US 11,809,501 B2
Systems, apparatuses, and methods for providing a ranking based recommendation
Chandra Khatri, Atlanta, GA (US); Steven Hui Luan, Fremont, CA (US); Michael Tanaka, San Ramon, CA (US); and Praveen K. Boinapalli, Pleasanton, CA (US)
Assigned to eBay Inc., San Jose, CA (US)
Filed by eBay Inc., San Jose, CA (US)
Filed on Dec. 30, 2014, as Appl. No. 14/586,462.
Claims priority of provisional application 62/043,064, filed on Aug. 28, 2014.
Prior Publication US 2016/0063065 A1, Mar. 3, 2016
Int. Cl. G06F 16/9535 (2019.01); G06Q 30/02 (2023.01)
CPC G06F 16/9535 (2019.01) [G06Q 30/02 (2013.01)] 20 Claims
OG exemplary drawing
 
1. A system comprising:
one or more hardware processors; and
a non-transitory machine-readable medium storing instructions that, when executed by the one or more hardware processors, cause the one or more hardware processors of the system to perform operations comprising:
determining, using a machine-learning model, a quality score for each item listing of a plurality of item listings, the quality score being based on an item listing attribute including item freshness and a user attribute including a user engagement, via user interfaces of client devices, with one or more item listings included in a collection of item listings, wherein the item freshness indicates how recently a corresponding item listing was posted for sale;
identifying, based on the determined quality scores of the plurality of item listings, a plurality of candidate item listings from among the plurality of item listings;
determining a browser type of a plurality of browser types, the determined browser type used by a client device to access a website hosting the plurality of item listings;
generating contextual information for the client device that indicates one or more device parameters, the one or more device parameters indicating the determined browser type;
selecting one or more item listings from among the plurality of candidate item listings by filtering the plurality of candidate item listings, wherein the filtering includes:
determining whether user attributes are available;
responsive to determining that user attributes are not available, causing the one or more item listings to be selected after filtering the plurality of candidate item listings based on the determined quality scores and then filtering based on the contextual information; and
responsive to determining that user attributes are available, causing the one or more item listings to be selected after filtering the plurality of candidate item listings based on the determined quality scores and the user attributes, and then filtering based on the contextual information, wherein the contextual information indicates the determined browser type and the user attributes include a user interest relationship for the one or more item listings indicated by the determined browser type; and
causing display of the one or more selected item listings on a user interface displayed on the client device.