Learning Ontology-Based User Profiles: A Semantic Approach to Personalized Web Search

Transcription

1 1 / 33 Learning Ontology-Based User Profiles: A Semantic Approach to Personalized Web Search Bernd Wittefeld Supervisor Markus Löckelt 20. July 2012

2 2 / 33 Teaser - Google Web History Web history associated with Google account Keeps track of search queries and visited websites Personalized search results displayed to the user Can be switched on, paused and off Can be deleted Cookie usage for users not signed in

3 Teaser - Google Web History 3 / 33

4 4 / 33 Introduction Outline 1 Introduction 2 Ontologies for Web Personalization 3 Search Personalization 4 Experimental Evaluation 5 Conclusion

5 5 / 33 Introduction Motivation

6 6 / 33 Introduction Motivation Tailor search results to users interest Maintain and update interest scores in a user profile

7 6 / 33 Introduction Motivation Tailor search results to users interest Maintain and update interest scores in a user profile Three crucial elements: User s short term information need Semantic knowledge about current domain User profile capturing long-term interests

8 7 / 33 Introduction Motivation Approach taken: Interest scores to existing concepts in a domain ontology Spreading activation algorithm to maintain scores

9 8 / 33 Ontologies for Web Personalization Outline 1 Introduction 2 Ontologies for Web Personalization 3 Search Personalization 4 Experimental Evaluation 5 Conclusion

10 9 / 33 Ontologies for Web Personalization Ontologies

11 Ontologies for Web Personalization Spreading Activation Algorithms 10 / 33

12 11 / 33 Ontologies for Web Personalization Ontological User Profiles Unified context model Instance of pre-existing reference domain ontology Concepts annotated with interest scores Updated based on user s behavior

13 12 / 33 Ontologies for Web Personalization Ontological User Profiles Information gathered implicitly through observing browsing behavior over time collecting interesting sites

14 12 / 33 Ontologies for Web Personalization Ontological User Profiles Information gathered implicitly through observing browsing behavior over time collecting interesting sites Factors for ranking can be frequency of visits time spent on the page bookmarking

15 13 / 33 Ontologies for Web Personalization Context Model Portion of an ontological user profile representing node Music Nodes represented as pair (C j, IS(C j )) C j concept in reference ontology IS(C j ) Interest score

16 14 / 33 Ontologies for Web Personalization Representation of Reference Ontology Open Directory Project (ODP) - Community driven web-classification

17 15 / 33 Ontologies for Web Personalization Representation of Reference Ontology Compute a term vector n containing most frequent terms for concept n and its subconcepts with corresponding weight Stop list contains semantically non relevant high frequency words Porter stemming to reduce words to their stems

18 16 / 33 Search Personalization Outline 1 Introduction 2 Ontologies for Web Personalization 3 Search Personalization 4 Experimental Evaluation 5 Conclusion

19 17 / 33 Search Personalization Re-ranking search results A set of query results is obtained from a search engine For each document in result a term vector r is computed as before Similarity of document and query is computed Compute similarity of document and each concept Rank-score for document is product of interest score for concept, similarity of document to query and similarity of specific concept to query

20 18 / 33 Experimental Evaluation Outline 1 Introduction 2 Ontologies for Web Personalization 3 Search Personalization 4 Experimental Evaluation 5 Conclusion

21 19 / 33 Experimental Evaluation Experimental Data Sets Open Directory contained concepts as of December 2006 Test-Set: Branching factor of four, six levels deep = 563 concepts and documents

22 19 / 33 Experimental Evaluation Experimental Data Sets Open Directory contained concepts as of December 2006 Test-Set: Branching factor of four, six levels deep = 563 concepts and documents pre-processed into training set - representation of reference ontology, 5041 documents test set - document collection for searching, 3067 documents profile set - spreading activation, 2118 documents

23 20 / 33 Experimental Evaluation User Profile Convergence Do the interest scores for individual concepts in the ontological profile converge?

24 20 / 33 Experimental Evaluation User Profile Convergence Do the interest scores for individual concepts in the ontological profile converge? Single profile document for every concept from profile set 25 rounds in spreading activation algorithm = signal concept Repeated experiment 50 times with distinct signal concepts Arithmetic mean and variance

25 21 / 33 Experimental Evaluation User Profile Convergence Arithmetic mean and variance of interest scores

26 22 / 33 Experimental Evaluation User Profile Accuracy Do the changes in interest scores accurately reflect user interest in specific topics?

27 22 / 33 Experimental Evaluation User Profile Accuracy Do the changes in interest scores accurately reflect user interest in specific topics? One signal concept from training set Single document belonging to signal concept from profile set Interest scores for all concepts monitored 30 repetitions of the experiment for the same signal concept Arithmetic mean and variance

28 23 / 33 Experimental Evaluation User Profile Accuracy

29 24 / 33 Experimental Evaluation User Profile Accuracy Two signal concepts from training set Single document belonging to signal concept from profile set 30 repetitions of the experiment alternating the signal concept every 5 iterations

30 25 / 33 Experimental Evaluation User Profile Accuracy

31 26 / 33 Experimental Evaluation Re-ranking Web Search Results Can the semantic evidence provided by the ontological profiles be used to effectively re-rank Web search results to present the user with a personalized view?

32 26 / 33 Experimental Evaluation Re-ranking Web Search Results Can the semantic evidence provided by the ontological profiles be used to effectively re-rank Web search results to present the user with a personalized view? Standard search on test set with queries Documents from profile set used to simulate user interest in concepts search results re-sorted based on ranking from user profile

33 26 / 33 Experimental Evaluation Re-ranking Web Search Results Can the semantic evidence provided by the ontological profiles be used to effectively re-rank Web search results to present the user with a personalized view? Standard search on test set with queries Documents from profile set used to simulate user interest in concepts search results re-sorted based on ranking from user profile effectiveness of re-ranking in terms of Top-n Recall = Top-n Precision = with n {100, 90, 80,..., 10} of relevant retrieved within n total of relevant documents of relevant retrieved within n n

34 Experimental Evaluation Re-ranking Web Search Results Keyword queries extracted from concept term vectors in training set Query of Terms Criteria Set 1 1 highest weighted term in concept term vector Set 2 2 two highest weighted terms in concept term vector Set 3 3 three highest weighted terms in concept term vector Set 4 2 or more overlapping terms within highest weighted 10 terms 27 / 33

35 28 / 33 Experimental Evaluation Re-ranking Web Search Results

36 29 / 33 Experimental Evaluation Re-ranking Web Search Results

37 30 / 33 Experimental Evaluation Discussion of Experimental Results Queries intentionally short Significant improvement for single word queries Query terms disambiguated by semantic evidence in ontological user profiles Stability of the approach was shown

38 31 / 33 Conclusion Outline 1 Introduction 2 Ontologies for Web Personalization 3 Search Personalization 4 Experimental Evaluation 5 Conclusion

39 32 / 33 Conclusion Framework for contextual information access using ontologies Semantic knowledge in ontologies combined with long-term user profiles effectively tailor search results based on user s interest Stability and adaptation to user s interest over long-term needs to be analyzed further

40 33 / 33 Conclusion Questions?

41 34 / 33 Representation of Reference Ontology Document d from training data represented as d = (w1, w 2,..., w k ) w i = tf i log( N n i ) tf i being the frequency of term i in document d N the total number of documents in the training set n i the number of documents containing term i. Vectors are normalized to a unit length.

42 35 / 33 Representation of Reference Ontology Aggregate representation of the concept hierarchy: S(n) the set of sub-concepts under concept n {d1 n, d 2 n,..., d k n } the individual documents indexed under concept n Docs(n) = [ n S(n)Docs(n ) ] {d n 1, d n 2,..., d n k } n = d Docs(n) d Docs(n)

43 36 / 33 Context Model Portion of an ontological user profile representing node Music Nodes represented as pair (C j, IS(C j )) C j concept in reference ontology IS(C j ) Interest score