Abstract: A navigation system includes primary and secondary navigation device, and a server. The secondary navigation device transmits geolocation data to the server. The primary navigation device receives user input indicating a destination. The primary navigation device also determines a current route to the destination based on a current location of the primary navigation device, and transmits the current route to the server. The primary navigation device also receives and displays a difficulty index from the server in response to transmitting the current route. The server determines historical routes reflecting historical commutes based on the geolocation data. The server also determines the difficulty index based on the one or more historical routes and the current route, and transmits the difficulty index to the primary navigation device in response to receiving the current route.

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 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: An approach is provided for training a device to associate user, object or device context information with a user defined context. The context information platform receives recorded context information from a device. The associated recorded context information is then associated with the context to enable training of a context model associated with the context.

Abstract: The disclosure is directed to determining an actual point-of-interest (POI). For example, using at least one computing device, a first set of probable POIs corresponding to a first geographical location and first POI category information may be determined. The at least one computing device may also be used to determine a second set of probable POIs corresponding to a second geographical location and determine second POI category information. By analyzing the first and second POI category information, the actual POI from the first and second set of probable POIs may be determined.

Abstract: An approach is provided for performing multiple and hybrid forms of communication in the same communication session. A communication manager receives a to establish a communication session using a first form of communication, wherein the communication session supports multiple and simultaneous forms of communication. Next, the communication manager selects a second form of communication to conduct the communication session. Then, the communication manager transcodes the second form of communication to the first form of communication. The different forms can be converted to facilitate and enrich the communication capability, according to an embodiment of the invention.

Abstract: An approach is provided for generating one or more recommendations to a user base on interactions the user may have with items or topics of interest. The approach involves processing and/or facilitating a processing of one or more interactions of a user with one or more content items. The approach further involves causing, at least in part, an accumulation of the one or more processed interactions of the user. The approach also involves causing, at least in part, a determination of one or more user preferences based, at least in part, on the accumulated one or more processed interactions. The approach additionally involves causing, at least in part, a generation of a rating score of the user for the topic based, at least in part, on the one or more user preferences.

Abstract: The disclosure is directed to improving driving experience by determining and providing a personalized estimated time of arrival (ETA). For example, using at least one computing device, driving data associated with a user may be collected and stored in memory. The at least one computing device may also be used to receive ETA-related data including one or more real time ETA estimations, perform analysis on one or more of the driving data and the ETA-related data so as to determine a set of personalized parameters of a mapping function that correspond to a training set. The at least one computing device may also be used to determine the personalized ETA based at least in part on the determined set of personalized parameters and provided to the user.

Abstract: A navigation system includes primary and secondary navigation device, and a server. The secondary navigation device transmits geolocation data to the server. The primary navigation device receives user input indicating a destination. The primary navigation device also determines a current route to the destination based on a current location of the primary navigation device, and transmits the current route to the server. The primary navigation device also receives and displays a difficulty index from the server in response to transmitting the current route. The server determines historical routes reflecting historical commutes based on the geolocation data. The server also determines the difficulty index based on the one or more historical routes and the current route, and transmits the difficulty index to the primary navigation device in response to receiving the current route.

Abstract: An approach is provided for diversifying recommendation results. A diversification manager determines one or more recommendation scores associated with one or more items. The one or more recommendation scores are for determining a sort order of the one or more items. The diversification manager then determines similarity information associated with the one or more items, and processes and/or facilitates a processing of the similarity information to cause, at least in part, an adjustment of the one or more recommendation scores, the sort order, or a combination thereof.

Abstract: An example approach is provided for providing recommendations to a user based on user's role derived from the context information and an advanced user profile. A recommendation platform processes context information to determine one or more roles associated with a user. Next, the recommendation platform calculates at least one level of similarity between the one or more roles and one or more other roles associated with one or more other users. Then, the recommendation platform determines recommendation information associated with the one or more other users based, at least in part, on the at least one level of similarity. Next, the recommendation platform processes the recommendation information to generate one or more recommendations for the user.

Abstract: The disclosure is directed to improving driving experience. For example, using one or more computing devices, a current location of a user may be determined and a place of interest, located at a distance from the current location, may be identified. The one or more computing devices may determine that the place of interest meets one or more requirements, and designate the place of interest as a candidate place of interest. The candidate place of interest may be recommended to the user based on a defined criteria. The one or more computing devices may also determine that the user has arrived at a parking location corresponding to the candidate place of interest, and associate the parking location and the candidate place of interest based on one or more inputs by the user.

Abstract: The disclosure is directed to determining an actual point-of-interest (POI). For example, using at least one computing device, a first set of probable POIs corresponding to a first geographical location and first POI category information may be determined. The at least one computing device may also be used to determine a second set of probable POIs corresponding to a second geographical location and determine second POI category information. By analyzing the first and second POI category information, the actual POI from the first and second set of probable POIs may be determined.

Abstract: The disclosure is directed to improving driving experience by determining and providing a personalized estimated time of arrival (ETA). For example, using at least one computing device, driving data associated with a user may be collected and stored in memory. The at least one computing device may also be used to receive ETA-related data including one or more real time ETA estimations, perform analysis on one or more of the driving data and the ETA-related data so as to determine a set of personalized parameters of a mapping function that correspond to a training set. The at least one computing device may also be used to determine the personalized ETA based at least in part on the determined set of personalized parameters and provided to the user.

Abstract: The disclosure is directed to improving driving experience. For example, using one or more computing devices, a current location of a user may be determined and a place of interest, located at a distance from the current location, may be identified. The one or more computing devices may determine that the place of interest meets one or more requirements, and designate the place of interest as a candidate place of interest. The candidate place of interest may be recommended to the user based on a defined criteria. The one or more computing devices may also determine that the user has arrived at a parking location corresponding to the candidate place of interest, and associate the parking location and the candidate place of interest based on one or more inputs by the user.

Abstract: A method, apparatus and computer-readable storage medium for determining one or more recommendations by applying efficient adaptive matrix factorization are disclosed. The method comprises causing, at least in part, an iterative performing of the following steps: a using of a current data set to optimize parameters used to adapt a current matrix factorization model by the end of the current time period, and a training of a current matrix factorization model and the current data set by the end of the current time period, based on the optimized parameters, to obtain an adapted matrix factorization model for service in a next time period.

Abstract: Methods and apparatuses are provided for user interest modeling. A method may include receiving an input from a user for specifying one or more topics from among a predetermined hierarchy of topics and subtopics. The method may additionally include retrieving one or more documents associated with the user and extracting language tokens from the documents based, at least in part, on the specified topics. Corresponding apparatuses are also provided.

Abstract: A context-aware adaptive authentication method may comprise: determining a context for a user; adjusting automatically an authentication configuration for the user based at least in part on the context, wherein different matching accuracies of an authentication algorithm for the same authentication input data are associated with respective authentication configurations; and performing an authentication of the user with the authentication configuration, wherein for the user under a safe context, the authentication is performed by decreasing a matching accuracy of the authentication algorithm.

Abstract: Method, apparatus, system, computer program product and computer readable medium are disclosed for recommending content to a plurality of users. Each of the users is associated with a user score. The method comprises determining a recommending score for an item of content at least partly based on a user's promotion of the item and the user score of the promoting user; recommending the item according to its recommending score; and adjusting the user score of the promoting user based on other users' feedback with respect to the item promoted by said user.

Abstract: An approach is provided for generating personalizing resource recommendations. A recommendation engine causes, at least in part, a classification of one or more items into a tree structure, wherein the tree structure is based, at least in part, on a hierarchical categorization of the one or more items, and wherein the one or more items are associated with a usage history of at least one user. The recommendation engine determines one or more candidate root nodes of the tree structure based, at least in part, on one or more user preference criteria. The recommendation engine determines one or more candidate sub-nodes associated the respective one or more candidate root nodes based, at least in part, on the usage history. The recommendation engine causes, at least in part, a ranking of the one or more candidate root nodes, the one or more candidate sub-nodes, or a combination thereof.