Abstract: The present teaching relates to a method, system, and programming for providing content. A plurality of content items and publication information related thereto are obtained. For each of the plurality of content items, one or more topics are determined in accordance with a model. The related publication information associated with each content item is analyzed to identify at least one source of a plurality of sources that published the content item. A distribution is generated of each of the plurality of content items with respect to the plurality of sources and the one or more topics of the content item, and a bias of a source with respect to publishing content is identified based on the distributions of the plurality of content items.

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 providing content. A plurality of content items and publication information related thereto are obtained. For each of the plurality of content items, one or more topics are determined in accordance with a model. The related publication information associated with each content item is analyzed to identify at least one source of a plurality of sources that published the content item. A distribution is generated of each of the plurality of content items with respect to the plurality of sources and the one or more topics of the content item, and a bias of a source with respect to publishing content is identified based on the distributions of the plurality of content items.

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: Methods and devices for assessing the quality of user-generated content are described. In one embodiment, a method is disclosed for measuring the quality of a user-generated answer to a question by combining various factors, including question-answer surface word vector similarity, question-answer explicit semantic analysis vector similarity, answer-answer explicit sematic analysis vector similarity, query performance predictor, sentiment analysis, textual analysis of the answer, and reputation of the answerer. The method uses a learning procedure to determine the best algorithm for measuring the overall quality of the answer based on these factors.

Abstract: One or more computing devices, systems, and/or methods for generating a set of tips for an entity are provided. For example, users may create user generated content describing an entity, such as a user review for a consumer good, a location, an event, etc. Because a user may be unable to read and digest all of the user reviews for the entity, the user may merely read a few user reviews, and thus miss out on useful information. Accordingly, tip templates, indicative of how tips are linguistically/grammatically constructed, are applied to the user reviews to automatically extract a set of tips for the entity (e.g., “make sure to bring a rain jacket”). The set of tips may be filtered to remove undesirable tips, ranked based upon usefulness, and/or diversified to remove redundant tips. In this way, a set of useful tips may be provided to the user.

Abstract: Disclosed are systems and methods for improving interactions with and between computers in content searching, generating, hosting and/or providing systems supported by or configured with personal computing devices, servers and/or platforms. The systems interact to identify and retrieve data within or across platforms, which can be used to improve the quality of data used in processing interactions between or among processors in such systems. The disclosure provides a novel, computerized framework for automatically identifying and recommending socially-engaging photos to their creators for sharing. Execution of the disclosed systems and methods turns a tedious manual chore into an automated, software-driven process.

Abstract: Disclosed are systems and methods for improving interactions with and between computers in content searching, generating, hosting and/or providing systems supported by or configured with personal computing devices, servers and/or platforms. The systems interact to identify and retrieve data within or across platforms, which can be used to improve the quality of data used in processing interactions between or among processors in such systems. A novel, computerized framework for automatically identifying and recommending socially-engaging photos to their creators for sharing is provided. Execution of the disclosed framework turns a tedious manual chore into an automated, software-driven process.

Abstract: Systems, devices, and methods for the scalable and distributed detection of road anomaly events are disclosed. In one embodiment, the system comprises a sensor monitor configured to: receive data regarding road anomaly events detected by a plurality of sensors through a communication network; analyze the received data; identify a subset road anomaly events based on analyzing the received data; determine the size of the subset of road anomaly events exceeds a pre-defined threshold; and in response to determining the size of the subset of road anomaly events exceeds a pre-defined threshold, perform at least one action.

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: 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 a method, system, and programming for providing content. A plurality of content items and publication information related thereto are obtained. For each of the plurality of content items, one or more topics are determined in accordance with a model. The related publication information associated with each content item is analyzed to identify at least one source of a plurality of sources that published the content item. A distribution is generated of each of the plurality of content items with respect to the plurality of sources and the one or more topics of the content item, and a bias of a source with respect to publishing content is identified based on the distributions of the plurality of content items.

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: Methods and devices for assessing the quality of user-generated content are described. In one embodiment, a method is disclosed for measuring the quality of a user-generated answer to a question by combining various factors, including question-answer surface word vector similarity, question-answer explicit semantic analysis vector similarity, answer-answer explicit sematic analysis vector similarity, query performance predictor, sentiment analysis, textual analysis of the answer, and reputation of the answerer. The method uses a learning procedure to determine the best algorithm for measuring the overall quality of the answer based on these factors.

Abstract: The present teaching relates to a method and system for selecting content. Upon receiving a request with an indication of a first piece of content for selecting one or more pieces of second content to be presented together with the first piece of content, a plurality of pieces of candidate second content are identified. At least one sentiment feature associated with the first piece of content is determined and the one or more pieces of second content are selected from the plurality of pieces of candidate second content based on the at least one sentiment feature of the first piece of content so that the one or more pieces of second content are emotionally compatible with the first piece of content. The one or more pieces of second content are sent in response to the request.

Abstract: Disclosed herein is a mechanism, which may be in a community-driven system, to assist a user, or member of the community, to articulate a request, such as a question, that is likely to receive a response, e.g., an answer to a question posed, from the user community. A machine learning approach may be used to generate a model, which model may be trained using previously-posted requests, e.g., questions, and response, e.g., answers. The generated model may be used to make a prediction, e.g., to predict a potential number of answers for a given question, which prediction may also have an accompanying confidence score. A prediction generated by the model may be presented to the user as the user is providing the input, e.g., typing a question. Such prediction may be generated for a partial question, e.g., as the question is being typed, as well as a completed question.

Abstract: Methods and devices for assessing the quality of user-generated content are described. In one embodiment, a method is disclosed for measuring the quality of a user-generated answer to a question by combining various factors, including question-answer surface word vector similarity, question-answer explicit semantic analysis vector similarity, answer-answer explicit sematic analysis vector similarity, query performance predictor, sentiment analysis, textual analysis of the answer, and reputation of the answerer. The method uses a learning procedure to determine the best algorithm for measuring the overall quality of the answer based on these factors.

Abstract: One or more computing devices, systems, and/or methods for generating a set of tips for an entity are provided. For example, users may create user generated content describing an entity, such as a user review for a consumer good, a location, an event, etc. Because a user may be unable to read and digest all of the user reviews for the entity, the user may merely read a few user reviews, and thus miss out on useful information. Accordingly, tip templates, indicative of how tips are linguistically/grammatically constructed, are applied to the user reviews to automatically extract a set of tips for the entity (e.g., “make sure to bring a rain jacket”). The set of tips may be filtered to remove undesirable tips, ranked based upon usefulness, and/or diversified to remove redundant tips. In this way, a set of useful tips may be provided to the user.

Abstract: One or more computing devices, systems, and/or methods for generating a set of tips for an entity are provided. For example, users may create user generated content describing an entity, such as a user review for a consumer good, a location, an event, etc. Because a user may be unable to read and digest all of the user reviews for the entity, the user may merely read a few user reviews, and thus miss out on useful information. Accordingly, tip templates, indicative of how tips are linguistically/grammatically constructed, are applied to the user reviews to automatically extract a set of tips for the entity (e.g., “make sure to bring a rain jacket”). The set of tips may be filtered to remove undesirable tips, ranked based upon usefulness, and/or diversified to remove redundant tips. In this way, a set of useful tips may be provided to the user.