1 / 50

Contents

Contents. Contents. What is a proper recommendation ? Defining Similarities for OLAP Sessions The SROS R ecommender System Assessing the quality of the recommender system Conclusion & Perspectives. Contents. Contents. What is a proper recommendation ?

minh
Télécharger la présentation

Contents

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. Contents Contents Whatisa properrecommendation? Defining Similarities for OLAP Sessions The SROS Recommender System Assessing the quality of the recommender system Conclusion & Perspectives

  2. Contents Contents Whatis a properrecommendation ? Defining Similarities for OLAP Sessions SROS System Assessing the quality of the recommender system Conclusion & Perspectives

  3. Whatis a properrecommendation ? > RecommenderSystems (E-commerce) RecommenderSystems (E-commerce) Here are the moviesthatI rated: Per qualchedollaro in piu (1) Recommendation ? ReservoirDogs (0.7) Requiem for a Dream (0.7) DjangoUnchained (0.7) • Threeapproaches : • Content-based • Collaborative Filtering • Hybrid Best approaches Qualitymeasuresalreadyproposed Aleksander

  4. Whatis a properrecommendation ? > RecommenderSystems (Databases) Recommendations in a DatabaseContext LOG • Query: • Declarativelanguage • Over a schema Analysis Sessions Aleksander Multi-user context • How to recommendqueries or sessions? • In a multi-user context • Leveragingthe schema ColloborativeFilteringapproach Usingquery expression to be efficient

  5. Whatis a properrecommendation ? > QueryRecommendation in Data Warehouses> ModelingMultidimensional data ModellingMultidimensional Data Query: fragment-based City AllRaces Year Group-by set AllOccs Sex Sex=Female Selection set City=NewYork Measure set AvgIncome CostGas One cube withhierarchies, and a set of measures: Session: Income PropInsr PerWt CostGas CostWtr CostElec Query 1 Query 3 Query 2

  6. Whatis a properrecommendation ? > QueryRecommendation in Databases > SnipSuggestapproach SnipSuggest([Khoussainova et al, 2010])approach LOG Association rules FROM Movies WHERE director=‘Quentin Tarantino’ WHERE genre=‘Western’ Conf:1/5 FROM Movies WHERE director=‘Quentin Tarantino’ WHERE genre=‘Thriller’ Conf:4/5 SELECT title WHERE director=‘Quentin Tarantino’ WHERE year>1995 Conf:4/5 TOP-K SELECT title, genre FROM Movies WHERE director = ‘Quentin Tarantino’ AND Aleksander

  7. Whatis a properrecommendation ? > QueryRecommendation in Data Warehouses > [Sapia, 2000] & [Aufaure et al., 2013] approaches • PROMISE [Sapia, 2000] & [Aufaure et al., 2013] approaches LOG Clusters of queries Query c1 Current Query Aleksander

  8. Whatis a properrecommendation ? > QueryRecommendation in Data Warehouses > [Giacometti et al., 2009] & [Negre, 2009] approaches • [Giacometti et al., 2009] & [Negre, 2009]approaches Generic Framework : Current Session Log Edit Distance Session 1 Session 2 Hausdorff Distance Query 1 Query 2 Distance in Hierarchy Position in a cube Position in a cube

  9. Whatis a properrecommendation ? > QueryRecommendation in Databases & Data Warehouses > Approaches QueryRecommendation in Databases & DataWarehouses SROS Yes Query Expression Sequence of queries Similarity-based

  10. Whatis a properrecommendation ? > QueryRecommendation in Databases & Data Warehouses QueryRecommendation in Databases & DataWarehouses • A DesirableRecommendation: • How to propose an informative recommendationto the user? • Proposal: • Recommend a sequence of queries • How to find log sessions close to the current session? • Proposal: • Define a two-levelsimilaritymeasurebetween sessions • How to be consistent with the context of the current session? • Proposal: • Adapt the recommendationwiththat of the current session • How to providea relevant recommendation to the user? • Proposal: • Definequalitycriteriaassessing the recommendation

  11. Contents Contents Whatis a properrecommendation ? Defining Similarities for OLAP Sessions SROS System Assessing the quality of the recommender system Conclusion & Perspectives

  12. DefiningSimilarities for OLAP Sessions > Requirementsfor Similarity-based Recommendation of OLAP Sessions Requirements for SimilarityMeasuresbetween OLAP Sessions • Intuitively: • The order of queriesisrelevant • Recentqueries are more relevant thanolderqueries • How to define a similaritymeasure for sequences? • Classically: Dice, Edit Distance, soft-TFIDF, SubsequenceAlignment • How to include the comparisonbetweenthe sequenceelements? • A two-levelapproachbased on similaritiesbetweenelements • Classically: Cosinesimilarity, Hamming Distance, Hausdorff Distance • No proposal for a measureusingqueryexpression!

  13. DefiningSimilarities for OLAP Sessions > SimilarityMeasurebetweenQueries SimilarityMeasurebetweenQueries [Aligon et al., KAIS, 2013] City AllRaces Year City AllRaces AllYear AllOccs Sex AllOccs Sex Sex=Female Sex=Female City=New-York Region=West AvgIncome CostGas AvgIncome MaxCostElec Query 2 Query 1

  14. DefiningSimilarities for OLAP Sessions > SimilarityMeasurebetweenSessions SimilarityMeasurebetween Sessions [Aligon et al., KAIS, 2013] • Differentclassicalapproachesextended to OLAP context: • Dice Coefficient

  15. DefiningSimilarities for OLAP Sessions > SimilarityMeasurebetweenSessions SimilarityMeasurebetween Sessions [Aligon et al., KAIS, 2013] • Differentclassicalapproachesextended to OLAP context: • Dice Coefficient

  16. DefiningSimilarities for OLAP Sessions > SimilarityMeasurebetweenSessions SimilarityMeasurebetween Sessions [Aligon et al., KAIS, 2013] • Differentclassicalapproachesextended to OLAP context: • soft-TF-IDF TF-IDF Scores Log

  17. DefiningSimilarities for OLAP Sessions > SimilarityMeasurebetweenSessions SimilarityMeasurebetween Sessions [Aligon et al., KAIS, 2013] • Differentclassicalapproachesextended to OLAP context: • Levenstein Distance • Based on a matrix of costcomputingbetweentwo sessions the minimal number of: • Insertion (I) • Deletion (D) • Substitution (S) (I) (S) (D) matching

  18. DefiningSimilarities for OLAP Sessions > SimilarityMeasurebetweenSessions SimilarityMeasurebetween Sessions [Aligon et al., KAIS, 2013] • Differentclassicalapproachesextended to OLAP context: • SubSequenceAlignment • To find the optimal local alignment whose result is a trade-off between the cost of gap and mismatching GAP No Alignment -> Minimal similarity Perfectalignement -> Maximal similarity Good alignement -> Good similarity Very Bad alignement -> Verylowsimilarity

  19. DefiningSimilarities for OLAP Sessions > SimilarityMeasurebetweenSessions Extension of SubsequenceAlignment in the OLAP context Time-discountingfunction Gap Penalty : variable score ensuring few gaps and long alignments

  20. DefiningSimilarities for OLAP Sessions > SimilarityMeasurebetweenSessions > Subjective & Objective Tests AssessedwithSubjective Tests • 41 students & researchers from ebiss’2011 summer school • Giventheirsimilaritydegrees(Low, Fair, Good, High) between a currentquery and differentqueries • Giventheirsimilaritydegrees (Low, Fair, Good, High) betweena current session and different sessions [ebiss2011 SummerSchool] Opinions: QuerySimilarities Session Similarities questionnaire

  21. DefiningSimilarities for OLAP Sessions > SimilarityMeasurebetweenSessions > Subjective & Objective Tests AssessedwithSubjective Tests

  22. DefiningSimilarities for OLAP Sessions > SimilarityMeasurebetweenSessions > Subjective & Objective Tests Assessedwith Objective Tests

  23. DefiningSimilarities for OLAP Sessions > SimilarityMeasurebetweenSessions > Subjective & Objective Tests Assessedwith Objective Tests

  24. Contents Contents Whatis a properrecommendation ? Defining Similarities for OLAP Sessions SROS System Assessing the quality of the recommender system Conclusion & Perspectives

  25. SROS System SROS System • Composed of three phases: • Selection : Select a set of possible futures • Ranking : Rank the futures fromfrequentsimilarities • Tailoring:Adapt the best future to the current session

  26. SROS System > Selection SROS System Current Session Current Session Aleksander Futures Future Former Close Sessions Log Session 1 Selection Log

  27. SROS System > Ranking SROS System Current Session 2 Ranking Aleksander Futures 3 2 Former Close Sessions 1 Selection 4 Log

  28. SROS System > Ranking SROS System Current Session 2 Ranking Aleksander Futures Futures 3 2 1 Former Close Sessions 1 Selection 4 Log

  29. SROS System > Ranking SROS System Current Session 2 Ranking Aleksander Futures Futures 3 0 2 2 1 1 2 0 2 0 1 Former Close Sessions 1 Selection 4 Log

  30. AdaptedRecommendation SROS System > Tailoring SROS System 3 Tailoring Association rules Current Session • Rules of Type 1 Year=2005 Year=2002 2 Ranking Aleksander Futures 3 AllCities Region State Year=2002 Year=2002 Year=2002 AvgIncome AvgIncome AvgCostWtr 2 • Current Session City City State Region State Former Close Sessions 1 1 Year=2002 Year=2005 Year=2002 Year=2002 Year=2005 Year=2005 Year=2005 Year=2005 Selection AvgCostGas AvgIncome AvgIncome AvgCostGas AvgCostGas • Log Session • Recommendation Log

  31. AdaptedRecommendation SROS System > Tailoring SROS System 3 Tailoring Association rules Current Session • Rules of Type 2 AvgIncome Year=2002 Aleksander 2 Ranking 3 AllCities Region State Year=2002 Year=2002 Year=2002 AvgIncome AvgIncome AvgCostWtr 2 • Current Session City City State Former Close Sessions 1 1 Year=2002 Year=2002 Year=2002 Selection AvgCostGas AvgCostGas AvgCostGas AvgIncome AvgIncome AvgIncome Log • Recommendation

  32. Contents Contents Whatis a properrecommendation ? Defining Similarities for OLAP Sessions SROS System Assessing the quality of the recommender system Conclusion & Perspectives

  33. Assessing the quality of the recommender system > QualityMeasures QualityMeasures • Different Quality criteria : • Novelty Recommendation LOG

  34. Assessing the quality of the recommender system > QualityMeasures QualityMeasures AllSexes • Different Quality criteria : • Novelty • Adaptation AvgCostElec AllCities Race AllOccs Year Year=2002 • Current Session Sex AvgIncome AllCities Race AllOccs Year Year=2005 • Recommendation

  35. Assessing the quality of the recommender system > QualityMeasures QualityMeasures • Different Quality criteria : • Novelty • Adaptation • Accuracy & Coverage ExpectedRecommendations Recommendations

  36. Assessing the quality of the recommender system > QualityMeasures QualityMeasures • An effective recommendation system finds a trade-off between: • A novelty whose recommendation provides new informative fragments from the log • An adaptation preserving the consistency with the current session

  37. Assessing the quality of the recommender system > Synthetic & Real Logs BehaviorGeneration • Explorativetemplate • Goal-orientedtemplate IQ: Initial Query FQ: Final Query SQ: SurprisingQuery RQ: RandomQuery IQ IQ Shortest OLAP Path RQ One randomoperation RQ • Synthetic log : • including Explorative and Goal-Oriented templates, randomly chosen • composed of 200 sessions (a total of 2950 queries) SQ RQ RQ RQ RQ FQ RQ

  38. Assessing the quality of the recommender system > Synthetic & Real Logs Real Logs • Questionnaires performed by Master’s students : • 40 students from University of Bologna and University of Tours • Devising OLAP sessions answering to questions of different complexities • A filtering is essential : (145 sessions after filtering)

  39. Assessing the quality of the recommender system > Effectiveness Test > Principle Effectiveness Test Principle • Conducted with the following logs : • The synthetic log • The logs devised by the student • N-fold cross validation Log Current session Expected Recommendation

  40. Assessing the quality of the recommender system > Effectiveness Test > AccuracyResult AccuracyResult Synthetic Log

  41. Assessing the quality of the recommender system > Effectiveness Test > AccuracyResult AccuracyResult Student Log

  42. Assessing the quality of the recommender system > Effectiveness Test > NoveltyResult NoveltyResult Novelty Measure

  43. Assessing the quality of the recommender system > Effectiveness Test > Adaptation Result Adaptation Result Adaptation Measure

  44. Contents Contents Whatis a properrecommendation ? Defining Similarities for OLAP Sessions SROS System Assessing the quality of the recommender system Conclusion & Perspectives

  45. Conclusion & Perspectives Conclusion • Requirements for Recommendation and Similarity measures • Definitions of query and session similarities for OLAP • Assessed with subjective and objective tests • Query similarity based on the structure • Session similarity extending Subsequence Alignment • Proposal of a similarity-based recommender system of OLAP sessions based on three phases: • Selection • Ranking • Tailoring • The recommender system is assessed in terms of effectiveness: • Quality Measure Proposals • The recommendations are: • well adapted to the context of the current session • preserve the logic of the log session to provide new information to the user • very accurate, for very different contexts of log density

  46. Conclusion & Perspectives A tool for Session Design Assistance • How to remedyto the cold startproblem ? • Solution : exploring the former session to initiate the first queries of a current session • Problem : how to navigatebetween the sessions ? • Proposal: organizing sessions in a hierarchical structure and definingbrowsingoperators • Problem : how to represent groups of sessions ? • Proposal : usingsummarization techniques to reduce the number of queries and sessions but also to design a representative session ([Aligon & Marcel, EDA’2012], [Aligon et al., PersDB’2012])

  47. Conclusion & Perspectives A benchmark of OLAP sessions • Development of a platerform to assess the quality of an analytical session over a cube: • Allowing to measure the effectiveness of user-centricapproaches • Finding a more precisedefinition of OLAP session

  48. Conclusion & Perspectives Adaptation of the Recommender System in othercontexts • Adaptation in Data-Miningcontextwhere sessions canbeconsidered as sequences of complextasks • Supposing to adapt the session similarity • In the Web, sequencescanbeanalysissequences over social networks : • Takingintoaccount the relationshipsbetweenusers • Considering a similaritymeasurebetweenusersto define user profiles

  49. Thankyou for your attention !

More Related