US 12,248,507 B2
Method, electronic device, and computer program product for content recommendation
Zijia Wang, WeiFang (CN); Jiacheng Ni, Shanghai (CN); Jinpeng Liu, Shanghai (CN); and Zhen Jia, Shanghai (CN)
Assigned to Dell Products L.P., Round Rock, TX (US)
Filed by Dell Products L.P., Round Rock, TX (US)
Filed on Jan. 19, 2022, as Appl. No. 17/578,838.
Claims priority of application No. 202111625650.1 (CN), filed on Dec. 28, 2021.
Prior Publication US 2023/0205802 A1, Jun. 29, 2023
Int. Cl. G06F 16/435 (2019.01); G06F 16/2457 (2019.01)
CPC G06F 16/435 (2019.01) [G06F 16/24578 (2019.01)] 20 Claims
OG exemplary drawing
 
1. A method for content recommendation, comprising:
determining, in at least one processing device of an Internet platform, a similarity between a first recommendation result and a second recommendation result for a content set, the first recommendation result and the second recommendation result being determined based on different recommendation techniques and respectively indicative of a recommendation degree for each of a plurality of content items in the content set, wherein the similarity is computed in the form of at least one vector of values indexed by identifiers of respective ones of the content items in the content set;
adjusting, in the at least one processing device of the Internet platform, the second recommendation result using the similarity;
determining, in the at least one processing device of the Internet platform, a target recommendation result for the content set based on the first recommendation result and the adjusted second recommendation result;
providing the target recommendation result from the at least one processing device of the Internet platform to a user device over a network; and
responsive to user interaction with a display presenting the target recommendation result at the user device, providing access to corresponding content at least in part via the at least one processing device of the Internet platform;
wherein determining the similarity between the first and second recommendation results comprises:
determining a first vector comprising a first set of indications based on the first recommendation result, the first vector being indexed by the identifiers of respective ones of the content items in the content set;
determining a second vector comprising a second set of indications based on the second recommendation result, the second vector being indexed by the identifiers of respective ones of the content items in the content set; and
determining the similarity based at least in part on a distance measure computed between the first and second vectors;
wherein adjusting the second recommendation result using the similarity comprises:
determining a modification factor based on the similarity; and
adjusting the second recommendation result based on the modification factor to alter an impact of the second recommendation result in determining the target recommendation result;
wherein adjusting the second recommendation result based on the modification factor comprises computing updated values for respective ones of the second set of indications of the second vector as a function of previous values for the respective ones of the second set of indications and the modification factor.