METHOD AND SYSTEM FOR RECOMMENDATION OF CONTENT ITEMS

• US 20100281025A1
• Filed: 05/04/2009
• Published: 11/04/2010
• Est. Priority Date: 05/04/2009
• Status: Abandoned Application

• Current Assignee: Google Technology Holdings LLC (Alphabet Inc.)
• Original Assignee: Motorola, Inc. (Motorola Solutions, Inc.)
• Inventors: Menemenis, Fotis, Kompatsiaris, Ioannis, Papadopoulos, Symeon, Kalfas, George, Bratu, Ben M., Tsatsou, Dorothea, Davis, Paul C.
• Application Number:
  US12/434,842
• Publication Number:
  US 20100281025A1
• Time in Patent Office: Days
• US Class Current: 707/733
• CPC Class Codes: G06F 16/335 Filtering based on additional data, e.g. user or group profiles filtering in web context G06F16/9535, G06F16/9536
• CPC Class Codes: G06Q 30/02 Marketing; Price estimation or determination; Fundraising

Abstract

A method of generating recommendations for content items comprises providing a domain ontology where concepts are characterized by a term vector with terms and associated weights. Associated term sets, each of which comprises a set of terms that characterize a content item, are further provided. A concept set is generated for each associated term set by determining the concepts of the domain ontology that match the terms of the associated term set. In addition, a user profile for a user is provided where the user profile comprises at least some of the concepts of the ontology coupled with preference weights. Recommendations for content items are generated based on the plurality of associated concept sets and the user profile. The invention may allow improved and/or facilitated generation of recommendations from text based characterizing data.

Claims

1. A method of generating recommendations for content items, the method comprising:

providing a domain ontology comprising a plurality of interrelated concepts, each concept of the plurality of interrelated concepts being represented by a term vector comprising at least one term and an associated weight for each term;

providing a plurality of associated term sets, each associated term set of the plurality of associated term sets comprising a set of terms characterizing a content item of a group of content items;

generating a plurality of associated concept sets for the group of content items by determining for each of at least some of the plurality of associated term sets a set of concepts comprising concepts of the domain ontology matching terms of the associated term set;

providing a user profile for a user, the user profile comprising user preference weights associated with at least some concepts of the domain ontology;

generating recommendations for at least one content item from the group of content items in response to the plurality of associated concept sets and the user profile.

2. The method of claim 1 wherein the step of generating the plurality of associated concept sets comprises:

including a first concept in a first associated concept set for a first content item in response to a detection of a first term of a first term vector for the first concept matching a term of a first associated term set for the first content item.

4. The method of claim 1 wherein the associated weight for a first term of a first term vector for a first concept of the domain ontology is indicative of a probability of the first term being relevant for characterizing the first concept.

5. The method of claim 1 further comprising

providing a lexical graph, the lexical graph comprising nodes corresponding to terms and edges corresponding to a relationship between terms of nodes connected by the edge;

and

adding terms to term vectors of the domain ontology based on the lexical graph.

6. The method of claim 5 further comprising for at least a first concept of the domain ontology performing the steps of:

identifying a first set of terms in the lexical graph matching the first concept;

for at least a first term of the first set of terms determining a neighbor set of terms belonging to a graph neighborhood of the first term;

generating a candidate set of terms comprising terms of the set of neighborhood terms; and

selecting terms for a first term vector for the first concept from the candidate set.

7. The method of claim 6 further comprising determining a weight for a selected term from the candidate set in response to at least one of a node value and an edge value for the selected term in the lexical graph.

8. The method of claim 6 further comprising determining a weight for a selected term from the candidate set in response to an edge value for an edge included in a connection between the selected term and the first term in the lexical graph.

9. The method of claim 6 wherein selecting the neighbor set of terms comprises further selecting the neighbor set of terms in response to at least one of a node value and an edge value.

10. The method of claim 9 wherein selecting the neighbor set of terms comprises selecting only terms having a connection to the first term for which a combined edge value of edges of the connection meet a criterion.

11. The method of claim 6 wherein a match criterion for identifying the first set of terms comprises a requirement for at least one term of the first term to match a term for that term to be included in the first set of terms.

12. The method of claim 5 wherein edges between nodes have a value indicative of a strength of association between terms of the nodes.

13. The method of claim 5 wherein edges between nodes have a value indicative of a co-occurrence frequency for terms of the nodes.

14. The method of claim 5 further comprising updating the lexical graph in response to term vectors characterizing content items consumed by the user.

15. The method of claim 1 wherein the step of determining recommendations comprises applying a rule based reasoning algorithm to the plurality of associated concept sets and the user profile.

16. The method of claim 15 wherein the rule based reasoning algorithm is a fuzzy logic rule based reasoning algorithm.

17. The method of claim 1 further comprising:

monitoring consumption of content items by the user;

providing a content item term set characterizing a first content item consumed by the user;

determining a first concept of the user profile for which a term vector comprises at least a first term matching at least one term of the content item term set; and

modifying a preference weight for the first concept.

18. The method of claim 17 further comprising:

monitoring consumption of content items by the user;

providing a content item term set characterizing a first content item consumed by the user;

determining a first concept of the ontology having a term vector comprising at least a first term matching at least one term of the content item term set; and

modifying an associated weight for the first term.

19. A computer program product comprising instructions which, when run on a computer, will cause said computer to perform the method of claim 17.

3. The method of claim 3 wherein the inclusion of the first concept in the first associated concept set is further in response to an associated weight of the first term in the first term vector.

20. A system for generating recommendations for content items, the system comprising:

a unit for providing a domain ontology comprising a plurality of interrelated concepts, each concept of the plurality of interrelated concepts being represented by a term vector comprising at least one term and an associated weight for each term;

a unit for providing a plurality of associated term sets, each associated term set of the plurality of associated term sets comprising a set of terms characterizing a content item of a group of content items;

a unit for generating a plurality of associated concept sets for the group of content items by determining for each of at least some of the plurality of associated term sets a set of concepts comprising concepts of the domain ontology matching terms of the associated term set;

a unit for providing a user profile for a user, the user profile comprising user preference weights associated with at least some concepts of the domain ontology; and

a unit for generating recommendations for at least one content item from the group of content items in response to the plurality of associated concept sets and the user profile.