Method and apparatus for recommendation by applying efficient adaptive matrix factorization

Abstract

A method, apparatus and computer-readable storage medium for determining one or more recommendations by applying efficient adaptive matrix factorization are disclosed. The method comprises causing, at least in part, an iterative performing of the following steps: a using of a current data set to optimize parameters used to adapt a current matrix factorization model by the end of the current time period, and a training of a current matrix factorization model and the current data set by the end of the current time period, based on the optimized parameters, to obtain an adapted matrix factorization model for service in a next time period.

Description

Background technique [0001] Service providers and device manufacturers (eg, wireless, cellular, etc.) are constantly challenged to deliver value and convenience to consumers, eg, by offering compelling network services. One such compelling web service is a service that provides users with recommendations on recommended content. Certain recommendation systems (eg, a collaborative recommendation model) may base recommendations to users on other users or on other items associated with the user based on various activities. Collecting information about users, items and activities allows recommendation service providers to collect large amounts of information to process and then use to generate recommendations. However, there are scalability issues caused by this recommendation model based on the massive computational issues required to process all the information, especially as new information such as additional activities associated with users and items are collected. There are ...

AI Technical Summary

Problems solved by technology

As such, service providers and device manufacturers face significant technical challenges in dealing with the scalability of recommendation models while maintaining accurate recommendations that users can confidently rely on

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