Ebay, Incorporated

Yahoo! Inc.

Amobee, Inc.

Synopsys, Inc.

Turn Inc.

Ali Dasdan

Sanjay Pundlkrao Ghatare

Naren M Chittar

Santanu Kolay

Sandip Namdeo Gaikwad

Anjan Goswami

Sung Hwan Chung

Panagiotis Papadimitriou

Kayhan Kucukcakar

Feroze P Taraporevala

Sumit Rangwala

Christos Koufogiannakis

Ruey-Lung Hsiao

Ali Jalali

Chris Drome

Hung-Chih Yang

Emre Salman

Peter Foldes

Kuang-chih Lee

Wentong Li

Burkay Birant Orten

Halim Damerdji

Lawrence Lo

Shahriar Shariat

Kayhan Küçükçakar

Shiyong Cheng

Andrey Svirsky

A metadata extraction machine accesses an image that depicts an item. The item depicted in the image may have an attribute that describes a characteristic of the item and an attribute descriptor that corresponds to the attribute of the item and specifies a value of the attribute. The metadata extraction machine performs an analysis of the image. The analysis may include identifying the attribute descriptor corresponding to the attribute based on image segmentation of the image. The metadata extraction machine transmits a communication to a device of a user based on the identifying of the attribute descriptor corresponding to the attribute of the item depicted in the image.

Identifying product metadata from an item image

Image-based features may be significantly correlated with click-through rates of images that depict a product, which may provide a more formal basis for the informal notion that good quality images will result in better click-through rates, as compared to poor quality images. Accordingly, an image assessment machine is configured to analyze image-based features to improve click-through rates for shopping search applications (e.g., a product search engine). Moreover, the image assessment machine may rank search results based on image quality factors and may notify sellers about low quality images. This may have the effect of improving the brand value for an online shopping website and accordingly have a positive long-term impact on the online shopping website.

Image quality assessment to merchandise an item

Techniques and mechanisms described herein facilitate adaptive fuzzy fallback stratified sampling. According to various embodiments, an actual or estimated minimum vertex cover of a feature dependency graph representing a dataset may be determined. The dataset may include a plurality of feature vectors and a plurality of features. Each feature vector may include a plurality of feature values that correspond with the features. The feature dependency graph may represent a plurality of conditional dependencies between the features. The minimum vertex cover may designate a subset of the features for strata selection. The feature vectors may be partitioned into a plurality of strata based on the designated subset of features. Each stratum may include one or more of the feature vectors. Each feature vector may be assigned to a corresponding stratum based on the values of the designated subset of features for the feature vector.

Adaptive fuzzy fallback stratified sampling for fast reporting and forecasting

A bid determination platform receives specifications for advertisements, each associated with a model configured to adjust a parameter value of a bid based on different combinations of user and/or media content characteristics associated with different advertisement requests. The platform also receives an advertisement request for placement of an advertisement on an advertisement space of a publisher's media content that has been requested by a user, and the advertisement request is associated with a set of user and media content profile data. For each of the advertisements, all or a subset of the set of user and/or media content profile data is input to each model so as to determine an associated parameter value. Bids for each advertisement are determined, with at least some bids being based on the associated parameter value. An optimum bid is sent with a location of its advertisement to the advertisement request sender.

Methods and systems for modeling campaign goal adjustment

At a bid determination platform, an initial sequence having an initial order of software steps for filtering advertisements in response to receiving an advertisement bid request is selected. Until a trigger event occurs, the initial sequence of software steps is implemented in the initial order in response to receiving advertisement bid requests. Implementing the initial sequence comprises automatically tracking a failure (or success) metric and resource requirement metric for each of the software steps. After the trigger event occurs, a first optimum sequence of the software steps is automatically selected in a first optimum order so as to optimize a total resource usage for execution of the software steps. Selecting the first optimum sequence of the software steps in the first optimum order is based on the tracked failure (or success) metric and resource requirement metric for each of the software steps during implementation of the initial sequence.

Dynamic ordering of online advertisement software steps

Systems, methods, and devices are disclosed herein for spend control in online advertising. Systems include a first plurality of advertisement servers included in a first data center, and a second plurality of advertisement servers included in a second data center. Systems include a performance data aggregator configured to receive performance data characterizing bids placed by the first plurality of advertisement servers and the second plurality of advertisement servers. Systems include a spend control analyzer configured to generate spend control data objects based on an estimate of bidding activity associated with the first data center and bidding activity associated with the second data center. Spend control data objects are capable of configuring advertisement servers to perform advertisement operations within determined budgetary constraints. The spend control analyzer is configured to provide the plurality of spend control data objects to the first plurality of advertisement servers and the second plurality of advertisement servers.

Systems, methods, and devices for spend control in online advertising

Methods and apparatus for identifying on-line users for advertisement or content targeting are disclosed. Historical user data is obtained in association with user identifiers, which have been unambiguously determined. The historical user data includes event data for one or more on-line user events that have occurred for each user identifier. The historical user data also specify fingerprint vectors of characteristic values that are each associated with specific ones of the user identifiers. A current one of the fingerprint vectors that is ambiguously associated with two or more user identifiers is received. A first user identifier is selected from the associated two or more user identifiers of the current fingerprint vector based on the event data of the historical user data. The selected first user identifier is provided to a server configured to provide advertisement or content based on user profile data that is obtainable for such selected first user identifier.

Methods and apparatus for identifying a cookie-less user

Methods and apparatus for finding detecting unique on-line users for advertisement or content targeting are disclosed. A bid request with initial fingerprint context data for a user and for content that is requested by such user is received at a bid management system, and the bid request is received with or without a user identifier. At the bid management system, an optimum bid for the bid request is determined based on the initial fingerprint context data for the user and the content. The optimum bid with a reference to an advertisement and a script for obtaining additional fingerprint context data from a device of the user is sent from the bid management system. The additional fingerprint context data is received and a unique user identifier is assigned in association with a combination of the initial and additional fingerprint context data.

Methods and apparatus for identifying unique users for on-line advertising

Embodiments of the invention present an approach to conversion rate estimation which relies on using past performance observations along user, publisher, and advertiser data hierarchies. More specifically, embodiments of the invention model the conversion event at different select hierarchical levels with separate binomial distributions and estimate the distribution parameters individually. It is shown how to combine these individual estimators using logistic regression to identify conversion events accurately. Embodiments of the invention can also handle many practical issues, such as data imbalance, missing data, and output probability calibration, which render this estimation problem more difficult for a real-world implementation of the approach.

Estimating conversion rate in display advertising from past performance data

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San Jose, CA, United States of America

Ali Dasdan