Abstract: Images can be used to determine the size of an article of clothing matching the physical characteristics of a user. An image can include an article of clothing known to fit well or a representation of a body part, which can comprise the body part itself. Images can also include a reference item. Various techniques are used to determine one or more perspective distortions in the image based on analysis of the reference item and information known about the reference item. The one or more perspective distortions can be accounted for to determine physical characteristics of the article of clothing or the body part associated with the article of clothing. The physical characteristics can be used to determine the size of an article of clothing matching, or corresponding to, the physical characteristics.

Abstract: Various approaches provide for determining the selection and/or ranking of items to display based at least in part upon the probabilities of a user being interested in those items. The probabilities can be based on profile information of a user. The profile information can include a three-dimensional virtual model of the user. When a search for content is received, the information for the three-dimensional virtual model can be used to determine a set of items. For each item, a matching score quantifying a visual appreciation can be determined based on how well an item virtually “fits” the three-dimensional virtual model. Based on the matching score, the selection and/or ranking of items to display can be determined. In some situations, the items can be shown to appear to be worn by the three-dimensional virtual model. The user can purchase items, cause item information to be presented to other users or sent to other devices, and/or perform other such actions.

Abstract: Disclosed are various embodiments for a navigation element. An insertion point is identified in a ranking of search results. A navigation element is included in the search results. A selection of the navigation element modifies a constraint used to generate the search result.

Abstract: The selection of content to present to a user can be based at least in part upon probabilities of the user selecting to view more information and/or entering into a transaction with respect to instances of the content. For example, user behavior with respect to various queries provided through a content provider can be determined in order to calculate a probability that a user was searching for a particular image. The user behavior can include historical action data, such as information that indicates images associated with an action (e.g., selected, purchased, etc.) in response to a particular image search. The historical action data can be analyzed to generate an index that indicates a likelihood that the search was intended for a particular image. Once an image query is received, items of interest can be determined using the index, and those images and associated content can be presented to the user.

Abstract: Video content can be analyzed to identify particular sections of the video content. Speech to text or similar techniques can be used to obtain a transcription of the video content. The transcription can be indexed (e.g., timestamped) to the video content. Information describing how users are interacting with or consuming the video content (e.g., social media information, viewing history data, etc.) can be collected and used to identify the particular sections. Once the particular sections have been identified, other services can be provided. For example, custom trailers and summaries of the video content can be generated based on the identified sections. Additionally, the video content can be augmented to include additional information relevant to the particular sections, such as production information, actor information, or other information. The additional information can be added so as not to interfere with the important sections.

Abstract: Embodiments described herein provide images representing a set of search results based on diversity between results of the search query. Images associated with a set of visually diverse items can be provided to provide a sample of items matching the search query across multiple types of categories. For example, search results can be grouped into types of categories and images from each of the types of categories can be grouped into subsets of visually related images (across one or more different visual attributes). A set of diverse representative images can be selected by taking at least one image from each of the groups of visually related images. The set of representative and diverse images can be displayed to provide an interesting, visually diverse, and aesthetically pleasing set of images to a user.

Abstract: Video content can be analyzed to identify particular sections of the video content. Speech to text or similar techniques can be used to obtain a transcription of the video content. The transcription can be indexed (e.g., timestamped) to the video content. Information describing how users are interacting with or consuming the video content (e.g., social media information, viewing history data, etc.) can be collected and used to identify the particular sections. Once the particular sections have been identified, other services can be provided. For example, custom trailers and summaries of the video content can be generated based on the identified sections. Additionally, the video content can be augmented to include additional information relevant to the particular sections, such as production information, actor information, or other information. The additional information can be added so as not to interfere with the important sections.

Abstract: Techniques to determine the three-dimensional structure of wearable items are disclosed. A scanning device can project waves at a wearable item and receive reflections of the waves. In embodiments, the scanning device projects light pulses and detects reflections of the light pulses. Depth measurements from the scanning device to the wearable item can be used to determine points corresponding to locations on the wearable item. A point cloud can be generated of the points, which can convey the three-dimensional structure of the wearable item. In embodiments, another wearable item can be determined to have a corresponding three-dimensional structure based on point cloud comparisons. The second wearable item can be suggested to a user as a wearable item having an appropriate fit for the user.

Abstract: Video content can be analyzed to identify particular sections of the video content. Speech to text or similar techniques can be used to obtain a transcription of the video content. The transcription can be indexed (e.g., timestamped) to the video content. Information describing how users are interacting with or consuming the video content (e.g., social media information, viewing history data, etc.) can be collected and used to identify the particular sections. Once the particular sections have been identified, other services can be provided. For example, custom trailers and summaries of the video content can be generated based on the identified sections. Additionally, the video content can be augmented to include additional information relevant to the particular sections, such as production information, actor information, or other information. The additional information can be added so as not to interfere with the important sections.

Abstract: Disclosed are various embodiments for a navigation element. An insertion point is identified in a ranking of search results. A navigation element is included in the search results. A selection of the navigation element modifies a constraint used to generate the search result.

Abstract: The present invention is directed towards systems and methods for providing dynamic search results based upon historical data through the use of one or more widgets. The method of the present invention comprises receiving a request for content from a client and generating one or more widgets for providing search result content. A display profile is applied to the one or more widgets and the one or more widgets are combined with static search results to form a search result page that is provided to a requesting client.

Abstract: A system for determining predictive models associated with online advertising can include a communications interface, a processor, and a display. The communications interface can be configured to receive a partial dataset. The partial dataset may include user information. The processor can be communicatively coupled to the communications interface and configured to identify the partial dataset. The processor can also be configured to determine a first predictive model corresponding to at least part of the partial dataset and a second predictive model by combining a probability distribution with the first predictive model. The display can be communicatively coupled to the processor and configured to display the second predictive model.

Abstract: A method for combining multiple probability of click models in an online advertising system into a combined predictive model, the method commencing by receiving a feature set slice (e.g. corresponding to demographics or taxonomies or clusters), and using the sliced data for training multiple slice-wise predictive models. The trained slice-wise predictive models are combined by overlaying a weighted distribution model over the trained slice-wise predictive models. The combined predictive model then is used in predicting the probability of a click given a query-advertisement pair in online advertising. The method can flexibly receive slice specifications, and can overlay any one or more of a variety of distribution models, such as a linear combination or a log-linear combination.

Abstract: A computer-implemented method and system for selecting a subject advertisement in a sponsored search system based on a user's commercial intent (pertaining to the subject advertisement), using techniques for determining intent-driven clicks from a historical database. The method includes steps for aggregating a training model dataset wherein the training model dataset contains a selected history of clicks. Then, selecting from the training model dataset, a clicked slate (further selection of clicks), the clicked slate comprising a set of clicked ads, and calculating an intent-driven click feedback value for the subject advertisement. The method includes techniques for selecting a clicked slate using features corresponding to clicks received within a particular time period (the time period determined statically or dynamically).

Abstract: The subject matter disclosed herein relates to maintaining a history of user interaction data within a sliding window, where the sliding window may be sized based at least in part on a quantification of such user interaction.

Abstract: Sponsored search advertising utilizes a click probability as one factor in selecting and ranking advertisements that are displayed with search results. The probability of click may also be referred to as a predicted click-through rate (“CTR”) that may be multiplied by an advertiser's bid for a particular advertisement to rank the display of advertisements. An accurate prediction of the click probability improves the potential revenue that is generated by advertisements in a pay per click system. Other advertising systems may benefit from an accurate and reliable estimate for an advertisement's probability of click in different environments and scenarios.

Abstract: A method of facilitating the endorsement of products through Internet advertisements accepts a bid for an endorsement, enables communication, associated with the bid, between an advertiser and a potential endorser, and serves an endorsement associated with the bid. In one implementation, the endorsement is displayed together with a symbol verifying the endorser.

Abstract: A computer-implemented method and system for selecting a subject advertisement in a sponsored search system based on a user's commercial intent (pertaining to the subject advertisement), using techniques for determining intent-driven clicks from a historical database. The method includes steps for aggregating a training model dataset wherein the training model dataset contains a selected history of clicks. Then, selecting from the training model dataset, a clicked slate (further selection of clicks), the clicked slate comprising a set of clicked ads, and calculating an intent-driven click feedback value for the subject advertisement. The method includes techniques for selecting a clicked slate using features corresponding to clicks received within a particular time period (the time period determined statically or dynamically).

Abstract: A method for combining multiple probability of click models in an online advertising system into a combined predictive model, the method commencing by receiving a feature set slice (e.g. corresponding to demographics or taxonomies or clusters), and using the sliced data for training multiple slice-wise predictive models. The trained slice-wise predictive models are combined by overlaying a weighted distribution model over the trained slice-wise predictive models. The combined predictive model then is used in predicting the probability of a click given a query-advertisement pair in online advertising. The method can flexibly receive slice specifications, and can overlay any one or more of a variety of distribution models, such as a linear combination or a log-linear combination.

Abstract: The present invention is directed towards systems and methods for providing dynamic search results based upon historical data through the use of one or more widgets. The method of the present invention comprises receiving a request for content from a client and generating one or more widgets for providing search result content. A display profile is applied to the one or more widgets and the one or more widgets are combined with static search results to form a search result page that is provided to a requesting client.