizing network-accessible content

Systems and methods are provided for selecting content components to include in portions of a content page under various constraints and according to various effectiveness considerations. Organizing the content components may include considering the expected user engagement with the content components, while simultaneously endeavoring to comply with certain content constraints imposed on the system. The content constraints may be complied with to the greatest extent possible before the expected user engagement with the page is adversely impacted to an unacceptable degree. The content constraints may be imposed across an entire set of content pages, such that only the content pages in the aggregate are expected to comply with a global constraint. This allows for maximizing the expected user engagement while assembling a content page without unduly constraining each content page, where those global constraints may be known to reduce expected user engagement in some instances.

Optimizing content engagement with imposed content constraints

The arrangement and selection of digital content to present to a user can be based upon criteria such as profitability or interest to a user. The selection can also be made to ensure that a diversity of item content is presented. The selection can utilize various rules or policies for diversity at the category level or item feature level, among other such options. In addition to selection diversity, the placement of item content displayed can satisfy various diversity criteria in order to ensure diversity of display as well.

Content selection algorithms

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Saratoga, CA, United States of America

Vishwanathan Swaminathan