In various example embodiments, techniques for querying a product in a database of a publication system using a tail query are presented. An application interface module receives, from a device of a user, a tail query for a product in the publication system. The tail query has a common term and an uncommon term. A data storage interface module can access, from a search metrics database, a plurality of product categories associated with the common term, and access user behavior data, with the user behavior data including product views corresponding to past search queries in the publication system. A category predictor can determine a first product category from the plurality of product categories for the tail query based on the user behavior data and information derived from the search metrics database. A listing generator can generate an ordered list of product listings associated with the first product category based on the user behavior data
Techniques for prefetching operation cost based digital content and digital content with emphasis that overcome the challenges of conventional systems are described. In one example, a computing device may receive digital content representations of digital content from a service provider system, which are displayed on a user interface of the computing device. Thereafter, the computing device may also receive digital content as prefetches having a changed display characteristic as emphasizing a portion of the digital content based on a model trained using machine learning. Alternatively, the computing device may receive digital content as a prefetch based on a model trained using machine learning in which the model addresses a likelihood of conversion of a good or service and an operation cost of providing the digital content. Upon receiving a user input selecting one of the digital content representations, digital content is rendered in the user interface of the computing device.
In various example embodiments, a system and method for determining a spam publication using a spam detection system are presented. The spam detection system receives, from a device, an image of an item and an item attribute for the item. Additionally, the spam detection system extracts an image attribute based on the received image, and compares the item attribute and the image attribute. Moreover, the spam detection system calculates a confidence score based on the comparison. Furthermore, the spam detection system determines that the item attribute is incorrect based on the confidence score transgressing a predetermined threshold. In response to the determination that the item attribute is incorrect, the spam detection system causes presentation, on a display of the device, of a notification.
Images of items often include a significant amount of information that can be used in categorizing the item (e.g., as a book, a phone, or an accessory) or in identifying attributes of the item (e.g., the item?s color or style). A user provides a search query for listings of items. Images of items in listings previously selected by users providing the same search query are analyzed to identify the category or attributes of the depicted items. The identified category or attributes are added to the user?s search query. Listings matching the modified search query are provided to the user.
In various example embodiments, a system and method for determining a spam publication using a spam detection system are presented. The spam detection system receives, from a device, an image of an item and an item attribute for the item. Additionally, the spam detection system extracts an image attribute based on the received image, and compares the item attribute and the image attribute. Moreover, the spam detection system calculates a confidence score based on the comparison. Furthermore, the spam detection system determines that the item attribute is incorrect based on the confidence score transgressing a predetermined threshold. In response to the determination that the item attribute is incorrect, the spam detection system causes presentation, on a display of the device, of a notification.
In various example embodiments, a system and method for determining a spam publication using a spam detection system are presented. The spam detection system receives, from a device, an image of an item and an item attribute for the item. Additionally, the spam detection system extracts an image attribute based on the received image, and compares the item attribute and the image attribute. Moreover, the spam detection system calculates a confidence score based on the comparison. Furthermore, the spam detection system determines that the item attribute is incorrect based on the confidence score transgressing a predetermined threshold. In response to the determination that the item attribute is incorrect, the spam detection system causes presentation, on a display of the device, of a notification.
In various example embodiments, techniques for querying a product in a database of a publication system using a tail query are presented. An application interface module receives, from a device of a user, a tail query for a product in the publication system. The tail query has a common term and an uncommon term. A data storage interface module can access, from a search metrics database, a plurality of product categories associated with the common term, and access user behavior data, with the user behavior data including product views corresponding to past search queries in the publication system. A category predictor can determine a first product category from the plurality of product categories for the tail query based on the user behavior data and information derived from the search metrics database. A listing generator can generate an ordered list of product listings associated with the first product category based on the user behavior data.
