Amazon Technologies, Inc.

Sungro Yoon

Soo-Min Pantel

Daniel Bruce Lloyd

Opeyemi Akanji

Pranav Varia

Vivek Kumar

Charles Lawrence Scott

Xiaonan Zhao

Sneha Rajana

Ben Allison

A search results user interface enables a user conducting a search of an electronic catalog to identify and access browse nodes (item categories) associated with the search query when the search query is determined to be broad. In one embodiment, if the catalog items that are responsive to the search query fall within more than a threshold number of browse nodes, a search results page is presented that provides functionality for the user to view the browse nodes associated with the search query and to navigate to associated browse node pages.

Categorical exploration facilitation responsive to broad search queries

Devices and techniques are generally described for generating personalized search query suggestions. In some examples, a first search query entered by a user into a search field may be determined. A first keyword associated with the first search query may be determined based at least in part on past interactions of the user with content. In various examples, first string data comprising at least a portion of the first search query and the first keyword may be generated. A data store may be searched using the first string data. A first suggested search string associated with the first string data may be determined. In some examples, the first suggested search string may be displayed on a display in association with the search field.

Personalized query suggestion

Disclosed are one or more embodiments for a unique and personalized experience for a user interacting with an electronic commerce site by identifying user-preferred item attributes using supervised machine learning and presenting items to the user in an arrangement that is based on the identified item attributes. A shopping mission is determined according to user interactions with an electronic commerce site. The shopping mission is applied to an attribute prediction model that is trained to detect user-preferred item attributes for items included the item category and estimate a likelihood that an item containing a particular attribute will be purchased or interacted with during the interactions with the electronic commerce site.

User-preferred item attributes

Systems and methods are provided for electronic catalog user experience improvements based on extracted item attributes. An example method includes obtaining information identifying items viewed during a user browsing session, the items being included in an electronic catalog, and the electronic catalog being organized according to a hierarchy, with the hierarchy comprising a plurality of categories. Information identifying attributes associated with the identified items is identified, and the attributes are ranked according to attribute relevance score. Items are determined for recommendation to the user based on the ranked attribute relevance scores, with the determined items being selected from items included in the electronic catalog which are also associated with a same category as the identified items.

Automated identification of item attributes relevant to a browsing session

The present disclosure is directed to dynamically determining the breadth of user intent reflected in a given search query. For example, historical user behaviors are used to construct a mapping that links particular search queries (or their constituent keyword strings) to items that users interact with during a predetermined window of time after submitting the query. The number of categories or browse nodes associated with these items (or a most popular subset thereof) is used to determine whether the search intent is broad or narrow. For broad searches, the present disclosure causes output of a specialized user interface that facilitates browsing of items in the browse nodes or categories, in place of the typical search results user interface.

Growing community of inventors

Redman, WA, United States of America

Sungro Yoon