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Random Indexing and Quantum Negation for TV-Shows Retrieval and Classification

My final presentation at the end of the internship in Philips Research (Eindhoven)

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High Tech Campus, Philips Research
                         Eindhoven, Netherlands




Random Indexing and Quantum
   Negation for TV-Shows
  Retrieval and Classification
             Cataldo Musto, Ph.D. Student
  cataldomusto@di.uniba.it - cataldo.musto@philips.com
           University of Bari “Aldo Moro” (Italy), SWAP Research Group
          Philips Research Center - Eindhoven (Netherlands) - HI&E Group
                                  14.07.11
outline
                •     part 1:    introduction
                     •      information overload, personalization, information filtering, recommender
                            systems

                •     part 2:    approaches
                     •      vector space model, random indexing, quantum negation

                •     part 3:    scenario
                     •      tv-show recommendation, description of the data, description of the tasks

                •     part 4:    experimental evaluation
                     •      results, discussion, future work



C.Musto: Random Indexing and Quantum Negation for TV shows Retrieval and Classification - Philips Research , Eindhoven (The Netherlands) - 14.07.11
part 1: introduction
                                       what are we talking about?




C.Musto: Random Indexing and Quantum Negation for TV shows Retrieval and Classification - Philips Research , Eindhoven (The Netherlands) - 14.07.11
TV
C.Musto: Random Indexing and Quantum Negation for TV shows Retrieval and Classification - Philips Research , Eindhoven (The Netherlands) - 14.07.11
text messages
C.Musto: Random Indexing and Quantum Negation for TV shows Retrieval and Classification - Philips Research , Eindhoven (The Netherlands) - 14.07.11
phone calls
C.Musto: Random Indexing and Quantum Negation for TV shows Retrieval and Classification - Philips Research , Eindhoven (The Netherlands) - 14.07.11

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Random Indexing and Quantum Negation for TV-Shows Retrieval and Classification

  • 1. High Tech Campus, Philips Research Eindhoven, Netherlands Random Indexing and Quantum Negation for TV-Shows Retrieval and Classification Cataldo Musto, Ph.D. Student cataldomusto@di.uniba.it - cataldo.musto@philips.com University of Bari “Aldo Moro” (Italy), SWAP Research Group Philips Research Center - Eindhoven (Netherlands) - HI&E Group 14.07.11
  • 2. outline • part 1: introduction • information overload, personalization, information filtering, recommender systems • part 2: approaches • vector space model, random indexing, quantum negation • part 3: scenario • tv-show recommendation, description of the data, description of the tasks • part 4: experimental evaluation • results, discussion, future work C.Musto: Random Indexing and Quantum Negation for TV shows Retrieval and Classification - Philips Research , Eindhoven (The Netherlands) - 14.07.11
  • 3. part 1: introduction what are we talking about? C.Musto: Random Indexing and Quantum Negation for TV shows Retrieval and Classification - Philips Research , Eindhoven (The Netherlands) - 14.07.11
  • 4. TV C.Musto: Random Indexing and Quantum Negation for TV shows Retrieval and Classification - Philips Research , Eindhoven (The Netherlands) - 14.07.11
  • 5. text messages C.Musto: Random Indexing and Quantum Negation for TV shows Retrieval and Classification - Philips Research , Eindhoven (The Netherlands) - 14.07.11
  • 6. phone calls C.Musto: Random Indexing and Quantum Negation for TV shows Retrieval and Classification - Philips Research , Eindhoven (The Netherlands) - 14.07.11
  • 7. internet navigation C.Musto: Random Indexing and Quantum Negation for TV shows Retrieval and Classification - Philips Research , Eindhoven (The Netherlands) - 14.07.11
  • 8. scenario • Daily interaction with electronic devices • eMail, Web navigation, Social media, instant messaging • Continuous flow of information • in 2007, 500.000 terabyte of information have been produced on the Web in one year • By including also telephone, radio, TV and so on we reach 18 exabytes of data! C.Musto: Random Indexing and Quantum Negation for TV shows Retrieval and Classification - Philips Research , Eindhoven (The Netherlands) - 14.07.11
  • 9. information overload • Consequences: cognitive overload • It is impossible to effectively deal with this surplus of information • It is difficult to quickly find the information we really need C.Musto: Random Indexing and Quantum Negation for TV shows Retrieval and Classification - Philips Research , Eindhoven (The Netherlands) - 14.07.11
  • 11. information filtering ” An information filtering system is a system that removes redundant of unwanted information from an information stream using automated methods ” Wikipedia. C.Musto: Random Indexing and Quantum Negation for TV shows Retrieval and Classification - Philips Research , Eindhoven (The Netherlands) - 14.07.11
  • 12. information filtering systems • How do they work? • Usually, in three steps • Training Step • User Modeling • Filtering C.Musto: Random Indexing and Quantum Negation for TV shows Retrieval and Classification - Philips Research , Eindhoven (The Netherlands) - 14.07.11
  • 13. Step 1: Training C.Musto: Random Indexing and Quantum Negation for TV shows Retrieval and Classification - Philips Research , Eindhoven (The Netherlands) - 14.07.11
  • 14. Step 2: User Modeling C.Musto: Random Indexing and Quantum Negation for TV shows Retrieval and Classification - Philips Research , Eindhoven (The Netherlands) - 14.07.11
  • 15. Step 3: Filtering C.Musto: Random Indexing and Quantum Negation for TV shows Retrieval and Classification - Philips Research , Eindhoven (The Netherlands) - 14.07.11
  • 16. recommender systems • A specific type of Information Filtering system that attempts to recommend information items (films, television, video on demand, music, books,  etc) that are likely to be of interest to the user • Everyday we interact with recommender systems, even if we do not know it! C.Musto: Random Indexing and Quantum Negation for TV shows Retrieval and Classification - Philips Research , Eindhoven (The Netherlands) - 14.07.11
  • 17. Amazon C.Musto: Random Indexing and Quantum Negation for TV shows Retrieval and Classification - Philips Research , Eindhoven (The Netherlands) - 14.07.11
  • 18. YouTube C.Musto: Random Indexing and Quantum Negation for TV shows Retrieval and Classification - Philips Research , Eindhoven (The Netherlands) - 14.07.11
  • 19. recommendation approaches • Content-based filtering • No interactions between users. Each user is an atomic entity • Prerequisite: each item to be recommended has to be described through a set of textual features • We store in a user profile the features that often occur in the items she like • Assumption: if a user usually likes items in whose description often occurs a specific feature we can assume that he will like that items also in the future • e.g. • If User_A likes a news with the features “Football” and “Internazionale FC” inside • We can recommend her other news about both Football or Internazionale FC C.Musto: Random Indexing and Quantum Negation for TV shows Retrieval and Classification - Philips Research , Eindhoven (The Netherlands) - 14.07.11
  • 20. part 2: approaches vector space model, random indexing,quantum negation C.Musto: Random Indexing and Quantum Negation for TV shows Retrieval and Classification - Philips Research , Eindhoven (The Netherlands) - 14.07.11
  • 21. vector space model • Introduced by Salton in 1975 • Given a set of M documents (items) d = (d1.....dM) • Given N features describing the documents • Each document (item) is represented in a an N- dimensional vector space • The whole corpus is represented in a N*M matrix called term/document matrix C.Musto: Random Indexing and Quantum Negation for TV shows Retrieval and Classification - Philips Research , Eindhoven (The Netherlands) - 14.07.11
  • 22. vector space model • VSM in a recommendation scenario • Document: point in the vector space • User profile: point in the vector space • e.g. built as the sum of the vector space representation of the documents liked in the past by the user • Goal: to find the documents that are the most relevant ones for that user profile • Assumption • the most similar documents in the vector space are the most relevant ones • Cosine Similarity to compute the similarity between query and documents C.Musto: Random Indexing and Quantum Negation for TV shows Retrieval and Classification - Philips Research , Eindhoven (The Netherlands) - 14.07.11
  • 23. vsm analysis (2) • Weak Points • Not incremental • The whole Vector Space has to be generated from scratch whenever a new item is added to the repository • High Dimensionality • NLP operations (stopwords elimination, stemming and so on) • Does not manage negative evidence • The vector space representation only depends on the features that occur in the document, there are no assumption about the features that don’t occur • Does not manage the latent semantic of documents • Any permutation of the terms in a document has the same VSM representation! C.Musto: Random Indexing and Quantum Negation for TV shows Retrieval and Classification - Philips Research , Eindhoven (The Netherlands) - 14.07.11
  • 24. idea • To introduce tools and techniques able to overcome these drawbacks • Random Indexing • Dimensionality reduction technique Sahlgren, 2005 • Quantum Negation • Based on Quantum Logic Widdows, 2007 C.Musto: Random Indexing and Quantum Negation for TV shows Retrieval and Classification - Philips Research , Eindhoven (The Netherlands) - 14.07.11
  • 25. random indexing • Random Indexing (RI) is an incremental and effective technique for dimensionality reduction • Distributional Models • Assumption: we can infer information about terms by analyzing how are they used in large corpus of data • Based on the so-called “Distributional Hypothesis” • “Words that occur in the same context tend to have similar meanings” • “Meaning is its use” (Wittgenstein) C.Musto: Random Indexing and Quantum Negation for TV shows Retrieval and Classification - Philips Research , Eindhoven (The Netherlands) - 14.07.11
  • 26. how it works? Random Indexing reduces the original dimensional term/doc matrix to a new lower dimensional matrix C.Musto: Random Indexing and Quantum Negation for TV shows Retrieval and Classification - Philips Research , Eindhoven (The Netherlands) - 14.07.11
  • 27. how it works? • How? • By multiplying the original matrix with a random one, built in an incremental way • formally: An,m * Rm,k = Bn,k • k << m • After projection, the distance between points in the vector space is preserved • Johnson-Lindenstrauss Lemma C.Musto: Random Indexing and Quantum Negation for TV shows Retrieval and Classification - Philips Research , Eindhoven (The Netherlands) - 14.07.11
  • 28. random matrix • How is the random matrix build? • The whole process is based on the concept of “context” • Given a term, its “context” could be the whole document, a paragraph, a sentence, a sliding window of words and so on. • The definition of the context influences the structure of the matrix • The matrix is built in an iterative and incremental way • The vector representing each document depends on the terms that occur in it • The vector representing each term depends on its context C.Musto: Random Indexing and Quantum Negation for TV shows Retrieval and Classification - Philips Research , Eindhoven (The Netherlands) - 14.07.11
  • 29. item representation • A context vector is assigned for each context (for simplicity, we assume as context the whole document) • This vector has a fixed dimension (k) and it can contain only values in -1, 0,1. Values are distributed in a random way but the number of non- zero elements is much smaller. • The Vector Space representation of a term is obtained by summing all its context (the documents it occurs in). • The Vector Space representation of a document (item) is obtained by summing the context vectors of the terms that occur in it • Output: lower-dimensional vector space representation based on random context vectors C.Musto: Random Indexing and Quantum Negation for TV shows Retrieval and Classification - Philips Research , Eindhoven (The Netherlands) - 14.07.11
  • 30. quantum negation • Random Indexing is still not capable of managing negative evidence • RI can be coupled with Quantum Negation (QN) operator • Definition inherited by Quantum logic • Negation as a form of orthogonality between vectors • Given two vectors A e B , we can define the vector A not B • It represents the projection of the vector A on the subspace orthogonal to those generated by vector B • In a recommendation scenario, this operator could be used to model two vectors, the first one representing positive evidence and the second one for modeling negative ones C.Musto: Random Indexing and Quantum Negation for TV shows Retrieval and Classification - Philips Research , Eindhoven (The Netherlands) - 14.07.11
  • 31. ...summing up • VSM is an effective model for document retrieval • It can be exploited in recommendation scenarios • It suffers from some well-known drawbacks • Solutions • Random Indexing is an incremental and effective approach that can catch the high-dimensionality problem • Quantum Negation can effectively model negative evidence • The combined use of RI and QN is a good alternative to VSM, especially for real-life scenarios C.Musto: Random Indexing and Quantum Negation for TV shows Retrieval and Classification - Philips Research , Eindhoven (The Netherlands) - 14.07.11
  • 32. part 3: scenario tv-shows recommendation C.Musto: Random Indexing and Quantum Negation for TV shows Retrieval and Classification - Philips Research , Eindhoven (The Netherlands) - 14.07.11
  • 33. Scenario: EPG (Electronic Program Guides) personalization C.Musto: Random Indexing and Quantum Negation for TV shows Retrieval and Classification - Philips Research , Eindhoven (The Netherlands) - 14.07.11
  • 34. scenario • Given a set of TV-Shows we want to provide user a set of suggestions about the shows that she should watch, according on her preferences C.Musto: Random Indexing and Quantum Negation for TV shows Retrieval and Classification - Philips Research , Eindhoven (The Netherlands) - 14.07.11
  • 35. approach Currently the recommendation model is implemented through the Vector Space Model (VSM) C.Musto: Random Indexing and Quantum Negation for TV shows Retrieval and Classification - Philips Research , Eindhoven (The Netherlands) - 14.07.11
  • 36. data • TV shows gathered from a set of 47 German-language broadcast channel • Each TV show is described through a set of textual features (title, synopsis, description, etc.) gathered from an XML feed • Each TV-Show is mapped to a fixed program type (Movie, Sport, Documentary, Magazine, etc.) C.Musto: Random Indexing and Quantum Negation for TV shows Retrieval and Classification - Philips Research , Eindhoven (The Netherlands) - 14.07.11
  • 37. problems • How to represent the data? • We compared two approaches • Bag of Words (BOW) • Tag.me • Which ones are the typical use cases? • We identified two tasks • Classification Task • Retrieval Task C.Musto: Random Indexing and Quantum Negation for TV shows Retrieval and Classification - Philips Research , Eindhoven (The Netherlands) - 14.07.11
  • 38. data representation • Bag of Words • Each item i is described through the words that appear in the text • Weighting of the words • Counting of the occurrences, normalization, TF-IDF weighting, etc. C.Musto: Random Indexing and Quantum Negation for TV shows Retrieval and Classification - Philips Research , Eindhoven (The Netherlands) - 14.07.11
  • 39. BOW representation • To improve BOW representation • Usually textual description are very noisy • Full of uninformative words • Further processing can improve the classical BOW representation • Stopword removal: filtering of all the uninformative words (articles, adverbs, adjectives and so on) C.Musto: Random Indexing and Quantum Negation for TV shows Retrieval and Classification - Philips Research , Eindhoven (The Netherlands) - 14.07.11
  • 40. data representation • Tag.me • Online tool developed by the University of Pisa (Italy) • Goal: to identify Wikipedia concepts that occur in the text • Idea: to process original text through Tag.me in order to avoid noise and provide a novel representation based on high-level Wikipedia concepts C.Musto: Random Indexing and Quantum Negation for TV shows Retrieval and Classification - Philips Research , Eindhoven (The Netherlands) - 14.07.11
  • 41. tag.me web interface C.Musto: Random Indexing and Quantum Negation for TV shows Retrieval and Classification - Philips Research , Eindhoven (The Netherlands) - 14.07.11
  • 42. final output Bow Tag.me C.Musto: Random Indexing and Quantum Negation for TV shows Retrieval and Classification - Philips Research , Eindhoven (The Netherlands) - 14.07.11
  • 43. description of the tasks • task 1: classification • Given a flow of TV shows, we would classify them against a the set of program types • task 2: retrieval • Given a set of program type and a repository of TV shows, we would retrieve the shows that belong to a specific program type C.Musto: Random Indexing and Quantum Negation for TV shows Retrieval and Classification - Philips Research , Eindhoven (The Netherlands) - 14.07.11
  • 44. VSM for TV shows classification • Steps • 1) Build a vector space for the tv shows • 2) Build a vector for each program type • 3) Use cosine similarity to compare tv shows and program types • 4) Assign the TV show to the program type that got the highest cosine similarity C.Musto: Random Indexing and Quantum Negation for TV shows Retrieval and Classification - Philips Research , Eindhoven (The Netherlands) - 14.07.11
  • 45. VSM for TV shows classification • Step 1: build a vector space representation of the TV-shows • For each TV show we collected a set of words by using the synopsis and the title of the show • We filtered out the set of the words through a fixed set of 996 stopwords for German language • We calculated the TF-IDF score for each document C.Musto: Random Indexing and Quantum Negation for TV shows Retrieval and Classification - Philips Research , Eindhoven (The Netherlands) - 14.07.11
  • 46. VSM for TV shows classification • Step 2: build a vector for each program type • Given the vector space representation of each document • The vector space representation of each program type is the sum of the vector space representations of each tv- show that belongs to that program type C.Musto: Random Indexing and Quantum Negation for TV shows Retrieval and Classification - Philips Research , Eindhoven (The Netherlands) - 14.07.11
  • 47. VSM for TV shows classification • Given a set of TV-shows • T=(s1...sn) • Given a set of program types • P=(t1...tm) • We define a function pt: P T • It returns the program type of a tv show • We can build the set S(t_i) as the set of the tv-shows that belong to t_i • It returns the program type of a tv show • C.Musto: Random Indexing and Quantum Negation for TV shows Retrieval and Classification - Philips Research , Eindhoven (The Netherlands) - 14.07.11
  • 48. VSM for TV shows classification • Given the set S(t_i) with a cardinality of k, the vector space representation of the program type is simply given by C.Musto: Random Indexing and Quantum Negation for TV shows Retrieval and Classification - Philips Research , Eindhoven (The Netherlands) - 14.07.11
  • 49. VSM for TV shows classification • Step 3 and Step 4 • Given the vector space representation of both program types and tv shows • Use of cosine similarity to compare each TV shows against the set of the program types • We assigned the TV show to the program type that got the highest cosine similarity C.Musto: Random Indexing and Quantum Negation for TV shows Retrieval and Classification - Philips Research , Eindhoven (The Netherlands) - 14.07.11
  • 50. RI for TV shows classification • Steps • 1) Build a vector space for the tv shows • 2) Reduce the vector space through the Random Indexing algorithm • 3) Build a vector for each program type on the (reduced) vector space • 4) Use cosine similarity to compare tv shows and program types • 5) Assign the TV show to the program type that got the highest cosine similarity C.Musto: Random Indexing and Quantum Negation for TV shows Retrieval and Classification - Philips Research , Eindhoven (The Netherlands) - 14.07.11
  • 51. RI for TV shows retrieval • Steps • 1) Build a vector space for the tv shows • 2) Reduce the vector space through the Random Indexing algorithm • 3) Build a positive vector for each program type on the (reduced) vector space • 4) Use cosine similarity to compare tv shows and program types • 5) Rank the tv shows and assign the first N to the program type C.Musto: Random Indexing and Quantum Negation for TV shows Retrieval and Classification - Philips Research , Eindhoven (The Netherlands) - 14.07.11
  • 52. RI+QN for TV shows retrieval • Steps • 1) Build a vector space for the tv shows • 2) Reduce the vector space through the Random Indexing algorithm • 3) Build a positive vector for each program type on the (reduced) vector space • 4) Build a negative vector for each program type on the (reduced) vector space • 5) Use cosine similarity to compare tv shows with both positive and negative program types vectors • 6) Rank the tv shows and assign the first N to the program type C.Musto: Random Indexing and Quantum Negation for TV shows Retrieval and Classification - Philips Research , Eindhoven (The Netherlands) - 14.07.11
  • 53. RI+QN for TV shows retrieval • Given a set of TV-shows • T=(s1...sn) • Given a set of program types • P=(t1...tm) • We define a function pt: P T • It returns the program type of a tv show • We can build the set S(t_i) as the set of the tv-shows that belong to t_i • It returns the program type of a tv show • C.Musto: Random Indexing and Quantum Negation for TV shows Retrieval and Classification - Philips Research , Eindhoven (The Netherlands) - 14.07.11
  • 54. RI+QN for TV shows retrieval • Given the sets S(t_i) and its complement with a cardinality of k and z the vector space representation of the program type is simply given by • The positive and negative vector will be combined in order to emphasize the features that occur in the positive vector and avoid the ones that occur in the negative one C.Musto: Random Indexing and Quantum Negation for TV shows Retrieval and Classification - Philips Research , Eindhoven (The Netherlands) - 14.07.11
  • 55. ...summing up • Classification task • Comparison of VSM and RI • We build a vector space • Applied RI to reduce the vector space • We tried to classify TV shows in the complete vector space and in the reduced one, comparing the accuracy • Retrieval task • Comparison of RI and RI+QN • We build a vector space • Applied RI to reduce the vector space • Build both positive and negative program types vectors and applied QN • We tried to retrieve TV shows and we compared the the RI without negation and the RI with negation C.Musto: Random Indexing and Quantum Negation for TV shows Retrieval and Classification - Philips Research , Eindhoven (The Netherlands) - 14.07.11
  • 56. part 4: experimental evaluation results, discussion, future work C.Musto: Random Indexing and Quantum Negation for TV shows Retrieval and Classification - Philips Research , Eindhoven (The Netherlands) - 14.07.11
  • 57. dataset program tv shows 133.579 17 types features features 306,006 74,599 (BOW) (Tag.me) avg avg features 42.11 features 9.21 (BOW) (Tag.me) C.Musto: Random Indexing and Quantum Negation for TV shows Retrieval and Classification - Philips Research , Eindhoven (The Netherlands) - 14.07.11
  • 58. experimental design • 10-fold cross validation • Dataset splitted in 10 partitions • 9 partitions for training the models, the last one for testing • Results averaged over all the partitions C.Musto: Random Indexing and Quantum Negation for TV shows Retrieval and Classification - Philips Research , Eindhoven (The Netherlands) - 14.07.11
  • 59. metrics • classification task • precision = • retrieval task • precision @n = • precision @k% = C.Musto: Random Indexing and Quantum Negation for TV shows Retrieval and Classification - Philips Research , Eindhoven (The Netherlands) - 14.07.11
  • 60. tuning of parameters • Random Indexing algorithm • Dimension of the vectors • Classification task: 500, 700 • Retrieval task: 500, 1000, 1500, 2000 • Minimum number of occurrences • Classification task: 2 • Retrieval task: 1, 3 • Training Cycles • Classification task: 1, 2 • Retrieval task: 1 C.Musto: Random Indexing and Quantum Negation for TV shows Retrieval and Classification - Philips Research , Eindhoven (The Netherlands) - 14.07.11
  • 61. classification task - results size occur. cycles tag.me bow 500 2 1 37.38 42.91 700 2 1 40.28 47.76 500 2 1 44.61 54.32 700 2 1 45.33 54.33 C.Musto: Random Indexing and Quantum Negation for TV shows Retrieval and Classification - Philips Research , Eindhoven (The Netherlands) - 14.07.11
  • 62. classification task: comparison 68.7 54.3 54.3 47.7 42.9 C.Musto: Random Indexing and Quantum Negation for TV shows Retrieval and Classification - Philips Research , Eindhoven (The Netherlands) - 14.07.11
  • 63. classification - results per program type C.Musto: Random Indexing and Quantum Negation for TV shows Retrieval and Classification - Philips Research , Eindhoven (The Netherlands) - 14.07.11
  • 64. classification task - outcomes • BOW better than Tag.me • Representation too poor • Difficult to learn a solid and effective model for text classification • Dimension of the vector space and the second training cycles affect the predictive accuracy • RI does not overcome the baseline • Vector space reduced over 99% (from 133579 to 500 or 700) • Too much loss of information • but • Splitting the results for single program types the Random Indexing got better results in 10 out of 17 program types • Need to investigate the reasons of that C.Musto: Random Indexing and Quantum Negation for TV shows Retrieval and Classification - Philips Research , Eindhoven (The Netherlands) - 14.07.11
  • 65. retrieval task - bow - p@n 82.6% 66.3% C.Musto: Random Indexing and Quantum Negation for TV shows Retrieval and Classification - Philips Research , Eindhoven (The Netherlands) - 14.07.11
  • 66. retrieval task - bow - p@n 65.9% 45.2% C.Musto: Random Indexing and Quantum Negation for TV shows Retrieval and Classification - Philips Research , Eindhoven (The Netherlands) - 14.07.11
  • 67. retrieval task - bow - p@n 58.1% 36.5% C.Musto: Random Indexing and Quantum Negation for TV shows Retrieval and Classification - Philips Research , Eindhoven (The Netherlands) - 14.07.11
  • 68. retrieval task - bow - p@k% 86.0% 58.1% C.Musto: Random Indexing and Quantum Negation for TV shows Retrieval and Classification - Philips Research , Eindhoven (The Netherlands) - 14.07.11
  • 69. retrieval task - bow - p@k% 55.4% 35.4% C.Musto: Random Indexing and Quantum Negation for TV shows Retrieval and Classification - Philips Research , Eindhoven (The Netherlands) - 14.07.11
  • 70. retrieval task - tagme - p@n 61.9% 47.9% C.Musto: Random Indexing and Quantum Negation for TV shows Retrieval and Classification - Philips Research , Eindhoven (The Netherlands) - 14.07.11
  • 71. retrieval task - tagme - p@n 53.7% 40.9% C.Musto: Random Indexing and Quantum Negation for TV shows Retrieval and Classification - Philips Research , Eindhoven (The Netherlands) - 14.07.11
  • 72. retrieval task - tagme - p@n 51.6% 39.0% C.Musto: Random Indexing and Quantum Negation for TV shows Retrieval and Classification - Philips Research , Eindhoven (The Netherlands) - 14.07.11
  • 73. retrieval task - tagme - p@k% 76.6% 57.9% C.Musto: Random Indexing and Quantum Negation for TV shows Retrieval and Classification - Philips Research , Eindhoven (The Netherlands) - 14.07.11
  • 74. retrieval task - tagme - p@k% 49.6% 35.4% C.Musto: Random Indexing and Quantum Negation for TV shows Retrieval and Classification - Philips Research , Eindhoven (The Netherlands) - 14.07.11
  • 75. retrieval task - overview 82.6% 61.9% C.Musto: Random Indexing and Quantum Negation for TV shows Retrieval and Classification - Philips Research , Eindhoven (The Netherlands) - 14.07.11
  • 76. retrieval task - overview 65.0% 53.0% C.Musto: Random Indexing and Quantum Negation for TV shows Retrieval and Classification - Philips Research , Eindhoven (The Netherlands) - 14.07.11
  • 77. retrieval task - overview 58.3% 53.2% C.Musto: Random Indexing and Quantum Negation for TV shows Retrieval and Classification - Philips Research , Eindhoven (The Netherlands) - 14.07.11
  • 78. retrieval task - outcomes • BOW always better than Tag.me • Between 5 and 20% difference • Parameters do not affect the accuracy • QN operator improves the retrieval accuracy by almost 20% C.Musto: Random Indexing and Quantum Negation for TV shows Retrieval and Classification - Philips Research , Eindhoven (The Netherlands) - 14.07.11
  • 79. conclusions & future work • In scenarios where the recommender system has to deal with a continous flow of information the VSM is not suitable • RI is able to effectively catch typical VSM drawbacks • Classification task • Even if its accuracy is lower, these preliminar results need to be further investigated, for example testing the algorithm with different values of the parameters • Is a worsening in precision suitable for an algorithm that provides a big improvement in scalability and efficiency? • Retrieval Task • QN improves the predictive accuracy of the model in the retrieval tasks • Novel operator, this is important outcome with a good scientific impact C.Musto: Random Indexing and Quantum Negation for TV shows Retrieval and Classification - Philips Research , Eindhoven (The Netherlands) - 14.07.11
  • 80. Thanks for you attention. Cataldo Musto, Ph.D. Student cataldomusto@di.uniba.it - cataldo.musto@philips.com University of Bari “Aldo Moro” (Italy), SWAP Research Group Philips Research Center - Eindhoven (Netherlands) - HI&E Group 14.07.11 C.Musto: Random Indexing and Quantum Negation for TV shows Retrieval and Classification - Philips Research , Eindhoven (The Netherlands) - 14.07.11

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