METHOD OF PROVIDING CONTENT ITEMS

• US 20100274849A1
• Filed: 07/02/2010
• Published: 10/28/2010
• Est. Priority Date: 11/08/2005
• Status: Active Grant

• Current Assignee: Intel Corporation
• Original Assignee: RealNetworks GmbH (RealNetworks, Inc.)
• Inventors: Inoto, Ruben, Sighart, Harald, Burgstaller, Rainer, Weiler, Christian
• Granted Patent: US 8,589,367 B2
• Time in Patent Office: Days
• US Class Current: 709/203
• CPC Class Codes: G06F 16/3326 using relevance feedback from the user, e.g. relevance feedback on documents, documents sets, document terms or passages
• CPC Class Codes: G06F 16/9535 Search customisation based on user profiles and personalisation
• CPC Class Codes: G06F 16/9536 Search customisation based on social or collaborative filtering

Abstract

A method for providing content items located on a server to a client is disclosed.

Claims

1. A method for providing content items located on a server to a client on a mobile device, the server being able to communicate with the client via a communication network, comprising the following steps:

a) transferring a first content recommendation list request from the client to the server;

b) generating a first portion of a content recommendation list comprising several content recommendation list entries arranged in an ordered sequence at the server in dependence on a user feedback profile, each content recommendation list entry representing a content item;

c) transferring the first portion of the content recommendation list from the server to the client;

d) providing a visual representation of the content recommendation list entries in the first portion of the content recommendation list through a client user interface, each entry being selectable and arranged such that for each entry, user feedback may be generated and corresponding user feedback information sent to the server; and

e) expanding the partially transferred content recommendation list at the server by generating a second portion of the content recommendation list comprising a plurality of additional content recommendation list entries based on the user feedback information.