Abstract: Embodiments of this specification provide a multi-objective learning-based service policy generation method and service policy generation apparatus. In the service policy generation method, a labeled service data sample set is obtained, where each piece of service data sample includes at least one service feature and at least two label values of the piece of service data sample; multi-objective optimization-based service rule training is performed based on the labeled service data sample set, to construct a service rule set, where each optimization objective in multi-objective optimization corresponds to one label in the service data; and then a service policy is generated based on the constructed service rule set.

Abstract: This disclosure describes a map-matching module that supports a Global Positioning System (GPS) and provides a user with a best match trajectory corresponding to GPS sampling points taken at a low sampling rate. The best match trajectory is based upon a spatial-temporal analysis.

Abstract: This disclosure describes a map-matching module that supports a Global Positioning System (GPS) and provides a user with a best match trajectory corresponding to GPS sampling points taken at a low sampling rate. The best match trajectory is based upon a spatial-temporal analysis.

Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for training explainable machine learning models are described. An exemplary method includes obtaining, by sampling without replacement for a plurality of times, a plurality of training datasets from a plurality of historical data records, including a first training dataset and a second training dataset with one or more overlapped historical data records; generating a plurality of histograms respectively corresponding to the plurality of training datasets, in which a histogram of the second training dataset reuses one or more data points corresponding to the one or more overlapped historical data records in a histogram of the first training dataset; training, based on the plurality of histograms, one or more machine learning models corresponding to the one or more user features; and providing personalization based on the one or more machine learning models.

Abstract: Systems and methods are provided for analyzing booking session data to generate a plurality of feature vectors for each booking session of the plurality of booking sessions, and generating training data comprising the plurality of feature vectors for each booking session and at least a first constraint. The systems and methods further providing for calculating a set of weights using the training data, wherein each weight is a lowest weight satisfying the most constraints possible, wherein the set of weights comprises a weight associated with each feature in the plurality of feature vectors, and computing a reservation value for each of a plurality of clients for each of a plurality of listings and for each date of a plurality of dates, based on the set of weights and the plurality of feature vectors.

Abstract: This disclosure describes a map-matching module that supports a Global Positioning System (GPS) and provides a user with a best match trajectory corresponding to GPS sampling points taken at a low sampling rate. The best match trajectory is based upon a spatial-temporal analysis.

Abstract: This disclosure describes a map-matching module that supports a Global Positioning System (GPS) and provides a user with a best match trajectory corresponding to GPS sampling points taken at a low sampling rate. The best match trajectory is based upon a spatial-temporal analysis.

Abstract: Systems and methods are provided for analyzing booking session data to generate a plurality of feature vectors for each booking session of the plurality of booking sessions, and generating training data comprising the plurality of feature vectors for each booking session and at least a first constraint. The systems and methods further providing for calculating a set of weights using the training data, wherein each weight is a lowest weight satisfying the most constraints possible, wherein the set of weights comprises a weight associated with each feature in the plurality of feature vectors, and computing a reservation value for each of a plurality of clients for each of a plurality of listings and for each date of a plurality of dates, based on the set of weights and the plurality of feature vectors.

Abstract: The invention provides a light emitting diode device that comprises a light emitting diode die. The method for manufacturing the light emitting diode device comprises positioning a first stencil over a carrier, printing a phosphor material onto the carrier through at least one aperture of the first stencil to form a phosphor material piece on the carrier, and attaching the light emitting diode die onto the printed phosphor material piece.

Abstract: Techniques for optimizing a guided edit process for editing a member profile page are described. According to various embodiments, incomplete member profile fields in a member profile associated with member of an online social networking service are identified. Profile completion score weight values associated with the incomplete member profile fields in the member profile are then determined. Thereafter, the incomplete member profile fields in the member profile are ranked, based on the profile completion score weight values associated with each of the incomplete member profile fields. A list of one or more of the highest ranked incomplete member profile fields are then displayed, together with a prompt recommending the member to complete the identified incomplete member profile fields.

Abstract: Techniques for optimizing a guided edit process for editing a member profile page are described. According to various embodiments, incomplete member profile fields in a member profile associated with member of an online social networking service are identified. Profile completion score weight values associated with the incomplete member profile fields in the member profile are then determined. Thereafter, the incomplete member profile fields in the member profile are ranked, based on the profile completion score weight values associated with each of the incomplete member profile fields. A list of one or more of the highest ranked incomplete member profile fields are then displayed, together with a prompt recommending the member to complete the identified incomplete member profile fields.

Abstract: Techniques for providing a route based on route-oriented vehicle trajectories are described. This disclosure describes receiving GPS logs and extracting route-oriented vehicle trajectory content from the GPS log data to pertain to a single trip. Next, the process maps each route-oriented vehicle trajectory to a corresponding road segment to construct a landmark graph. A landmark is a road segment frequently visited by route-oriented vehicles. The process includes receiving a user query with a starting point and a destination point; searching the landmark graph for a sequence of landmarks with corresponding transition times and a least amount of travel time. Then the process identifies and connects sets of road segments between each pair of consecutive landmarks, and displays a route to a user with a nearest landmark to the starting point, other landmarks along the route, and another nearest landmark to the destination point.

Abstract: This disclosure describes a map-matching module that supports a Global Positioning System (GPS) and provides a user with a best match trajectory corresponding to GPS sampling points taken at a low sampling rate. The best match trajectory is based upon a spatial-temporal analysis.

Abstract: Techniques for providing a route based on route-oriented vehicle trajectories are described. This disclosure describes receiving GPS logs and extracting route-oriented vehicle trajectory content from the GPS log data to pertain to a single trip. Next, the process maps each route-oriented vehicle trajectory to a corresponding road segment to construct a landmark graph. A landmark is a road segment frequently visited by route-oriented vehicles. The process includes receiving a user query with a starting point and a destination point; searching the landmark graph for a sequence of landmarks with corresponding transition times and a least amount of travel time. Then the process identifies and connects sets of road segments between each pair of consecutive landmarks, and displays a route to a user with a nearest landmark to the starting point, other landmarks along the route, and another nearest landmark to the destination point.