Abstract: An advertising exchange server for context-contingent targeting of online advertisements may include a processor, memory, a communication interface, and a database saved in system storage to store advertisements saved in relation to advertising attributes. The processor may be configured to work with the communication interface to provide an option for an advertiser to express a targeting profile using a freeform mixture of logical operations including a plurality of targeting attributes, the freeform mixture of logical operations excluding solely a series of conjunctive combinations. The processor may receive, and store in memory, the expressed targeting profile and at least one associated advertisement from the advertiser. The processor may receive and match an advertising opportunity with one or more targeting attributes of the targeting profile.

Abstract: A nano-particle coated genuine leather floor including genuine leather, a leather pigment, and nano-powder. The nano-powder is a mixture of nano-silver and sub-nanometer SiO2 with a weight ratio of 1:33 to 1:5. The nano-powder is added into the leather pigment and the resulting mixture is coated on genuine leather. The nano-particle coated genuine leather floor has high degree of wear resistance, moisture resistance, good antibacterial property, and long service lifetime.

Abstract: Disclosed are apparatus and methods for detecting whether a video is adult or non-adult. In certain embodiments, a learning system is operable to generate one or more models for adult video detection. The model is generated based on a large set of known videos that have been defined as adult or non-adult. Adult detection is then based on this adult detection model. This adult detection model may be applied to selected key frames of an unknown video. In certain implementations, these key frames can be selected from the frames of the unknown video. Each key frame may generally correspond to a frame that contains key portions that are likely relevant for detecting pornographic or adult aspects of the unknown video. By way of examples, key frames may include moving objects, skin, people, etc. In alternative embodiments, a video is not divided into key frames and all frames are analyzed by a learning system to generate a model, as well as by an adult detection system based on such model.

Abstract: Systems and Methods for multi-modal or multimedia image retrieval are provided. Automatic image annotation is achieved based on a probabilistic semantic model in which visual features and textual words are connected via a hidden layer comprising the semantic concepts to be discovered, to explicitly exploit the synergy between the two modalities. The association of visual features and textual words is determined in a Bayesian framework to provide confidence of the association. A hidden concept layer which connects the visual feature(s) and the words is discovered by fitting a generative model to the training image and annotation words. An Expectation-Maximization (EM) based iterative learning procedure determines the conditional probabilities of the visual features and the textual words given a hidden concept class. Based on the discovered hidden concept layer and the corresponding conditional probabilities, the image annotation and the text-to-image retrieval are performed using the Bayesian framework.

Abstract: Techniques are provided for advertiser bid forecasting in online advertising, including display advertising. Methods are provided in which key targeting-related user segments are determined from bidding statistics. A feature set is extracted from an impression opportunity, based at least in part on the bidding statistics. A gradient boosting descent tree technique is utilized in determining an initial bid forecasting result. A linear regression-based model is used in post-tuning to arrive at a post-tuned result. For short-term forecasting, this may be the final result. For long-term forecasting, a hybrid approach may be utilized with further processing including utilization of a publisher-specific model.

Abstract: Disclosed are apparatus and methods for facilitating the ranking of web objects. The method includes automatically adjusting a plurality of weight values for a plurality of parameters for inputting into a ranking engine that is adapted to rank a plurality of web objects based on such weight values and their corresponding parameters. The adjusted weight values are provided to the ranking engine so as to generate a ranked set of web objects based on such adjusted weight values and their corresponding parameters, as well as a particular query. A relevance metric (e.g., that quantifies or qualifies how relevant the generated ranked set of web objects are for the particular query) is determined. The method includes automatically repeating the operations of adjusting the weight values, providing the adjusted weight values to the ranking engine, and determining a relevance metric until the relevance metric reaches an optimized level, which corresponds to an optimized set of weight values.

Abstract: The present invention provides a method and system for ranking and selecting advertisements based on relevancy, click feedback and click over expected click (COEC) data. Advertisements may be described as contextual, page-embedded advertisements appearing on publisher websites. The method and system includes storing page-advertisement relevancy features in a vector space model and historical impression and click features in a click feedback model and analyzing data in the vector space model and click feedback model. The method and system further includes storing empirical click-through data in a serving log and analyzing data therein. The method and system then generates a regression model based on the analyzed data, which is stored in a regression storage module. The method and system receives requests for advertisement content from client devices, selects a plurality of candidate advertisements based on the generated regression model and provides a plurality of advertisements to a client device.

Abstract: The present invention provides methods and systems for use in bid optimization in connection with advertisement serving impression opportunities available in an auction-based online advertising exchange. Methods are presented in which, based in part on historical advertisement performance information, a Kalman filter-based model is used in forecasting performance of a set of possible advertisement impressions served over a future period of time. Forecasted performance information is used in determining an optimized bid in connection with an available opportunity. A similarity function, including non-linearly determined feature weighting, can be used in determining most similar forecasted impressions to the available opportunity.

Abstract: The present invention provides methods and systems for use in bid optimization in connection with advertisement serving impression opportunities available in an auction-based online advertising exchange. Methods are presented in which, based in part on historical advertisement performance information, a Kalman filter-based model is used in forecasting performance of a set of possible advertisement impressions served over a future period of time. Forecasted performance information is used in determining an optimized bid in connection with an available opportunity.

Abstract: Techniques are described herein for automatically evaluating the quality of digital images based on one or more color characteristics of the images. In some embodiments, a quality metric that indicates the likelihood that the digital images convey semantics is generated based on color characteristics of the digital images. The quality metric may be used, for example, to determine which keyframe to use to make a thumbnail to represent video data. In some embodiments, feature values are generated for an image based on color characteristics of the image, and the feature values are assigned to bins. In such embodiments, the quality metric may be generated to indicate how uniform the distribution of feature values is among the bins.

Abstract: The present invention provides for improving the search relevance of a search results page by including a perceived relevance factor. The system, device and method monitors user selection of elements in the search results page, where these selections indicate relevance of the element compared with the original search request. A perceived relevance factor for the element is then determined based on probabilistic-based computations accounting for the element, which may include a description, a thumbnail and/or meta data, and the position of the element on the search results page. Thereby, for future searches and search results page generation, relevance factors may be calculated based on various factors, including the element attribute based relevant scores and the perceived relevance factor.

Abstract: The present invention is directed towards systems and methods for generating one or more channels for the organization of content items. A method according to one embodiment comprises selecting a content item and one or more items of metadata for the selected content item. A determination is made to determine if the selected content item should be associated with a given channel on the basis of the metadata. A channel is generated on the basis of the determination, with the selected content item organized in association with the channel.

Abstract: Systems and Methods for multi-modal or multimedia image retrieval are provided. Automatic image annotation is achieved based on a probabilistic semantic model in which visual features and textual words are connected via a hidden layer comprising the semantic concepts to be discovered, to explicitly exploit the synergy between the two modalities. The association of visual features and textual words is determined in a Bayesian framework to provide confidence of the association. A hidden concept layer which connects the visual feature(s) and the words is discovered by fitting a generative model to the training image and annotation words. An Expectation-Maximization (EM) based iterative learning procedure determines the conditional probabilities of the visual features and the textual words given a hidden concept class. Based on the discovered hidden concept layer and the corresponding conditional probabilities, the image annotation and the text-to-image retrieval are performed using the Bayesian framework.

Abstract: An improved system and method for learning a ranking model that optimizes a ranking evaluation metric for ranking search results of a search query is provided. An optimized nDCG ranking model that optimizes an approximation of an average nDCG ranking evaluation metric may be generated from training data through an iterative boosting method for learning to more accurately rank a list of search results for a query. A combination of weak ranking classifiers may be iteratively learned that optimize an approximation of an average nDCG ranking evaluation metric for the training data by training a weak ranking classifier at each iteration for each document in the training data with a computed weight and assigned class label, and then updating the optimized nDCG ranking model by adding the weak ranking classifier with a combination weight to the optimized nDCG ranking model.

Abstract: Disclosed are apparatus and methods for facilitating the ranking of web objects. The method includes automatically adjusting a plurality of weight values for a plurality of parameters for inputting into a ranking engine that is adapted to rank a plurality of web objects based on such weight values and their corresponding parameters. The adjusted weight values are provided to the ranking engine so as to generate a ranked set of web objects based on such adjusted weight values and their corresponding parameters, as well as a particular query. A relevance metric (e.g., that quantifies or qualifies how relevant the generated ranked set of web objects are for the particular query) is determined. The method includes automatically repeating the operations of adjusting the weight values, providing the adjusted weight values to the ranking engine, and determining a relevance metric until the relevance metric reaches an optimized level, which corresponds to an optimized set of weight values.

Abstract: Disclosed are apparatus and methods for detecting whether a video is adult or non-adult. In certain embodiments, a learning system is operable to generate one or more models for adult video detection. The model is generated based on a large set of known videos that have been defined as adult or non-adult. Adult detection is then based on this adult detection model. This adult detection model may be applied to selected key frames of an unknown video. In certain implementations, these key frames can be selected from the frames of the unknown video. Each key frame may generally correspond to a frame that contains key portions that are likely relevant for detecting pornographic or adult aspects of the unknown video. By way of examples, key frames may include moving objects, skin, people, etc. In alternative embodiments, a video is not divided into key frames and all frames are analyzed by a learning system to generate a model, as well as by an adult detection system based on such model.

Abstract: The subject matter disclosed herein relates to generating a fingerprint for identifying electronic video files based at least in part on color correlograms.

Abstract: A system for generating subphrase queries. The system includes a sequence label modeling engine and a regression modeling engine. The sequence label modeling engine generates a plurality of subphrase queries by indexing through each token in a search phrase and labeling each token based on an association to other tokens in the search phrase. The regression modeling engine scores each subphrase query at least partially on the association according to a scoring model. The regression modeling engine identifies the subphrase query with the highest score which may then be used for identifying a sponsored search list or a web search item.

Abstract: The present invention provides for improving the search relevance of a search results page by including a perceived relevance factor. The system, device and method monitors user selection of elements in the search results page, where these selections indicate relevance of the element compared with the original search request. A perceived relevance factor for the element is then determined based on probabilistic-based computations accounting for the element, which may include a description, a thumbnail and/or meta data, and the position of the element on the search results page. Thereby, for future searches and search results page generation, relevance factors may be calculated based on various factors, including the element attribute based relevant scores and the perceived relevance factor.

Abstract: Techniques are described herein for automatically evaluating the quality of digital images based on one or more color characteristics of the images. In some embodiments, a quality metric that indicates the likelihood that the digital images convey semantics is generated based on color characteristics of the digital images. The quality metric may be used, for example, to determine which keyframe to use to make a thumbnail to represent video data. In some embodiments, feature values are generated for an image based on color characteristics of the image, and the feature values are assigned to bins. In such embodiments, the quality metric may be generated to indicate how uniform the distribution of feature values is among the bins.