Abstract: The present disclosure is directed to apparatuses, systems, and methods for predicting item characteristic popularity—i.e., identifying item characteristics (e.g., item brands, item types, etc.) that are to eventually become popular. Something that is to eventually become popular is referred to herein as “pre-trend” or “cool.” In the embodiments described herein, electronic marketplace transaction data is analyzed to identify popular characteristics of items involved in recent transactions. The electronic marketplace transaction data is further analyzed to identify one or more users that executed transactions for items having these popular characteristics during a previous time period. These users' transaction histories are analyzed to determine what other item characteristics are prevalent in their more recent transactions, as these item characteristics can be identified as pre-trend/cool.

Abstract: A system comprising a computer-readable storage medium storing at least one program and a computer-implemented method for creating messages using generative grammar models is presented. Consistent with some embodiments, the method may include receiving a request to generate a message, which in an example embodiment is to be published to a social network platform. In response to receiving the request, a generative grammar model defining the structure of the message is accessed. The generative grammar model may include a number of blanks and may specify a source along with a grammatical constraint for a term to populate each blank. The method may further include generating the message in accordance with the generative grammar model, and causing the generated message to be published.

Abstract: Systems and methods are presented for matching a buyer and a seller on a market place system and generating calibrated user profiles. In one such system a plurality of subjective estimations of value is received. The subjective estimations of value are a measure between a predetermined minimum value and a predetermined maximum value. A user profile is generated. A plurality of user actions corresponding to the plurality of subjective estimations of value is received. The user profile is calibrated based on the plurality of user actions.

Abstract: Software authenticates a first user of a first mobile device and a second user of a second mobile device. Each of the mobile devices includes a video capturing device and each user agrees or has previously agreed to share video data captured by the user with other authenticated users. The software determines whether the first mobile device is located proximate to the second mobile device. Then the software receives video data captured by the second mobile device and transmits the video data to the first mobile device for display along with an annotation indicating the location of the second mobile device relative to the first mobile device, if the first mobile device is proximate to the second mobile device. The software receives an indication from the first mobile device that at least some of the video data is to be indexed in a profile associated with the first user.

Abstract: A user activity detection engine monitors user activity during user sessions on a publication system, and detects a change in the level of the activity of the user that predicts that the user is about to execute a transaction. The change may be an increase in the level of activity. When the user activity detection engine detects such a change, the system may make an intervention to provide personalized marketing content for display to the user in an effort to improve the probability that the user will execute a transaction, and/or also to motive the user to execute the transaction on the system site instead of moving to a competitor site in search of a different transaction.

Abstract: A system comprising a computer-readable storage medium storing at least one program and a computer-implemented method for generating and provisioning personalized messages is presented. Consistent with some embodiments, the method may include receiving a request from a user to share content with one or more other users. In response to receiving the request, user profile data about the user may be accessed, and a portion of the user profile data may be selected. The method may further include generating a personalized message based on the selected portion of the user profile data, and communicating the personalized message to the one or more other users.

Abstract: A machine may be configured to determine fashion preferences of users and to provide item recommendations to the users based on the users' fashion preferences. For example, the machine receives an image of a user and a set of spatial data indicating a position of the body of the user in a three-dimensional space. The machine may perform an analysis of the image and the set of spatial data. The performing of the analysis may include extracting, from the image, an image swatch that depicts a portion of an item worn by the user. The machine may identify a fashion preference of the user based on the analysis of the image and of the set of spatial data. The machine may identify an item that corresponds to the fashion preference of the user within an inventory of fashion items and may generate a recommendation of the identified fashion item.

Abstract: A system to customize user experience based on brand resilience data is described. An example system includes a new session detector, a session type module, a brand resilience module, and a search strategy selector. The new session detector detects commencement of a user session in the on-line trading platform. The session type module examines the initial search request in a user session and determines whether the initial search request includes a phrase that represents a brand name. The brand resilience module examines brand resilience value assigned to the brand name. The search strategy selector selects, based on the brand resilience value, a search strategy for the user session.

Abstract: A machine may be configured to perform image evaluation of images depicting items for sale and to provide recommendations for improving the images depicting the items to increase the sales of the items depicted in the images. For example, the machine accesses a result of a user behavior analysis. The machine receives an image of an item from a user device. The machine performs an image evaluation of the received image based on an analysis of the received image and the result of the user behavior analysis. The performing of the image evaluation may include determining a likelihood of a user engaging in a desired user behavior in relation to the received image. Then, the machine generates, based on the evaluation of the received image, an output that references the received image and indicates the likelihood of a user engaging in the desired behavior.

Abstract: The disclosure herein relates to a system and method for personalizing an online experience of a user based on the user's attitude. Attitude models are constructed from user activity data that are able to infer or determine attitudes for a user. Based on the attitudes derived from applying the attitude models, attitude profiles are created for the users. As a users engage in various types of online interactions, the attitude profiles associated with the users can be utilized to personalize the online experience of the user.

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: Software authenticates a first user of a first mobile device and a second user of a second mobile device. Each of the mobile devices includes a video capturing device and each user agrees or has previously agreed to share video data captured by the user with other authenticated users. The software determines whether the first mobile device is located proximate to the second mobile device. Then the software receives video data captured by the second mobile device and transmits the video data to the first mobile device for display along with an annotation indicating the location of the second mobile device relative to the first mobile device, if the first mobile device is proximate to the second mobile device. The software receives an indication from the first mobile device that at least some of the video data is to be indexed in a profile associated with the first user.

Abstract: The disclosure herein relates to a system and method for personalizing an online experience of a user based on the user's attitude. Attitude models are constructed from user activity data that are able to infer or determine attitudes for a user. Based on the attitudes derived from applying the attitude models, attitude profiles are created for the users. As a users engage in various types of online interactions, the attitude profiles associated with the users can be utilized to personalize the online experience of the user.

Abstract: Location aware notification service technology operable to provide users with notification of opportunities to complete errands on a personal sharable list is disclosed. The location aware notification service technology provides a means for generating recommendations for users for the successful completion of errands based upon current location, time and travel context.

Abstract: Personality traits displayed on a workbench are grouped into a character profile that is utilized in determining the personality and/or specific actions of a synthetic character. The personality traits are selectable by an operator from graduated dials. The personality traits may also be displayed in the form of selectable attributes or trait indicative behaviors that, when selected, are mapped into predetermined amounts of each trait in the character profile. The character profile is utilized to compute activation levels for primary behaviors, which are selected to influence or direct behavior of the synthetic character. Secondary behaviors are also selected, but discarded if conflicting with the selected primary behaviors. When behaviors are selected, the corresponding behaviors are translated into classes of ‘personality effectors’ that modulate the synthetic character's behavioral expression to reflect the desired set of traits.

Abstract: A system and method for repurposing and augmenting document content by clipping, annotating, and reassembling portions of documents, and which may be used in an online or collaborative environment. As used herein the term “repurposing” includes packaging the online information in a manner so that it can be re-used by subsequent users for subsequent applications. An input device or menu referred to herein as a “clipbar” may be provided within a software application or in a kiosk environment that allows users to clip and annotate portions of online or other documents, while retaining both the document fragment's rendered image and its underlying structure. The clips thus created may be stored as collections (together with their associated user-specified and system-specified meta-data) in a form compatible with the other input documents. The collections can be immediately made available to other users in a collaborative fashion, and re-clipped as desired.

Abstract: Methods and systems that can be used to dynamically restructure user interfaces into a set of communicating processes. The methods and systems update new and wrapped legacy user interface components for repurposing of user interfaces. The methods and systems provide for changing one or more of at least an appearance, a behavior, and a state or condition of the user interfaces.

Abstract: User behavior while interacting with one or more first representations of digital content can be monitored. Interaction information can be determined by monitoring the user behavior while interacting. Interaction information can include information identifying interaction areas of the first representation, degree of interaction information, and sequence of interaction information. The interaction information can be maintained and used to deform a second representation of digital content. The first and second representations can be of the same or different digital content.

Abstract: Personality traits displayed on a workbench are grouped into a character profile that is utilized in determining the personality and/or specific actions of a synthetic character. The personality traits are selectable by an operator from graduated dials. The personality traits may also be displayed in the form of selectable attributes or trait indicative behaviors that, when selected, are mapped into predetermined amounts of each trait in the character profile. The character profile is utilized to compute activation levels for primary behaviors, which are selected to influence or direct behavior of the synthetic character. Secondary behaviors are also selected, but discarded if conflicting with the selected primary behaviors. When behaviors are selected, the corresponding behaviors are translated into classes of ‘personality effectors’ that modulate the synthetic character's behavioral expression to reflect the desired set of traits.

Abstract: Deliberative and reactive processing are combined to process multi-modal inputs and direct movements and speech of a synthetic character that operates as an interface between a user and a piece of equipment. The synthetic character is constructed as an ally, working with and helping the user learn and operate the equipment. The synthetic character interacts with both a virtual space where the character is displayed, and a physical space (domain) that includes the user. Real-time reactive processing provides lifelike and engaging responses to user queries and conversations. Deliberative processing provides responses to inputs that require more processing time (deep linguistic processing, for example). Knowledge bases are maintained for both dynamic (discourse model, for example) and static (e.g., knowledge about the domain or discourse plans) information types.