Client, server, and client-server system adapted for updating a client-item matrix

Abstract

A client (i) adapted for updating individual elements (rij) of a client-item matrix (R) by means of Collaborative Filtering, R = XTY. The matrix (R) comprises a plurality of individual elements (rij),each individual element relating to a specific client (i) and a specific item (j). The client (i) utilizes an individual part (Ai) of a first model component (A) and at least one element of local client data (ui). The first model component (A) is a factor matrix A = X(i, k) comprising a plurality of client factor vectors (xi). The individual part (Ai) of the first model component (A) is the client factor vector (xi) for said client (i). The client (i) is connected to a server utilizing a second model component (B) and a global set of items (j1,..., jM). The second model component (B) is a factor matrix B = Y(J, k) comprising a plurality of item factor vectors (yj). The client (i) is configured to assign the individual part (Ai) of the first model component (A) to the client (i) and download a second model component (B) from the server. Thereafter, an updated individual part (Ai2) of the first model component (A) is calculated, as well as an individual value for each item (j1,..., jM).An evaluation of the value is uploaded to the server such that an updated second model component (B2) is calculated by the server. The updated second model component (B2) is downloaded from the server, and a new updated individual part (Ai3) of the first model component (A) is calculated.

Description

technical field [0001] The present invention relates to an improved client, server and client-server system allowing updating of individual elements of a client-item matrix. Background technique [0002] A client-server system is a structure in which the tasks of the system are divided between a service provider (ie server) and a service requester (ie client). The server may run one or more programs that share resources with the client. On the other hand, the client does not share any of its resources, but requests content or service functions from the server. The clients, i.e. user devices such as mobile phones or tablets, are an important part of the machine learning process used in such client-server systems because each client is a source of data that is used to build the The models used in the machine learning process and the results that produce said models. [0003] The results can be used to generate recommendations of one or a few specific items taken from a larg...

AI Technical Summary

Problems solved by technology

[0008] In addition, collecting gigabytes of user data for a large number of clients every day and storing and using this data securely requires expensive infrastructure and management solutions

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