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: 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 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: 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 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 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: 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: 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.

Abstract: A system, computer-readable storage medium storing at least one program, and computer-implemented method for providing recommendations based on social network sharing activity. Sharing activity relating to the sharing of the content item on a social network by a first user is accessed. Consumption information related to the consumption of the content item. A correlation between the sharing activity and the consumption information is determined. A recommendation is then generated based on the correlation.

Abstract: In one embodiment, a method is illustrated as including defining a set of perspective objects capable of being placed onto a modified web page, monitoring parameters of a web page, the parameters including a number of times a current object is executed on the web page, using an Artificial Intelligence (AI) algorithm to determine a perspective object with a preferred Return On Investment (ROI), and selecting the perspective object to be placed onto the modified web page.

Abstract: In various example embodiments, a system and method for updating stored item data using a remote computing system are presented. In example embodiments, a first user selection of a representation of an item accessible from a first remote computing system is received at a client device while the client device is in electronic communication with the first remote computing system via a network. Based on the first user selection, first item data describing the selected item is retrieved from the first remote computing system via the network, and the first item data is stored in a data storage system residing on the client device. After the storing of the first item data, second item data for the selected item is retrieved from a second remote computing system via the network, and the stored first item data is updated based on at least a portion of the retrieved second item data.

Abstract: Methods and system for user definition and identification are described. In one embodiment, a user selection criterion for an application associated with a target service provider may be accessed. User relationship information of a source user within a source service provider may be accessed. The user relationship information may define a relationship between the source user and a plurality of users of the source service provider. One or more target users of the plurality of users may be identified for the application based on the accessing of the user relationship information and the user selection criterion. Additional methods and systems are disclosed.

Abstract: A system comprising a computer-readable storage medium storing at least one program, and a computer-implemented method for saving and presenting a state of a communication session are presented. The communication session may be established between a client device and an application server of a content publisher, and may include the presentation of content on the client device. In some embodiments, the method may include receiving user input to save a state of the communication session, and in response, temporarily storing session data representative of the state of the communication session for a predetermined duration of the communication session. The method may further include generating and presenting an interface that includes a visual representation of the session data, and allows a user to return to the saved state of the communication session.

Abstract: A code completion tool uses machine learning models to more precisely predict the likelihood of the parameters of a method invocation. A score is computed for each candidate variable that is used to rank the viability of a variable as the intended parameter. The score is a weighted sum of a scope factor, an edit distance factor and a declaration proximity factor. The factors are based on a scope model, a method overload model, and a weight file trained offline on a training set of source code programs utilizing various method invocations.

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 a same class 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 of a class in commonly-used programs from a sampled population.

Abstract: A method, system, and article of manufacture for recommending items for a room. An image of a room is received, a box image is fitted to the image of the room. Information is extracted from the fitted box image and is used for recommending items for the room. The image is a color image and extracting information is done by extracting color histograms from the fitted box image. The color histograms are used to determine items that match the color scheme of the room, the lighting of the room, and/or the decorating style of the room.

Abstract: An automated command assistance tool is provided for a browser-enabled command line interface of a cloud service. The automated command assistance tool provides examples illustrating the correct syntax for commands used to manage the resources of a cloud service. The command assistance tool learns the syntax of a command from usage patterns found in telemetric data, scripts and user documentation and forms templates containing a command's usage pattern and related information. The templates are used to generate examples that respond to a user query for assistance with usage of a particular command.

Abstract: A method of providing a level of certification of an attribute of an item is disclosed. A requirement is determined for a level of certification for an attribute of an item. A notification is provided of an evidence item that is to be submitted to evaluate the level of certification of the attribute of the item. The evidence item is received. The level of certification of the attribute of the item is determined based on the received evidence item.

Abstract: A code completion tool uses machine learning models generated for custom or proprietary classes associated with a custom library of classes of a programming language and for overlapping classes associated with a standard library of classes for the programming language. The machine learning models are trained with features from usage patterns of the custom classes and overlapping classes found in two different sources of training data. An n-order Markov chain model is trained for each custom class and each overlapping class from the usage patterns to generate probabilities to predict a method invocation more likely to follow a sequence of method invocations for a custom class and for an overlapping class.