Abstract: Systems, methods, and computer-readable medium storing instructions for identifying low-quality signals of an electronic listing of a listing entity are described herein. The method, system, or instructions may include ingesting text including listing information of the electronic listing and listing entity information of the listing entity; normalizing the text; parsing the text to identify a plurality of phrases; generating a set of category scores for each of at least one of the plurality of phrases; identifying, based on the on the sets of category scores, one or more low-quality signals each including a low-quality phrase and low-quality categories; and presenting an indication of the one or more low-quality signals with the electronic listing.

Abstract: Embodiments of the present invention provide methods, systems, apparatuses, and computer program products for predicting network asset requests for a future network time interval.

Abstract: Systems, methods, and computer-readable medium storing instructions for automatically identifying and emphasizing key text of an electronic listing. The method, system, or instructions may include parsing text of the electronic listing to identify a plurality of phrases; applying a scoring algorithm to one or more phrases of the plurality of phrases to determine a score corresponding to each of the one or more phrases; identifying key text of the electronic listing as at least one phrase of the one or more phrases that have a score at least equal to a threshold; marking the key text of the electronic listing for emphasized display; and in response to a request from a user for the electronic listing, causing the text of the listing to be displayed on a display of a user computing device with the key text being emphasized within the text based upon the marking of the key text.

Abstract: Embodiments of the present invention provide methods, systems, apparatuses, and computer program products for predicting system resource volumes for future network time intervals based upon predicted likelihoods of termination transactions for the future network time interval.

Abstract: Embodiments of the present invention provide methods, systems, apparatuses, and computer program products for predicting network asset requests for a future network time interval.

Abstract: Embodiments of the present invention provide methods, systems, apparatuses, and computer program products for predicting system resource volumes for future network time intervals based upon predicted likelihoods of termination transactions for the future network time interval.

Abstract: Embodiments of the present invention provide methods, systems, apparatuses, and computer program products for predicting network asset requests for a future network time interval.

Abstract: Systems and methods for predicting user actions based upon electronic communication between a user and an organization are disclosed. Based upon user interactions with an organization via electronic communication channels (e.g., e-mail, electronic chat, or voice/video calls), a trained predictive model is used to predict a probability of a future user action relating to the organization. Such user electronic communication is processed to generate message features from message content and metadata, which message features are then combined with other user data features associated with the user or with user interaction with an electronic data system of the organization. The combined data set of message features and user data features is analyzed by the predictive model to generate an output value indicative of a predication of a future user action.

Abstract: Systems and methods for guided vehicle matching are disclosed. In order to generate vehicle recommendations for a user of an electronic vehicle listing service, a plurality of vehicle-related lifestyle options are presented to the user, and a user selection received. Additional data regarding the user's vehicle preferences, requirements, or usage may be obtained. Based upon such information, a set of example vehicles is generated and presented to the user. Each example vehicle has characteristics representing a plurality of other vehicles. Based upon user ratings or selections of at least some of the example vehicles, a plurality of vehicle recommendations for specific available vehicles are generated and presented to the user. In some embodiments, further user interaction with such recommendations is used to refine the vehicle recommendations and identify additional vehicle recommendations.

Abstract: Embodiments of the present invention provide methods, systems, apparatuses, and computer program products for classifying merchants. In one embodiment a method is provided comprising determining, based on a first value, a first prediction value that indicates a programmatically expected number of consumers that will request termination of an accepted first promotion; and determining, based on the first prediction value, a classification for the first entity, the classification specifying a likelihood that the transmittal of the first promotion to consumers will result in a number of terminations less than a pre-specified threshold.

Abstract: Systems and methods for guided vehicle matching are disclosed. In order to generate vehicle recommendations for a user of an electronic vehicle listing service, a plurality of vehicle-related lifestyle options are presented to the user, and a user selection received. Additional data regarding the user's vehicle preferences, requirements, or usage may be obtained. Based upon such information, a set of example vehicles is generated and presented to the user. Each example vehicle has characteristics representing a plurality of other vehicles. Based upon user ratings or selections of at least some of the example vehicles, a plurality of vehicle recommendations for specific available vehicles are generated and presented to the user. In some embodiments, further user interaction with such recommendations is used to refine the vehicle recommendations and identify additional vehicle recommendations.

Abstract: Embodiments of the present invention provide methods, systems, apparatuses, and computer program products for classifying merchants. In one embodiment a method is provided comprising determining, based on a first value, a first prediction value that indicates a programmatically expected number of consumers that will request termination of an accepted first promotion; and determining, based on the first prediction value, a classification for the first entity, the classification specifying a likelihood that the transmittal of the first promotion to consumers will result in a number of terminations less than a pre-specified threshold.

Abstract: An apparatus, method, and computer program product are provided to predict and model the performance of option data objects prior to the introduction of such option data objects into a network environment through the application of specific rule sets that are applied in a multi-stage arrangement on sets of extracted option data object parameters. In some example implementations, option data object parameters are extracted and parsed into sets of categorical and non-categorical parameters, which may be both quantitative and qualitative, and transmitted to one or more predictive models. Through the operation of the predictive models, the option data object parameters are transformed into a weighted value associated with the option data object that reflects a predicted performance level of the option data object and can be used when comparing multiple option data objects against each other.

Abstract: An apparatus, method, and computer program product are provided to predict and model the performance of option data objects prior to the introduction of such option data objects into a network environment through the application of specific rule sets that are applied in a multi-stage arrangement on sets of extracted option data object parameters. In some example implementations, option data object parameters are extracted and parsed into sets of categorical and non-categorical parameters, which may be both quantitative and qualitative, and transmitted to one or more predictive models. Through the operation of the predictive models, the option data object parameters are transformed into a weighted value associated with the option data object that reflects a predicted performance level of the option data object and can be used when comparing multiple option data objects against each other.

Abstract: Systems and methods for guided vehicle matching are disclosed. In order to generate vehicle recommendations for a user of an electronic vehicle listing service, a plurality of vehicle-related lifestyle options are presented to the user, and a user selection received. Additional data regarding the user's vehicle preferences, requirements, or usage may be obtained. Based upon such information, a set of example vehicles is generated and presented to the user. Each example vehicle has characteristics representing a plurality of other vehicles. Based upon user ratings or selections of at least some of the example vehicles, a plurality of vehicle recommendations for specific available vehicles are generated and presented to the user. In some embodiments, further user interaction with such recommendations is used to refine the vehicle recommendations and identify additional vehicle recommendations.

Abstract: Systems and methods for providing information regarding vehicle inventory availability to users of an information system are disclosed. When generating a vehicle data page for a particular vehicle at a particular location, the information system may generate an expected duration of availability of the vehicle as a prediction of the time between listing and sale of the vehicle. When the expected duration of availability is below a threshold, a status indicator may be generated and added to the vehicle data page to alert users that the vehicle is expected to remain available for only a short period of time. An estimate of time remaining for the vehicle may also be generated and presented to users as part of the vehicle data page.