Abstract:
Described herein are techniques and systems for online ad campaign tuning using proportional-integral-derivative (PID) controllers, such as proportional (P) controllers and proportional-integral (PI) controllers. Also, described herein are techniques and systems for shortening a learning/tuning phase of a PID controller used for optimizing an online ad campaign.
Abstract:
Briefly, embodiments of methods and/or systems of generating preference indices for contiguous portions of digital images are disclosed. For one embodiment, as an example, parameters of a neural network may be developed to generate object labels for digital images. The developed parameters may be transferred to a neural network utilized to generate signal sample value levels corresponding to preference indices for contiguous portions of digital images.
Abstract:
Described herein are example systems and operations for enhancing response prediction and bidding decision making. A feature recommendation controller may include a factorization machine that generates a set of combinations of contextual and advertiser features yielding high expected response rates. A bidding controller may implement a multi-arm bandit system that uses Thompson sampling to select an optimal one of the feature combinations that corresponds to a highest expected response rate. The bidding controller may compare the corresponding highest expected response rate with a threshold response rate associated with a pacing rate to determine whether to place a bid for a received ad request.
Abstract:
Briefly, embodiments of methods and/or systems of generating preference indices for contiguous portions of digital images are disclosed. For one embodiment, as an example, parameters of a neural network may be developed to generate object labels for digital images. The developed parameters may be transferred to a neural network utilized to generate signal sample value levels corresponding to preference indices for contiguous portions of digital images.
Abstract:
The technologies described herein serve contextually relevant advertisements under a guaranteed advertisement campaign. A publisher retrieves a guaranteed advertisement campaign related to a webpage available for serving an advertisement, and identifies a set of advertisements relating to the guaranteed advertisement campaign. Advertisement selecting circuitry of the publisher determines whether an advertisement that is contextually relevant to content published at the webpage is present in the set of advertisements. If there is no contextually relevant advertisement in the set of advertisements, the advertisement selecting circuitry selects an alternative advertisement from the set of advertisements that minimizes an under-delivery risk related to the guaranteed advertisement campaign. If there is a contextually relevant advertisement in the set of advertisements, the advertisement selecting circuitry selects the contextually relevant advertisement. Then, the publisher provides the selected advertisement to a client device.
Abstract:
Users may consume and/or share information through various types of content items. For example, user may post a family photo through a social network, create a running blog through a microblogging service, etc. Because users may be overwhelmed by the amount of available content items, it may be advantageous to recommend content items, such as blogs to follow, to users. Accordingly, inductive matrix completion is used to evaluate user interactions with content items (e.g., a user following a blog), content item features (e.g., text and/or images of a blog is evaluated to identify a topic of the blog), and/or user features (e.g., a user liking or reblogging a blog, user demographics, user interests, etc.) to determine whether to recommend a content item to a user. Additionally, graph proximity is used to recommend content items based upon weights of edges connecting user nodes to content item nodes within a directed graph.
Abstract:
Described herein are techniques and systems for online ad campaign pacing. The techniques described herein use budget allocation along with the estimations of bids and response rates. With use of budget allocation, the techniques can use budget pacing to enhance impressions and maximize desired responses, such as desired click-through rates. These techniques focus on enhancing pacing and performance of ad campaigns, such as enhancing performance across distinct and/or unified online ad marketplaces. These techniques are especially useful in the context of a demand-side platform (DSP). In some examples, the techniques assume that impression supply is much larger than advertiser demand for impressions of their ads, so such techniques focus on selecting high performing inventory of ad space. Yet, with such a focus, a smooth or consistent delivery of ads over time is used.
Abstract:
Briefly, embodiments of methods and/or systems of generating preference indices for contiguous portions of digital images are disclosed. For one embodiment, as an example, parameters of a neural network may be developed to generate object labels for digital images. The developed parameters may be transferred to a neural network utilized to generate signal sample value levels corresponding to preference indices for contiguous portions of digital images.
Abstract:
A computer system implementing a method for ad realization prediction may be configured to receive a plurality of target realization factors associated with a target ad display opportunity; determine a reference realization probability score of the target ad display opportunity based on a global reference realization probability distribution associated with an ad display realization probability decision tree; using the reference realization probability score, determine an ad realization probability score of the target ad display opportunity according to a piecewise calibrated realization probability function; and return the ad realization probability score.