Abstract: A system to provide image processing services responsive to requests including image data includes a system layer that forwards a request to an image application processing interface. Image processing provides an image comparison, barcode recognition, and optical character recognition. The image processing compares the image data to products in a database in order to identify a matching product. The system layer receives the matching information and forwards to a user.

Abstract: Electronic content that has a tactile dimension when presented on a tactile-enabled computing device may be referred to as tactile-enabled content. A tactile-enabled device is a device that is capable of presenting tactile-enabled content in a manner that permits a user to experience tactile quality of electronic content. In one example embodiment, a system is provided for generating content that has a tactile dimension when presented on a tactile-enabled device.

Abstract: A system receives item data corresponding to an item list from a user. The item list may include one or more items. The system communicates the item list to a community group associated with the user and the system then receives member data from one or more members of the community group, wherein the member data is associated with the one or more items on the item list.

Abstract: A code completion tool uses a neural transformer model to generate candidate sequences to complete a line of source code. The neural transformer model is trained using a conditional language modeling objective on a large unsupervised dataset that includes source code programs written in several different programming languages. The neural transformer model is used within a beam search that predicts the most likely candidate sequences for a code snippet under development.

Abstract: The behavior of a machine learning model and the training dataset used to train the model are monitored to determine when the accuracy of the model's predictions indicate that the model should be retrained. The retraining is determined from one or more precision metrics and a coverage metric that are generated during operation of the model. A precision metric measures the ability of the model to make predictions that are accepted by an inference system and the coverage metric measures the ability of the model to make predictions given a set of input features. In addition, changes made to the training dataset are analyzed and used as an indication of when the model should be retrained.

Abstract: An apparatus and method to adjust item recommendations are disclosed herein. A first image attribute of a query image is compared to a second image attribute of each of a plurality of inventory images of a plurality of inventory items to identify the inventory items similar to the query image. Item recommendations comprising the identified inventory items in a first listing order are provided for display at a remote device. A second listing order of the identified inventory items is determined based on a user preference for a particular one of the identified inventory items. At least the second listing order is provided to the remote device for re-display of the item recommendations in accordance with the second listing order.

Abstract: In various example embodiments, a system and method for providing listing assistance to a user for generating an item listing is provided. In example embodiments, user input is received from a device of the user. Information regarding item listings that correspond to the user input is accessed. Statistic data is generated using the accessed information. The statistic data is provided for display to the device of the user for generating the item listing.

Abstract: A method and a system for publishing contextual content. A method and a system maintain user profile information on a user of referrer content associated with a referrer system, the referrer content including a reference to a referee system. The method and system may transmit the user profile information to the referee system in response to the user using the reference to access the referee system. The referee system is to adapt content, presented to a user when accessing the referee system, based on the received user profile information.

Abstract: A system comprising a computer-readable storage medium storing at least one program and a computer-implemented method for automatically saving and presenting a state of a communication session are presented. A user interface is displayed. The user interface comprises an interface element presented in conjunction with a presentation of content. The interface element comprises: a first selectable element operable to recall a first state of the communication session and a second selectable element to hide the interface element. The display of the interface element is updated based on user input to include a third selectable element corresponding to a second state of the communication session. The display of the interface element is hidden while maintaining a presentation of the content in response to user input indicative of a selection of the second selectable element.

Abstract: A method and system for forming hybrid cluster to process log files are described. In example embodiments, a method configures a node to execute as a first slave node. The first slave node executes in a first operating environment. The method also adds the first slave node to a Hadoop cluster. The Hadoop cluster includes a second slave node that operates in a second and different operating environment.

Abstract: Embodiments of a method and system for utilizing a configurable interface are disclosed. User information for a first user is accessed. The user information is reviewed to determine whether the user information includes a preselected configurable interface. The preselected configurable interface is provided based on the user information including the preselected configurable interface. Alternatively, based on the user information not including the preselected configurable interface, a configurable interface based on a user selection of the configurable interface from a plurality of available configurable interfaces is configured.

Abstract: Systems and methods of visual mining of user behavior patterns are disclosed. A plurality of clickstreams may be received. Each clickstream may represent a corresponding sequence of user actions. A visual representation of each clickstream may be caused to be displayed on a device. Each visual representation may comprise a distinct graphical element for each user action of the corresponding clickstream, and each visual representation may be configured to indicate a frequency level of the corresponding clickstream. Each distinct graphical element may comprise a geometric shape and a corresponding color that distinctly represents the corresponding user action. Graphical elements of each visual representation may comprise a size that is proportional to the frequency level of the corresponding clickstream. An indication of a selection of one of the visual representations may be received, and additional information about the corresponding clickstream may be caused to be displayed on the device.

Abstract: A cloud resource management system trains, through ensemble learning, multiple time series forecasting models to forecast a future idle time of a virtual machine operating on a cloud computing service. The models are trained on historical usage and metric data of the virtual machine. The metric data includes CPU usage, disk usage and network usage. A select one of the models having the best accuracy for a target virtual machine is used in a production run to predict when the virtual machine will be idle. At this time, the virtual machine may be automatically shutdown in order to reduce the expense associated with the continued operation of the virtual machine.

Abstract: In various example embodiments, a system and method for an electronic commerce file system are provided. In example embodiments, a selection of an item contained in a folder of an electronic commerce file system is received. The item is offered for sale by an electronic commerce provider, and the electronic commerce file system resides locally on a client device. Based on a type of the folder, a set of actions are provided for selection, with the set of actions to be performed with respect to the item. A selection of an action to be performed with respect to the item is received. The action is performed with respect to the item, with the action being performed between the electronic commerce file system and the electronic commerce provider via a network.

Abstract: A data mining system and method retrieve data related to an item from a database. A survey is generated for presentation in a game. The survey includes the retrieved item data and solicits from a user input data pertaining to the retrieved item data. The input data is received from the survey and stored in a database with the item data. The input data is transmitted to the game and incorporated into the game such that the user interacts with the input data as part of playing the game.

Abstract: A system comprising a computer-readable storage medium storing at least one program and a computer-implemented method for automatically saving and presenting a state of a communication session are presented. A user interface is displayed. The user interface comprises an interface element presented in conjunction with a presentation of content. The interface element comprises: a first selectable element operable to recall a first state of the communication session and a second selectable element to hide the interface element. The display of the interface element is updated based on user input to include a third selectable element corresponding to a second state of the communication session. The display of the interface element is hidden while maintaining a presentation of the content in response to user input indicative of a selection of the second selectable element.

Abstract: A code completion tool uses machine learning models to more precisely predict the likelihood of a method invocation completing a code fragment that follows one or more method invocations of different classes in a same document during program development. In one aspect, the machine learning model is a n-order Markov chain model that is trained on features that represent characteristics of the context of method invocations found in commonly-used programs from a sampled population. The machine learning model is implemented as a hash table contained a ranked order of hash values in descending order of probability of completing a partially-formed method invocation.

Abstract: A code completion system predicts candidates to complete a method invocation in a source code program written in a dynamically-typed programming language. A pseudo type is generated for each variable in the source code program to approximate the runtime type of the variable. The pseudo type is then used to group a set of method invocations into a classification that can be modeled by an n-order Markov chain model. The n-order Markov chain model is used to predict candidate methods more likely to complete a method invocation in a dynamically-typed programming language.

Abstract: An apparatus and method to adjust item recommendations are disclosed herein. A first image attribute of a query image is compared to a second image attribute of each of a plurality of inventory images of a plurality of inventory items to identify the inventory items similar to the query image. Item recommendations comprising the identified inventory items in a first listing order are provided for display at a remote device. A second listing order of the identified inventory items is determined based on a user preference for a particular one of the identified inventory items. At least the second listing order is provided to the remote device for re-display of the item recommendations in accordance with the second listing order.

Abstract: A code completion tool uses a deep learning model to predict the likelihood of a method completing a method invocation. In one aspect, the deep learning model is a LSTM trained on features that represent the syntactic context of a method invocation derived from an abstract tree representation of the code fragment.