Abstract:
An image associated with a location and depicting the current weather conditions at the location is served to the user along with the current weather data of the location. If an image for the location that aptly depicts the weather conditions as indicated by the weather data cannot be identified, the geographical area associated with the location is expanded to search for appropriate images from an expanded image pool. The expansion can continue in one or more steps until a threshold is reached beyond which the geographical area my no longer be expanded. If no images are retrieved upon reaching the threshold, an image reflecting the weather condition is selected from a fallback image set for presentation with the weather data.
Abstract:
A content item categorizer system retrieves content items from Internet sources. If a retrieved content item includes sufficient information for traditional categorization methods, then the system assigns one or more categories to the content item using such traditional methods. The system creates a metadata model, based on information about traditionally-categorized content items, that maps at least hashtags from the content items to one or more content categories. When the system retrieves a sparse-info item that does not include sufficient information for traditional categorization, the system applies the metadata model to categorize the content item using at least hashtags in the sparse-info item. The metadata model may also include information indicating mappings between categories and coincidence of hashtags and additional content item attributes. Also, the metadata model may provide information for categorizing sparse-info items based on multiple hashtags in the sparse-info item metadata.
Abstract:
Stabilization of content displays in a wearable device when the wearer is in motion is disclosed. The acceleration and rotation forces acting on the wearable device are obtained. The forces acting on the body part bearing the wearable device are modeled. One or more offsets to counteract the forces are determined based on the models. The offsets are applied to the content display shown on the wearable device.
Abstract:
Users may receive content (e.g., an email, an app interface, a website, a social media post, etc.) comprising a first advertisement. The first advertisement may be evaluated to extract advertisement features (e.g., an item, an item price, a supplemental term of the first advertisement, etc.). An advertisement opportunity may be offered to a second advertiser through an advertiser exchange interface. The second advertiser may generate an advertisement bid to show a second advertisement for the item. Responsive to the advertisement bid being more favorable to the user than the first advertisement (e.g., a lower price, the item with enhanced features for a similar price, etc.), the first advertisement and the second advertisement may be presented with the content to the user.
Abstract:
In one embodiment, a current context of a mobile device may be ascertained, where the current context includes an indication of a last application opened via the mobile device, wherein the last application opened is one of a plurality of applications installed on the mobile device. A probability, for each of the plurality of applications, that a user of the mobile device will use the corresponding application under the current context may be determined, where the probability for at least a portion of the plurality of applications is determined by applying a computer-generated model to the current context, wherein the computer-generated model is associated with the mobile device. One or more of the plurality of the applications may be identified based, at least in part, upon the probability, for each one of the plurality of applications, that the user of the mobile device will use the corresponding application.
Abstract:
The present teaching relates to entity linking. In one example, a text string is received. The text string is segmented to obtain a segmentation with a set of one or more segments of the text string. A set of entities are identified, with respect to the one or more segments, from a plurality of entities as linked to the one or more segments. The identifying is in accordance with a probabilistic model based on surface form information associated with the plurality of entities.
Abstract:
Method, system, and programs for providing content recommendation are disclosed. A first set of candidate content items may be generated based on a user profile, and a second set of candidate items may be generated based on the likelihood that the user will click a corresponding candidate content item in the second set. The candidate content items in the first and second sets may be ranked together using a learning model and presented to the user as content recommendations based on their rankings. The likelihood that the user will click a given candidate content item in the second set may be estimated based on similarities between the given content item and content items related to the given content item. Such a similarity may be computed based on activities performed by users who have viewed both the given content item and a related content item.
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.