Uber Technologies, Inc.

Square, Inc.

Facebook, Inc.

Fei Guo

Joachim Bekmann

Bradley Ray Green

Jun Li

Xinyu Tang

Dajiang Wei

Shiyun Huang

A server of a transportation network determines characteristics of trips provided to users, as well as the usage of the services by users. Using the determined characteristics, the server trains a model that, for a given set of trips for a user, estimates a degree of likely user dissatisfaction with the trips. Based on the estimated degree of user dissatisfaction, the system can estimate user dissatisfaction in real time, directly after completion of a trip, and can take remedial actions should the user be estimated to be likely dissatisfied.

Anticipating user dissatisfaction via machine learning

In one embodiment, a method includes accessing user-concept scores for a first set of users, wherein each user-concept score is associated with a user-concept pair; calculating recommended user-concept scores for a subset of user-concept pairs in a second set of users. The first set of users may be discrete from the second set of users. A recommendation-algorithm may compute the recommended user-concept scores for a user-concept pair by optimizing an objective function comprising a plurality of predicted rating functions. Each predicted rating function may be determined using a user score, a concept score, a user-bias value associated with the user, as well as a concept-bias value associated with the concept. Finally, the method may include sending recommendations for one or more concepts based on the recommended user-concept scores for the second set of users.

Large-scale page recommendations on online social networks

A server provides communications to users and evaluates actions of the users after the provision of the communications to determine how receptive the various users are to such communications. Receptiveness is determined through a training process involving the formation of training sets based on whether users a test set of users were receptive to a previous set of communications, the derivation of relevant features for the users of the training set, the training of a model based on the derived features, and the use of the model for users whose receptiveness is to be estimated. With the most receptive users identified, the server can focus in future on communicating with those most receptive users.

Quantification of user receptiveness via machine learning

Financial transaction card data can be entered by providing a picture of the card to a server programmed with a text recognition algorithm. The server can perform text recognition on the image at the same time that a consumer enters additional required data, such as a zip code. The server can perform as much text recognition processing as possible in the time the consumer is entering the additional data. Once the additional data is received, a signal can be provided to the server indicating that the user is now waiting for results of the text recognition process, meaning the server should provide them as quickly as possible. Once text recognition results are received, a consumer can make a selection to identify a character which the text recognition algorithm did not sufficiently identify. Based on known account number constraints, the user selection can cause multiple characters to be identified.

Background OCR during card data entry

In one embodiment, a method includes accessing a social graph comprising a plurality of nodes and edges, accessing user-concept scores for a first set of users nodes of the plurality for nodes, respectively, generating a recommendation-algorithm for estimating recommended user-concept scores for all user-concept pairs in the first set of user nodes and the plurality of concept nodes, and calculating recommended user-concept scores for a second set of user nodes of the plurality of nodes.

Large scale page recommendations on online social networks

A contextual description of the arrival location can be determined based on individual or combined attributes, which include (i) a text-based identifier of one or more of the multiple places, and (ii) a spatial relationship as between one or more of the places, or as between the multiple places and the arrival location. The contextual description can be provided for a navigation interface of a computing device used by the user.

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Sunnyvale, CA, United States of America

Fei Guo