# US 10,372,759 B2 · Profile based content retrieval for recommender systems

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- Number: US 10,372,759 B2
- Document ID: US10372759B2
- Status: Granted
- Kind: B2
- Application number: 15/274,190
- Filed: 2016-09-23
- Published: 2017-03-16
- Granted: 2019-08-06
- Earliest claimed: 2010-06-15
- Assignees: Funke Digital Tv Guide GmbH
- Applicants: FUNKE DIGITAL TV GUIDE GmbH
- Inventors: Mauro Barbieri; Serverius Petrus Paulus Pronk; Jan Korst
- Web page: https://patentgrep.com/patent/US10372759B2

## CPC

- G06F 16/90335: {Query processing}
- G06F 16/2453: Query optimisation
- G06F 16/2455: Query execution
- G06F 16/78: Retrieval characterised by using metadata, e.g. metadata not derived from the content or metadata generated manually
- G06F 16/9535: Search customisation based on user profiles and personalisation
- G06F: ELECTRIC DIGITAL DATA PROCESSING (computer systems based on specific computational models G06N)

## Abstract

The present invention relates to an apparatus, a method and a computer program product for controlling a recommender system, wherein a user profile normally used by a recommender to predict user ratings is employed to generate a targeted query for the remote database yielding a set of results that can be scored by the recommender and provided as suggestions to the user.

## Claims

### Claim 1

An apparatus for controlling a recommender system, said apparatus comprising:
a feature-value extractor for analyzing a user profile and for extracting feature-value pairs which characterize a user of said recommender system;
a feature selector for selecting from said feature-value pairs extracted by said feature-value extractor those feature-value pairs which are most discriminative for said user profile,
a query composer for composing, based on feature-value pairs extracted by said feature-value extractor and selected by said feature selector, a query to be sent to at least one content source; and
a recommender engine for rating results obtained in response to said query so as to provide a recommendation for said user;
wherein said feature-value selector is adapted to use a Relief algorithm as a feature selection algorithm for selecting said most discriminative feature-value pairs.

### Claim 2 (depends on 1)

The apparatus according to claim 1, wherein said feature-value extractor is adapted to extract feature-value pairs based on positive user ratings.

### Claim 3 (depends on 1)

The apparatus according to claim 1, wherein said query composer is adapted to expand said query using a thesaurus or an ontology.

### Claim 4 (depends on 1)

The apparatus according to claim 1, wherein said query composer is adapted to extend said query to categories other than those of said user profile.

### Claim 5 (depends on 4)

The apparatus according to claim 4, wherein cross-domain recommendations are provided.

### Claim 6 (depends on 1)

The apparatus according to claim 1, wherein said user profile has been derived from said recommender system.

### Claim 7 (depends on 1)

The apparatus according to claim 1, wherein said query composer is configured count co-occurrences of feature-value pairs, and to combine only those feature-value pairs with a sufficient co-occurrence count in the query.

### Claim 8

A method of controlling a recommender system, said method comprising:
analyzing a user profile and extracting feature-value pairs which characterize a user of said recommender system;
selecting from said extracted feature-value pairs those feature-value pairs which are most discriminative for said user profile;
composing based on extracted and selected feature-value pairs a query to be sent to at least one content source; and
rating results obtained in response to said query so as to provide a recommendation for said user; wherein a Relief algorithm is used as a feature selection algorithm for selecting said most discriminative feature-value pairs.

### Claim 9 (depends on 8)

The method according to claim 8, further comprising counting co-occurrences of feature-value pairs that appear in a same item and combining only those feature-value pairs with a sufficient co-occurrence count in said query.

### Claim 10 (depends on 8)

The method according to claim 8, wherein said composing comprises extending said query to categories other than those of said user profile.

### Claim 11 (depends on 10)

The method according to claim 10, further comprising providing cross-domain recommendations.

### Claim 12 (depends on 8)

A non-transitory computer readable medium stored with code, which when executed by a processor of a computer device, causes the computer device to perform the method of claim 8.