Amazon Technologies, Inc.

Yahoo! Inc.

Srinivasan Hanumantha Rao Sengamedu

Anirban Majumder

Rajeev Ramnarain Rastogi

Prakash Mandayam Comar

Ashwin Devendrappa Tengli

Rupesh R Mehta

Amit Madaan

Charu Tiwari

Pankaj Gulhane

Vinayak Shripad Puranik

S R Jeyashankher

At an artificial intelligence-based service, an indication of a similarity group of items of a data stream is obtained. A subset of the stream items is to be included in an ordered collection and presented via an interface which allows one or more types of interactions. Using a first data set which includes interaction records of items in the similarity group, one or more machine learning models are trained to predict a relevance metric associated with a particular type of interaction. A predicted value of the relevance metric is obtained from a trained version of a model and stored.

Artificial intelligence system for relevance analysis of data stream items using similarity groups and attributes

The arrangement and selection of digital content to present to a user can be based at least in part upon the relevance of content, e.g., the probabilities of the user selecting to view more information and/or otherwise engaging with instances of the content. Features of items associated with content presented to a user can be determined, as well as the user's response to items with those features. This data is used to determine the probability of the user viewing and/or otherwise engaging an item having specific values for at least some of those features. The probabilities are combined with abandonment scores associated with a feed position of an interface to ensure that content displayed is optimized for engagement. The items can be ranked based on this engagement determination, in order to select which item content to display and the appropriate arrangement of that content.

Shaping a relevance profile for engagement

The effect of position bias on content performance can be determined in part using artificial intelligence and computer learning. Performance data can include the frequency with which an action, such as a purchase, occurs in response to an instance of content being displayed. The content can be associated with a node at a lowest level of an offering hierarchy, and performance data from the various levels can be rolled up to higher level nodes to obtain sufficient data to for accurate position bias determinations. A bias model can be trained using the data from the various levels, where the training determines weightings for the bias determinations of each level. Once the position bias for an offer is determined, a normalized performance value can be obtained that does not include the effects of the bias. The normalized values can be used to select and place content based on actual performance.

Correcting for location bias using artificial intelligence

An example of a method includes determining features of a first type for a web page of a plurality of web pages. The method also includes electronically determining a plurality of rules for an attribute of the first web page, wherein the plurality of rules are determined based on features of the first type. The method also includes electronically identifying a first rule, from the plurality of rules, which satisfies a first predefined criterion. The first predefined criteria include at least one of a first threshold for a precision parameter, a second threshold for a support parameter, a third threshold for a distance parameter and a fourth threshold for a recall parameter. The method further includes storing the first rule to enable extraction of value of the attribute from a second web page.

Method and system for processing and learning rules for extracting information from incoming web pages

A method includes generating, electronically, one or more matching patterns for one or more pairs of attribute values. Each pair includes two attribute values. The two attribute values include a first attribute value from a first record and a second attribute value from a second record. The first attribute value and the second attribute value satisfy a first criterion. Further, the method includes identifying, electronically, matching segment between the first attribute value and the second attribute value of a first pair. The method also includes repeating identifying for each pair. Moreover, the method includes computing a similarity score for the first pair using one of the first pair and the matching segment based on the one or more matching patterns and matching segments of the one or more pairs satisfying a second criterion. The method also includes repeating computing for each pair.

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Bengaluru, India

Srinivasan Hanumantha Rao Sengamedu