Abstract: Disclosed herein are system, method, and computer program product embodiments for probabilistic search biasing and recommendations. A system including at least one processor may be configured to receive an input relating to an identified item, generate a database query based on the input, and receive a response to the database query. The response may include information on comparable items similar to the identified item, and corresponding metadata for the comparable items. The corresponding metadata may include a range of values corresponding to the comparable items. The system may be further configured to generate a probability score for at least two values of the range of values, based at least on the corresponding metadata for the comparable items. The system may be further configured to output at least a suggested value based on at least the generated probability score for the range of values and prompt for further input.

Abstract: Disclosed herein are system, method, and computer program product embodiments for probabilistic search biasing and recommendations. A system including at least one processor may be configured to receive an input relating to an identified item, generate a database query based on the input, and receive a response to the database query. The response may include information on comparable items similar to the identified item, and corresponding metadata for the comparable items. The corresponding metadata may include a range of values corresponding to the comparable items. The system may be further configured to generate a probability score for at least two values of the range of values, based at least on the corresponding metadata for the comparable items. The system may be further configured to output at least a suggested value based on at least the generated probability score for the range of values and prompt for further input.

Abstract: Disclosed herein are system, method, and computer program product embodiments for generating sellability and cancellability scores for selling objects on an electronic marketplace. An embodiment operates by receiving, from a mobile device, an image including an object for sale and determining information associated with the object for sale. Information associated with pre-identified objects is determined from information associated with pre-identified objects, where each of the pre-identified objects corresponds to the object for sale. A sellability score and/or a cancellability score is generated based at least in part on the information associated with the object for sale and the information associated with the pre-identified objects. The sellability score and the cancellability score are further provided to users.

Abstract: Disclosed herein are system, method, and computer program product embodiments for the prediction of listing attributes for a for sale object (FSO). Data analyzed with respect to historical listings in a database is compared to attributes of the FSO using machine learning (ML) and artificial intelligence (AI), in order for the user to select a category related to the FSO, and for a listing generation module to generate a ultimately reduced set of listings related to the FSO, where the ultimately reduced set is used to predict a price for the FSO based on the selected category, and generate a listing for the FSO, wherein a listing for the FSO is monitored, and information from the monitoring is used as machine learning feedback to train and enhance predictions.

Abstract: Disclosed herein are system, method, and computer program product embodiments for probabilistic search biasing and recommendations. A system including at least one processor may be configured to receive an input relating to an identified item, generate a database query based on the input, and receive a response to the database query. The response may include information on comparable items similar to the identified item, and corresponding metadata for the comparable items. The corresponding metadata may include a range of values corresponding to the comparable items. The system may be further configured to generate a probability score for at least two values of the range of values, based at least on the corresponding metadata for the comparable items. The system may be further configured to output at least a suggested value based on at least the generated probability score for the range of values and prompt for further input.

Abstract: Disclosed herein are system, method, and computer program product embodiments for probabilistic search biasing and recommendations. A system including at least one processor may be configured to receive an input relating to an identified item, generate a database query based on the input, and receive a response to the database query. The response may include information on comparable items similar to the identified item, and corresponding metadata for the comparable items. The corresponding metadata may include a range of values corresponding to the comparable items. The system may be further configured to generate a probability score for at least two values of the range of values, based at least on the corresponding metadata for the comparable items. The system may be further configured to output at least a suggested value based on at least the generated probability score for the range of values and prompt for further input.

Abstract: Disclosed herein are system, method, and computer program product embodiments for probabilistic item matching and searching. A system including at least one processor may be configured to receive a data point relating to a specific item, generate a database query based on the data point, and receive a response to the database query. The response may include multiple candidate items relating to the specific item. The system may be further configured to receive a first input relating to the specific item and generate a probability score for at least two candidate items of the multiple candidate items in the response, based on at least the second input. The system may be further configured to select a selected item from the candidate items, based on the probability score for the selected item. The system may be further configured to output a reference to, or value representing, the selected item.

Abstract: Disclosed herein are system, method, and computer program product embodiments for the prediction of listing attributes for a for sale object (FSO). Data analyzed with respect to historical listings in a database is compared to attributes of the FSO using machine learning (ML) and artificial intelligence (AI), in order for the user to select a category related to the FSO, and for a listing generation module to generate a ultimately reduced set of listings related to the FSO, where the ultimately reduced set is used to predict a price for the FSO based on the selected category, and generate a listing for the FSO, wherein a listing for the FSO is monitored, and information from the monitoring is used as machine learning feedback to train and enhance predictions.

Abstract: Disclosed herein are system, method, and computer program product embodiments for probabilistic item matching and searching. A system including at least one processor may be configured to receive a data point relating to a specific item, generate a database query based on the data point, and receive a response to the database query. The response may include multiple candidate items relating to the specific item. The system may be further configured to receive a first input relating to the specific item and generate a probability score for at least two candidate items of the multiple candidate items in the response, based on at least the second input. The system may be further configured to select a selected item from the candidate items, based on the probability score for the selected item. The system may be further configured to output a reference to, or value representing, the selected item.

Abstract: Disclosed herein are system, method, and computer program product embodiments for probabilistic search biasing and recommendations. A system including at least one processor may be configured to receive an input relating to an identified item, generate a database query based on the input, and receive a response to the database query. The response may include information on comparable items similar to the identified item, and corresponding metadata for the comparable items. The corresponding metadata may include a range of values corresponding to the comparable items. The system may be further configured to generate a probability score for at least two values of the range of values, based at least on the corresponding metadata for the comparable items. The system may be further configured to output at least a suggested value based on at least the generated probability score for the range of values and prompt for further input.

Abstract: Disclosed herein are system, method, and computer program product embodiments for generating sellability and cancellability scores for selling objects on an electronic marketplace. An embodiment operates by receiving, from a mobile device, an image including an object for sale and determining information associated with the object for sale. Information associated with pre-identified objects is determined from information associated with pre-identified objects, where each of the pre-identified objects corresponds to the object for sale. A sellability score and/or a cancellability score is generated based at least in part on the information associated with the object for sale and the information associated with the pre-identified objects. The sellability score and the cancellability score are further provided to users.