GENERATION OF PERSONALIZED RECOMMENDATIONS

• Main Authors: FLANAGAN, Adrian, AMMAD-UD-DIN, Muhammad, TAN, Kuan Eeik, ALI KHAN, Suleiman
• Format: Patent
• Language: English
• Published: 19.01.2023
• Subjects: CALCULATING; COMPUTING; COUNTING; DATA PROCESSING SYSTEMS OR METHODS, SPECIALLY ADAPTED FORADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORYOR FORECASTING PURPOSES; PHYSICS; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE,COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTINGPURPOSES, NOT OTHERWISE PROVIDED FOR

There is provided a client adapted for generating personalized cold-start federated recommendations for a user of the client. The client generates personalized recommendations for three cold-start scenarios, namely i) recommendation of an item to a new user which does not have any history of user-item interactions, ii) recommendation of a new item to a set of the most prospective users where the item has no history, and iii) recommendation of a new item to a new user, where there is no history associated with either the user or the item. The client uses a federated multi-view matrix factorization method to generate cold-start recommendations without transferring users' personal data to a remote server. Further, a server and a content provider for assisting in generating the personalized cold-start recommendations are provided in a federated set-up according to some aspects.