Abstract: An experience-aware anomaly processing system and a method for an experience-aware anomaly processing system are provided. The experience-aware anomaly processing system comprises an anomaly detection module configured to receive geographic location data with corresponding time information of a target object, and analyze target object behavior based on the geographic location data with corresponding time information of the target object; a user feedback module configured to receive user feedback from a user and model user feedback behavior when the user receives an alarm message indicating the target object is abnormal; and a decision module configured to receive user setting from the user, and make a detection decision through fusing target object behavior information corresponding to the target object behavior, user feedback behavior information corresponding to the user feedback behavior, and the user setting.

Abstract: A method is provided for recommending a desired func sequence schedule to a user. The method includes obtaining at least one user intention inputted by the user; creating a func sequence including a plurality of funcs for fulfilling the user intention; and determining user preference on scheduling of the plurality of funcs in the func sequence. The method also includes determining a dependency relationship among the plurality of funcs in the func sequence; and determining scheduling constraints to the scheduling of the plurality of funcs in the func sequence. The method further includes, based on the user preference, the dependency relationship, and the scheduling constraints, performing an optimization process to determine a desired schedule for the plurality of funcs in the func sequence to fulfill the user intention; and recommending the func sequence with the desired schedule to the user.

Abstract: A method for an app search engine leveraging user reviews is provided. The method includes receiving an app search query from a user, determining a plurality of relevant apps based on the received app search query, and extracting app descriptions and user reviews associated with the plurality of relevant apps from an app database. The method also includes preprocessing the extracted app descriptions and user reviews of each of the plurality of relevant apps to generate a text corpus and creating a topic-based language model for each of the plurality of relevant apps based on the generated text corpus. Further, the method includes ranking a list of relevant apps using the topic-based language model and providing the ranked app list for the user.

Abstract: A method is provided for providing desired application usage experience to a user on a user device. The method includes automatically converting a plurality of App pages into FUNC descriptors to be stored, wherein each FUNC descriptor includes at least non-functional properties of a FUNC, capability of the FUNC, and an interface of the FUNC to invoke the FUNC. The method also includes obtaining at least one user intention inputted by the user; determining at least one FUNC descriptor from the FUNC descriptors for fulfilling the user intention; recommending to the user with at least one FUNC corresponding to the at least one FUNC descriptor by rendering the at least one FUNC descriptor on the user device; and invoking the at least one FUNC directly from the user device without going through a launching page of an App page providing the at least one FUNC.

Abstract: A method for a conversational recommender system includes receiving at least one user preference from a user, generating an abstract explanation reflecting the at least one user preference and based on the abstract explanation, and retrieving a list of items to be evaluated by the user from an item database. The items on the list have a relevance with the user preference. The method further includes determining an initial ranking order of the items to be evaluated by the user, and ranking the items to be evaluated by the user based on a random walk algorithm and the initial ranking order. Items not ranked are remaining items. The method further includes receiving a user feedback of the ranked item, and re-ranking the remaining items based on the user feedback of the ranked items and the random walk algorithm.

Abstract: A context-aware chatbot method and system are provided. The context-aware chatbot method comprises receiving a user's voice; converting the user's voice to a question to be answered; determining a question type of the question to be answered; generating at least one answer to the question based on a context-aware neural conversation model; validating the answer generated by the context-aware neural conversation model; and delivering the answer validated to the user. The context-aware neural conversation model takes contextual information of the question into consideration, and decomposes the contextual information of the question into a plurality of high dimension vectors.

Abstract: A method for scalable user intent mining is provided. The method includes detecting named entities from a plurality of query logs in a public query log dataset and generating features of the plurality of query logs based on the detected named entities. The method also includes applying a multimodal restricted boltzmann machine (RBM) on the generated features of the plurality of query logs to train a public multimodal RBM and generating a plurality of public query representations. Further, the method includes receiving a search query from a user, determining whether there are a plurality of history queries of the user. When there is no history query, user intent is predicted using the public multimodal RBM. When there are the history queries, the public multimodal RBM is applied on the plurality of history queries to train a personalized multimodal RBM, and the user intent is predicted using the personalized multimodal RBM.

Abstract: An experience-aware anomaly processing system and a method for an experience-aware anomaly processing system are provided. The experience-aware anomaly processing system comprises an anomaly detection module configured to receive geographic location data with corresponding time information of a target object, and analyze target object behavior based on the geographic location data with corresponding time information of the target object; a user feedback module configured to receive user feedback from a user and model user feedback behavior when the user receives an alarm message indicating the target object is abnormal; and a decision module configured to receive user setting from the user, and make a detection decision through fusing target object behavior information corresponding to the target object behavior, user feedback behavior information corresponding to the user feedback behavior, and the user setting.

Abstract: A unified attractiveness prediction method is provided. The method includes receiving a plurality of videos and extracting at least one of metadata and view data from each of the plurality of received videos, wherein the metadata is information for describing video contents, and the view data is a total number of users who watch the video. The method also includes obtaining potential view amounts of the plurality of received videos and calculating impact factor scores of the plurality of received videos based on the potential view amounts, each impact factor score including a numerical score for indicating a degree of effectiveness of a corresponding video. Further, the method includes providing a video with a highest impact factor score based on the calculated impact factor scores.

Abstract: A method for multimodal clue based personalized app function recommendation is provided. The method includes receiving an app search query from a user, obtaining a plurality of real-time clues collected from the user and extracting a plurality of features to represent the collected clues, wherein the plurality of features include structured features and text features. The method also includes generating a joint representation of the multimodal clues based on the plurality of extracted features and creating a logistic regression model based on the joint representation of the multimodal clues. Further, the method includes ranking a list of app functions using the logistic regression model and displaying the ranked app function list for the user.

Abstract: The present invention provides a method for providing augmented product specifications based on user reviews. The method obtains input data of specifications and user reviews on a plurality of products, each specification including at least a pair of a feature and a feature-value of the product. The method concatenates the user reviews of the products to form product-documents, each product-document corresponding to the concatenated user reviews of a product. The method further employs a topic model to process the input data and learn topic distributions in the product-documents and word distributions in topics. The topics include specifications of the products. The topic model is a type of statistical model for discovering topics that occur in a collection of product-documents. Based on the topic model, the method can provide augmented specifications including one or more of relevant sentences of the feature-value, feature importance information, and product-specific words of the product.

Abstract: A method and a non-transitory computer-readable medium having computer program for optimizing a system function-flow of a system capable of performing at least one function and comprising a plurality of components by utilizing application programming interface (API) profiling are provided. The method comprises receiving a request to perform the at least one function of the system from a user; obtaining at least one component required to realize the at least one function requested by the user from the plurality of the components; providing a plurality of APIs to the at least one obtained component to choose from; profiling each of the plurality of APIs to get a performance score of each API; based on the profiled APIs, optimizing the system function-flow of the system; determining corresponding APIs for the optimized system function-flow; and performing the at least one function requested by the user based on the determined APIs.

Abstract: The present disclosure provides a time-value estimation method for sharing environment, including obtaining time-values of a plurality of shared items from a sharing platform. A time-value is a time duration for the shared item to be shared or traded. Further, a plurality of features of the plurality of shared items from the sharing platform may be extracted. The features may include objective-level features related to a specific item and subjective-level features related to an owner of the specific item. A time-value model may be trained to obtain a time-value estimation function based on the time-values of the plurality of shared items and the plurality of features of the plurality of shared items. The method may further include estimating a time-value of an item in the sharing platform based on the plurality of features of the item and the time-value estimation function.

Abstract: A method for a conversational recommender system includes receiving at least one user preference from a user, generating an abstract explanation reflecting the at least one user preference and based on the abstract explanation, and retrieving a list of items to be evaluated by the user from an item database. The items on the list have a relevance with the user preference. The method further includes determining an initial ranking order of the items to be evaluated by the user, and ranking the items to be evaluated by the user based on a random walk algorithm and the initial ranking order. Items not ranked are remaining items. The method further includes receiving a user feedback of the ranked item, and re-ranking the remaining items based on the user feedback of the ranked items and the random walk algorithm.

Abstract: A method is provided for providing desired application usage experience to a user on a user device. The method includes automatically converting a plurality of App pages into FUNC descriptors to be stored, wherein each FUNC descriptor includes at least non-functional properties of a FUNC, capability of the FUNC, and an interface of the FUNC to invoke the FUNC. The method also includes obtaining at least one user intention inputted by the user; determining at least one FUNC descriptor from the FUNC descriptors for fulfilling the user intention; recommending to the user with at least one FUNC corresponding to the at least one FUNC descriptor by rendering the at least one FUNC descriptor on the user device; and invoking the at least one FUNC directly from the user device without going through a launching page of an App page providing the at least one FUNC.

Abstract: The present invention provides a method a method for learning and using relationships among users and apps on a mobile device. The method includes collecting user profile information and app profile information on the mobile device and obtaining ontology knowledge on contexts and relationships. The user profile information is associated with users including one or more owners of the mobile device and one or more people whose information have been accessed on the mobile device. The method further includes generating a network model based on the user profile information, the app profile information and the ontology knowledge. The network model is a heterogeneous information network model that links the users and the apps. Further, information based on the generated network model are outputted, which at least one of a role of a user, a relationship between two apps or app functions, and a recommended apps list.

Abstract: A method is provided for recommending a desired func sequence schedule to a user. The method includes obtaining at least one user intention inputted by the user; creating a func sequence including a plurality of funcs for fulfilling the user intention; and determining user preference on scheduling of the plurality of funcs in the func sequence. The method also includes determining a dependency relationship among the plurality of funcs in the func sequence; and determining scheduling constraints to the scheduling of the plurality of funcs in the func sequence. The method further includes, based on the user preference, the dependency relationship, and the scheduling constraints, performing an optimization process to determine a desired schedule for the plurality of funcs in the func sequence to fulfill the user intention; and recommending the func sequence with the desired schedule to the user.

Abstract: The present invention provides a focal-point based recommendation method and system. The method includes collecting textual information displayed on a screen of the mobile terminal; identifying one or more focal points on the screen from a user; collecting contextual information of the mobile terminal; and suggesting one or more point-of-interests (POIs) to the user based on the textual information, the focal points and the contextual information. Further, when the user selects one or more POIs, based on the selected POIs, one or more contents are recommended to the user. The contents include at least one of an app content and an app function.

Abstract: A method for multimodal clue based personalized app function recommendation is provided. The method includes receiving an app search query from a user, obtaining a plurality of real-time clues collected from the user and extracting a plurality of features to represent the collected clues, wherein the plurality of features include structured features and text features. The method also includes generating a joint representation of the multimodal clues based on the plurality of extracted features and creating a logistic regression model based on the joint representation of the multimodal clues. Further, the method includes ranking a list of app functions using the logistic regression model and displaying the ranked app function list for the user.

Abstract: A unified attractiveness prediction method is provided. The method includes receiving a plurality of videos and extracting at least one of metadata and view data from each of the plurality of received videos, wherein the metadata is information for describing video contents, and the view data is a total number of users who watch the video. The method also includes obtaining potential view amounts of the plurality of received videos and calculating impact factor scores of the plurality of received videos based on the potential view amounts, each impact factor score including a numerical score for indicating a degree of effectiveness of a corresponding video. Further, the method includes providing a video with a highest impact factor score based on the calculated impact factor scores.