Abstract: The present teaching relates to a method, system, and programming for content personalization. A request is received from a user to obtain a content item. Information indicative of a literacy-level of the user is obtained and the content item to be provided to the user is retrieved. The content item is modified by updating information included in the content item based on the literacy-level of the user to generate an updated content item. The updated content item is provided to the user in response to the request.

Abstract: The present teaching relates to a method, system, and programming for content personalization. A request is received from a user to obtain a content item. Information indicative of a literacy-level of the user is obtained and the content item to be provided to the user is retrieved. The content item is modified by updating information included in the content item based on the literacy-level of the user to generate an updated content item. The updated content item is provided to the user in response to the request.

Abstract: Disclosed are systems and methods for improving interactions with and between computers in electronic messaging and/or providing systems supported by or configured with personal computing devices, servers and/or platforms. The disclosed systems and methods provide systems and methods for generating electronic message filters and for using electronic message filters comprising item category filtering criteria and having an automatically-determined expiration. The discloses systems and methods filter electronic messages using the item category filtering criteria while an electronic message filter remains active as determined using the automatically-determined expiration information.

Abstract: The present teaching relates to learning a model. Supervised training data with samples having feature values and a label is received. Unlabeled data be classified is received having samples with values of the same features. Un-stationary features in the supervised training data are detected based on respective feature values from the supervised training data and the unlabeled data. If un-stationary feature exists, adjusted training data set is created based on the supervised training data and the un-stationary features and used to train a stationary classification model. Otherwise, the supervised training data is used to train the stationary classification model.

Abstract: The present teaching relates to method, system, medium, and implementations for machine learning. A bid is received, from an expert during training, for a training sample with an amount within a level of available bidding currency associated with the expert. The training sample is used for training a model associated with the expert. It is determined whether the expert is among at least one winner selected based on bids from one or more experts. If the expert is among the at least one winner, the training sample is sent to the expert. The at least one winner is selected based on one or more criteria aiming at expert diversification.

Abstract: Disclosed are systems and methods for improving interactions with and between computers in electronic messaging and/or providing systems supported by or configured with personal computing devices, servers and/or platforms. The disclosed systems and methods provide systems and methods for generating electronic message filters and for using electronic message filters comprising item category filtering criteria and having an automatically-determined expiration. The discloses systems and methods filter electronic messages using the item category filtering criteria while an electronic message filter remains active as determined using the automatically-determined expiration information.

Abstract: Disclosed are systems and methods for improving interactions with and between computers in distributional similarity identification using randomized observations. In connection with an intrusion detection system monitoring a computing system, a pair of perturbed sample sets are generating using a pair of real sample set (or real observations) and a pair of random sample sets (of randomly-selected observations), and a similarity measuring representing a level of consistency in user behavior is determined. The systems improve the quality and accuracy of the similarity determination for use in intrusion detection.

Abstract: Disclosed are systems and methods for improving interactions with and between computers in electronic messaging and/or providing systems supported by or configured with personal computing devices, servers and/or platforms. The disclosed systems and methods provide systems and methods for generating electronic message filters and for using electronic message filters comprising item category filtering criteria and having an automatically-determined expiration. The discloses systems and methods filter electronic messages using the item category filtering criteria while an electronic message filter remains active as determined using the automatically-determined expiration information.

Abstract: Disclosed are systems and methods for improving interactions with and between computers in distributional similarity identification using randomized observations. In connection with an intrusion detection system monitoring a computing system, a pair of perturbed sample sets are generating using a pair of real sample set (or real observations) and a pair of random sample sets (of randomly-selected observations), and a similarity measuring representing a level of consistency in user behavior is determined. The systems improve the quality and accuracy of the similarity determination for use in intrusion detection.

Abstract: The present teaching relates to method, system, medium, and implementations for machine learning. A training sample is first received from a source. A prediction is generated according to the training sample and based on one or more parameters associated with a model. A metric characterizing the prediction is also determined. The prediction and the metric are transmitted to the source to facilitate a determination on whether a ground truth label for the training sample is to be provided. When the ground truth label is received from the source, the one or more parameters of the model are updated based on the prediction and the ground truth label.

Abstract: One or more computing devices, systems, and/or methods for mailbox-based coupon display are provided. For example, a plurality of email messages stored in a mailbox of a first email account of a first user may be identified, and may be evaluated to identify a plurality of coupons. The plurality of coupons may be analyzed to determine one or more coupon features. User data and/or activity data associated with the first user may be analyzed to determine one or more user features. A predictive model may be tuned based upon the one or more coupon features and/or the one or more user features. A set of scores may be generated for the plurality of coupons based upon the predictive model. A graphical user interface may be controlled based upon the set of scores to display one or more coupons.

Abstract: The present teaching relates to method, system, medium, and implementations for machine learning. A check is performed on a level of available bidding currency for bidding a training sample that is used to train a model via machine learning. A bid in an amount within the available bidding currency is sent, to a source of the training sample, for the training sample. The training sample is received from the source when the bid is successful. A prediction is then generated in accordance with the training sample based on one or more parameters associated with the model and is sent to the source.

Abstract: The present teaching relates to a method, system, and programming for content personalization. A request is received from a user to obtain a content item. Information indicative of a literacy-level of the user is obtained and the content item to be provided to the user is retrieved. The content item is modified by updating information included in the content item based on the literacy-level of the user to generate an updated content item. The updated content item is provided to the user in response to the request.

Abstract: The present teaching relates to a method, system, and programming for content personalization. A request is received from a user to obtain a content item. Information indicative of a literacy-level of the user is obtained and the content item to be provided to the user is retrieved. The content item is modified by updating information included in the content item based on the literacy-level of the user to generate an updated content item. The updated content item is provided to the user in response to the request.

Abstract: Disclosed are systems and methods for improving interactions with and between computers in content providing, searching and/or hosting systems supported by or configured with devices, servers and/or platforms. The disclosed systems and methods provide a novel framework that automatically generates and recommends Internet memes for inclusion within electronically communicated messages. As a user is drafting a message, the input text of the drafted message is analyzed and a set of Internet (or visual) memes are compiled and presented to the user within the drafting interface. Upon selection of at least one of the memes, the message is modified by automatically removing the text from the message and replacing the now removed text with the selected meme.

Abstract: Disclosed are systems and methods for improving interactions with and between computers in content providing, searching and/or hosting systems supported by or configured with devices, servers and/or platforms. The disclosed systems and methods provide a novel framework that automatically generates and recommends Internet memes for inclusion within electronically communicated messages. As a user is drafting a message, the input text of the drafted message is analyzed and a set of Internet (or visual) memes are compiled and presented to the user within the drafting interface. Upon selection of at least one of the memes, the message is modified by automatically removing the text from the message and replacing the now removed text with the selected meme.

Abstract: The present teaching relates to method, system, medium, and implementations for machine learning. A training sample is sent to an expert for training a model representative of the expert. A prediction is received, which is generated by the expert in accordance with the training sample and based on one or more parameters associated with the model. A metric with respect to the prediction characterizing the prediction received from the expert is analyzed. When the metric satisfies a first criterion, a ground truth label associated with the training sample is sent to the expert to facilitate the training.

Abstract: The present teaching relates to method, system, medium, and implementations for machine learning. A bid is received, from an expert during training, for a training sample with an amount within a level of available bidding currency associated with the expert. The training sample is used for training a model associated with the expert. It is determined whether the expert is among at least one winner selected based on bids from one or more experts. If the expert is among the at least one winner, the training sample is sent to the expert. The at least one winner is selected based on one or more criteria aiming at expert diversification.

Abstract: The present teaching relates to method, system, medium, and implementations for machine learning. A check is performed on a level of available bidding currency for bidding a training sample that is used to train a model via machine learning. A bid in an amount within the available bidding currency is sent, to a source of the training sample, for the training sample. The training sample is received from the source when the bid is successful. A prediction is then generated in accordance with the training sample based on one or more parameters associated with the model and is sent to the source.

Abstract: The present teaching relates to method, system, medium, and implementations for machine learning. A training sample is first received from a source. A prediction is generated according to the training sample and based on one or more parameters associated with a model. A metric characterizing the prediction is also determined. The prediction and the metric are transmitted to the source to facilitate a determination on whether a ground truth label for the training sample is to be provided. When the ground truth label is received from the source, the one or more parameters of the model are updated based on the prediction and the ground truth label.