Abstract: An approach is provided for determining the context-aware similarity between applications. A similarity platform processes one or more context logs associated with one or more devices to determine one or more context intents. The similarity platform further determines one or more distributions of one or more applications executed at the one or more devices with respect to the one or more context intents. The similarity platform also determines a context-aware similarity between two or more applications based, at least in part, on the one or more application distributions.

Abstract: The present disclosure relates to predicting a destination of a user's current travel path. Real-time travel data associated with the current travel path is generated, wherein the real-time travel data comprises the user's current location. A plurality of potential destinations of the current travel path are determined based on historic travel data associated with one or more historic travel paths of the user. At least one destination of the current travel path is predicted from the plurality of potential destinations based on tracking a distance from the user's current location to each of the plurality of potential destinations.

Abstract: A dialog system and intelligent personal assistant capable of semantic-understanding mapping between user intents and machine services includes an interface to receive a voice or text request from a user. Semantics of the request including at least one of entities, intent, or context are extracted from the user's request. A sequence of action features is selected based on the extracted semantics. A sequence of application programming interfaces (APIs) corresponding to the sequence of action features is then executed to generate a result. An action sequence optimizer may optimize the sequence of action features based on user configuration. The examples provide a technical solution to model a richer semantic-understanding mapping between user intents and available APIs, that will be greatly improving user experience in the spoken dialogue system, as core of personal assistant.

Abstract: A system, method and non-transitory computer-readable medium for predicting a trip destination of a user based on user behavior learning are provided. Historical behaviors and a target behavior of the user are received from a feature processing layer, and the received historical behaviors and the target behavior are embedded with features including a time and a location to produce a context modeling layer. A user modeling layer is produced by embedding the context modeling layer. A trip destination is predicted based on historical trip data and target trip data in the user modeling layer.

Abstract: A system, method and non-transitory computer-readable medium are provided for deep user modeling of user behavior. According to the deep user modeling, user behavior vectors that represent historical user behaviors of a user are determined. Based on a concatenation of the user behavior vectors, a variable-length user behavior matrix is determined. The variable-length user behavior matrix is converted into a fixed-length embedding vector via a long short term memory network, and the fixed-length embedding vector is outputted to the user as a predicted target behavior.

Abstract: A system, method and non-transitory computer-readable medium provided an algorithmic framework for context modeling of user behavior and machine learning of the user behavior in order to optimize user behavior across users, context, and content with different kinds of behaviors. According to the algorithmic framework, context and content modeling optimizes user behavior across users, context, content with different kind of behaviors based on a user behavior matrix.

Abstract: The exemplary embodiments of the invention provide at least a method, apparatus and system to perform operations including receiving context data from an electronic device, causing, at least in part based on the received context data, an identification of at least one context model compatible with the electronic device, and causing, at least in part, provision of the electronic device with the at least one compatible context model. In addition, the exemplary embodiments of the invention further provide at least a method, apparatus and system to perform operations including causing, at least in part, a provision of context data associated with an electronic device to a context inference service, in response, receiving a context model from the context inference service, and causing adaptation of the received context model as a current context model of the electronic device.

Abstract: A method for providing responsive actions to user inputs in a multi-domain context includes receiving, by a speech-based user interface, a first speech input from a user and converting said first speech input into a text-based representation of the first speech input. A natural language processor processes the text-based representation to determine an intent, entity and internal state of the first speech input. The method further includes determining, by a model-based module based on the intent, entity and internal state, a first data processing policy to apply to the first speech input, wherein the first data processing policy is either a rules-based data processing policy applied by a rules-based module or a statistical model-based data processing policy applied by the model-based module. The first responsive action is generated by the determined first data processing module, and outputted via the speech-based user interface and/or a machine interface.

Abstract: A method for smart parking a connected vehicle is disclosed. The method determines a destination of a user's vehicle based on the user's input or historic travel data of the user. The method receives information on available parking spots in a predefined area near the user's destination based on a parking database. The method receives an input to reserve a desired parking spot among the available parking spots. The method initiates an online check-in of the reserved parking spot in the parking database.

Abstract: A fuel consumption prediction method and apparatus is provided. A respective historic fuel consumption is extracted from historic travel data for at least one historic travel path of a vehicle. At least one historic fuel-related quantity influencing the respective historic fuel consumption is extracted from the historic travel data for each historic travel path. Model parameters of a machine learning model for predicting fuel consumption is adjusted based on the extracted respective historic fuel consumption and the at least one historic fuel-related quantity for each historic travel path. The fuel consumption for a planned trip is predicted using the machine learning model with the adjusted model parameters.

Abstract: The exemplary embodiments of the invention provide at least a method, apparatus and system to perform operations including receiving context data from an electronic device, causing, at least in part based on the received context data, an identification of at least one context model compatible with the electronic device, and causing, at least in part, provision of the electronic device with the at least one compatible context model. In addition, the exemplary embodiments of the invention further provide at least a method, apparatus and system to perform operations including causing, at least in part, a provision of context data associated with an electronic device to a context inference service, in response, receiving a context model from the context inference service, and causing adaptation of the received context model as a current context model of the electronic device.

Abstract: A system, method, and non-transitory computer-readable medium are provided for implementing adaptive live trip prediction in a vehicle of a user in real-time while the user uses the vehicle. In addition to predicting trip destinations based on historical trip data, the system uses a confidence measure of each of a plurality of known candidate destinations and a rejection strategy for rejecting candidate destinations as possible destinations based on real-time vehicle location information. When the system rejects all of the known candidate destinations and determines that the user's destination is an unknown or new destination, the system determines the location of the new destination be repeatedly determining an angle between a line from the original location to the current location and a north direction over time until the new destination can be accurately predicted. Further, the system is able to self-optimize the prediction performance without an explicit interaction with the user.

Abstract: A method and system for determining a preference of a user for a location is provided. The preference of the user for the location is determined based on a plurality of users and a plurality of locations. The method includes determining a user-location relation based on a plurality of relations of the users with the locations, determining a plurality of POItags indicative of one or more properties of the plurality of locations, and determining a user-POItag relation based on the plurality of users and the plurality of POItags. The method also includes determining a location-POItag relation based on the plurality of locations and the plurality of POItags, and determining the preference of the user for the location based on at least one of the user-location relation, the user-POItag relation, and the location-POItag relation. The system includes a controller configured to perform the method. A vehicle including the system is also provided.

Abstract: Method, apparatus, system, computer program product and computer readable medium are disclosed for enhancing a user's engagement or experience with a service. The method comprises obtaining the user's current active level with respect to the service with respect to the service; determining a target active level; and sending the user a notification about the service at a time determined based on the user's current active level and the target active level, so that the user is notified of the service without being severely annoyed.

Abstract: An approach is provided for determining the context-aware similarity between applications. A similarity platform processes one or more context logs associated with one or more devices to determine one or more context intents. The similarity platform further determines one or more distributions of one or more applications executed at the one or more devices with respect to the one or more context intents. The similarity platform also determines a context-aware similarity between two or more applications based, at least in part, on the one or more application distributions.

Abstract: A method for smartly managing a plurality of potential travel destinations of a user is provided. The method includes determining a plurality of similarities between different pairs of the plurality of potential travel destinations. Further, the method includes weighting the plurality of similarities using contextual information related to the user, and clustering part of the plurality of potential travel destinations based on the plurality of weighted similarities.

Abstract: A system, method, and non-transitory computer-readable medium are provided which track mobility of a user via a vehicle and/or a portable electronic device to automatically and proactively provide enhanced service information to the user based on changes in behavior of the user. According to the method, data indicating a behavior pattern of the user is received from a memory, the mobility of the user is continuously tracked, and a determination is made of whether a change occurs in the behavior pattern of the user based on the mobility of the user. When it is determined that a change has occurred, a determination is made of the type of change in the behavior pattern, and based on the type of change, the service information is outputted to the user and/or a third party such as a financial service provider and the behavior pattern is periodically adapted according to the user's feedback.

Abstract: A method for determining a travel destination from user generated content is proposed. The method includes determining a text string in the user generated content that indicates a place or an address. Further, the method includes determining a plurality of potential travel destinations based on the text string. The method additionally includes determining similarities between the plurality of potential travel destinations and a plurality of reference positions assigned to the user, and ranking the plurality of potential travel destinations based on the similarities.

Abstract: A method and system for determining a preference of a user for a location is provided. The preference of the user for the location is determined based on a plurality of users and a plurality of locations. The method includes determining a user-location relation based on a plurality of relations of the users with the locations, determining a plurality of POItags indicative of one or more properties of the plurality of locations, and determining a user-POItag relation based on the plurality of users and the plurality of POItags. The method also includes determining a location-POItag relation based on the plurality of locations and the plurality of POItags, and determining the preference of the user for the location based on at least one of the user-location relation, the user-POItag relation, and the location-POItag relation. The system includes a controller configured to perform the method. A vehicle including the system is also provided.

Abstract: An approach is provided for constructing dynamic latent models that determine consumer/social network intrinsic properties and automatically recommend user interactions with different social networks. A modeling platform determines one or more social networks associated with one or more users, one or more devices associated with the one or more users, or a combination thereof. A modeling platform processes and/or facilitates a processing of data associated with the one or more social networks to generate one or more latent models describing the one or more social networks. A modeling platform causes, at least in part, a presentation of a recommendation to interact with the one or more social networks, one or more other social networks, or a combination thereof based, at least in part, on the one or more latent models.