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Ph.D. Defense - Enhanced Vector Space Models for Content-based Recommender Systems

Cataldo Musto
Cataldo Musto
Cataldo MustoAssistant Professor (Ricercatore a Tempo Determinato) presso Università degli Studi di Bari

08.06.12 PhD defense: Enhanced Vector Space Models for Content-based Recommender Systems.

Ph.D. Defense - Enhanced Vector Space Models for Content-based Recommender Systems

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Università degli Studi di Bari ‘Aldo Moro’
           Dottorato di Ricerca in Informatica - Ciclo XXIV




   Enhanced Vector Space
  Models for Content-based
   Recommender Systems
                            Cataldo Musto, Ph.D. Candidate
                                    Supervisor: prof. Giovanni Semeraro
08.06.12
what will we talk about
                                             in the next 40 minutes?




Cataldo Musto - Enhanced Vector Space Models for Content-based Recommender Systems - Ph.D. defense - University of Bari Aldo Moro, Italy - 08.06.12
life is all
                                                                                                       a matter of
                                                                                decisions




Cataldo Musto - Enhanced Vector Space Models for Content-based Recommender Systems - Ph.D. defense - University of Bari Aldo Moro, Italy - 08.06.12
life is all
                                                                                                  a matter of
                                                                           decisions




Cataldo Musto - Enhanced Vector Space Models for Content-based Recommender Systems - Ph.D. defense - University of Bari Aldo Moro, Italy - 08.06.12
decision-making
                                                  is actually challenging
Cataldo Musto - Enhanced Vector Space Models for Content-based Recommender Systems - Ph.D. defense - University of Bari Aldo Moro, Italy - 08.06.12
decision-making
                                                  is actually challenging
Cataldo Musto - Enhanced Vector Space Models for Content-based Recommender Systems - Ph.D. defense - University of Bari Aldo Moro, Italy - 08.06.12

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Ph.D. Defense - Enhanced Vector Space Models for Content-based Recommender Systems

  • 1. Università degli Studi di Bari ‘Aldo Moro’ Dottorato di Ricerca in Informatica - Ciclo XXIV Enhanced Vector Space Models for Content-based Recommender Systems Cataldo Musto, Ph.D. Candidate Supervisor: prof. Giovanni Semeraro 08.06.12
  • 2. what will we talk about in the next 40 minutes? Cataldo Musto - Enhanced Vector Space Models for Content-based Recommender Systems - Ph.D. defense - University of Bari Aldo Moro, Italy - 08.06.12
  • 3. life is all a matter of decisions Cataldo Musto - Enhanced Vector Space Models for Content-based Recommender Systems - Ph.D. defense - University of Bari Aldo Moro, Italy - 08.06.12
  • 4. life is all a matter of decisions Cataldo Musto - Enhanced Vector Space Models for Content-based Recommender Systems - Ph.D. defense - University of Bari Aldo Moro, Italy - 08.06.12
  • 5. decision-making is actually challenging Cataldo Musto - Enhanced Vector Space Models for Content-based Recommender Systems - Ph.D. defense - University of Bari Aldo Moro, Italy - 08.06.12
  • 6. decision-making is actually challenging Cataldo Musto - Enhanced Vector Space Models for Content-based Recommender Systems - Ph.D. defense - University of Bari Aldo Moro, Italy - 08.06.12
  • 7. decision-making is actually challenging Cataldo Musto - Enhanced Vector Space Models for Content-based Recommender Systems - Ph.D. defense - University of Bari Aldo Moro, Italy - 08.06.12
  • 8. as much we need to hold knowledge as possible Cataldo Musto - Enhanced Vector Space Models for Content-based Recommender Systems - Ph.D. defense - University of Bari Aldo Moro, Italy - 08.06.12
  • 9. Leibniz “In things which are absolutely indifferent there can be no choice and consequently no option or will. ” Cataldo Musto - Enhanced Vector Space Models for Content-based Recommender Systems - Ph.D. defense - University of Bari Aldo Moro, Italy - 08.06.12
  • 10. information age knowledge is spread through the Web Cataldo Musto - Enhanced Vector Space Models for Content-based Recommender Systems - Ph.D. defense - University of Bari Aldo Moro, Italy - 08.06.12
  • 11. social media changed the rules for information management and knowledge acquisition Cataldo Musto - Enhanced Vector Space Models for Content-based Recommender Systems - Ph.D. defense - University of Bari Aldo Moro, Italy - 08.06.12
  • 12. exponential growth of the available information Cataldo Musto - Enhanced Vector Space Models for Content-based Recommender Systems - Ph.D. defense - University of Bari Aldo Moro, Italy - 08.06.12
  • 13. Cataldo Musto - Enhanced Vector Space Models for Content-based Recommender Systems - Ph.D. defense - University of Bari Aldo Moro, Italy - 08.06.12
  • 14. it is physiologically impossible to follow the information flow in real time Cataldo Musto - Enhanced Vector Space Models for Content-based Recommender Systems - Ph.D. defense - University of Bari Aldo Moro, Italy - 08.06.12
  • 15. how much information? Cataldo Musto - Enhanced Vector Space Models for Content-based Recommender Systems - Ph.D. defense - University of Bari Aldo Moro, Italy - 08.06.12
  • 16. we daily interact with 393 bits of information per second Cataldo Musto - Enhanced Vector Space Models for Content-based Recommender Systems - Ph.D. defense - University of Bari Aldo Moro, Italy - 08.06.12
  • 17. human brain can absorb 126 bits of information per second Cataldo Musto - Enhanced Vector Space Models for Content-based Recommender Systems - Ph.D. defense - University of Bari Aldo Moro, Italy - 08.06.12
  • 18. we can handle 126 bits of information we deal with 393 bits of information ratio: more than (Source: Adrian C.Ott, The 24-hour customer) 3x consequence: Information Overload Cataldo Musto - Enhanced Vector Space Models for Content-based Recommender Systems - Ph.D. defense - University of Bari Aldo Moro, Italy - 08.06.12
  • 19. Information Overload Cataldo Musto - Enhanced Vector Space Models for Content-based Recommender Systems - Ph.D. defense - University of Bari Aldo Moro, Italy - 08.06.12
  • 20. Information Overload Cataldo Musto - Enhanced Vector Space Models for Content-based Recommender Systems - Ph.D. defense - University of Bari Aldo Moro, Italy - 08.06.12
  • 21. Information Overload Cataldo Musto - Enhanced Vector Space Models for Content-based Recommender Systems - Ph.D. defense - University of Bari Aldo Moro, Italy - 08.06.12
  • 22. Information Overload Cataldo Musto - Enhanced Vector Space Models for Content-based Recommender Systems - Ph.D. defense - University of Bari Aldo Moro, Italy - 08.06.12
  • 23. Information Overload Cataldo Musto - Enhanced Vector Space Models for Content-based Recommender Systems - Ph.D. defense - University of Bari Aldo Moro, Italy - 08.06.12
  • 24. paradox of choice (Barry Schwartz, TED talk “Why more is less”) Cataldo Musto - Enhanced Vector Space Models for Content-based Recommender Systems - Ph.D. defense - University of Bari Aldo Moro, Italy - 08.06.12
  • 25. Buridan’s ass paradox Two alternatives. The ass cannot decide. It starves. Cataldo Musto - Enhanced Vector Space Models for Content-based Recommender Systems - Ph.D. defense - University of Bari Aldo Moro, Italy - 08.06.12
  • 26. Is the information overload actually unbearable? Cataldo Musto - Enhanced Vector Space Models for Content-based Recommender Systems - Ph.D. defense - University of Bari Aldo Moro, Italy - 08.06.12
  • 27. “It is not information overload. It is filter failure” Clay Shirky talk @Web2.0 Expo Cataldo Musto - Enhanced Vector Space Models for Content-based Recommender Systems - Ph.D. defense - University of Bari Aldo Moro, Italy - 08.06.12
  • 28. Solution we need to the improve techniques for filtering the information Cataldo Musto - Enhanced Vector Space Models for Content-based Recommender Systems - Ph.D. defense - University of Bari Aldo Moro, Italy - 08.06.12
  • 29. Information Filtering (IF) “To expose users only with the information that are relevant for them, thus avoiding information overload.” to filter. as kids do when they play with sand. Cataldo Musto - Enhanced Vector Space Models for Content-based Recommender Systems - Ph.D. defense - University of Bari Aldo Moro, Italy - 08.06.12
  • 30. IF applications Example: Recommender System Relevant items (movies, news, books, etc.) are pushed to the user according to her needs. Cataldo Musto - Enhanced Vector Space Models for Content-based Recommender Systems - Ph.D. defense - University of Bari Aldo Moro, Italy - 08.06.12
  • 31. Recommender Systems are an effective way to face the Information Overload problem Cataldo Musto - Enhanced Vector Space Models for Content-based Recommender Systems - Ph.D. defense - University of Bari Aldo Moro, Italy - 08.06.12
  • 32. example Amazon.com Recommendations Cataldo Musto - Enhanced Vector Space Models for Content-based Recommender Systems - Ph.D. defense - University of Bari Aldo Moro, Italy - 08.06.12
  • 33. Information Retrieval (IR) “Findings of relevant pieces of information from a collection of (usually unstructured) data” Cataldo Musto - Enhanced Vector Space Models for Content-based Recommender Systems - Ph.D. defense - University of Bari Aldo Moro, Italy - 08.06.12
  • 34. IR applications Example: Search Engines Relevant document are returned to the user, according to her query. Cataldo Musto - Enhanced Vector Space Models for Content-based Recommender Systems - Ph.D. defense - University of Bari Aldo Moro, Italy - 08.06.12
  • 35. IR vs. IF • IR and IF represent two strictly related research areas • Same goal: to optimize and make easier the access to (unstructured) data sources • “Two sides of the same coin” (*) (*) N.Belkin, W. Croft: Information Filtering and Information Retrieval: Two sides of the same coin”, Communications of ACM, Volume 35, Issue 12, pp. 29-38, 1992 Cataldo Musto - Enhanced Vector Space Models for Content-based Recommender Systems - Ph.D. defense - University of Bari Aldo Moro, Italy - 08.06.12
  • 36. IR vs IF: differences • Little differences • Representation of user needs • Query in IR, user profile in IF • Convergence between IR and IF • Personalized Search ! Cataldo Musto - Enhanced Vector Space Models for Content-based Recommender Systems - Ph.D. defense - University of Bari Aldo Moro, Italy - 08.06.12
  • 37. Cataldo Musto - Enhanced Vector Space Models for Content-based Recommender Systems - Ph.D. defense - University of Bari Aldo Moro, Italy - 08.06.12
  • 38. Ph.D. dissertation Research Question Is it possible to exploit the convergence between IR and IF to introduce a recommendation framework based on IR techniques? Cataldo Musto - Enhanced Vector Space Models for Content-based Recommender Systems - Ph.D. defense - University of Bari Aldo Moro, Italy - 08.06.12
  • 39. outline. Cataldo Musto - Enhanced Vector Space Models for Content-based Recommender Systems - Ph.D. defense - University of Bari Aldo Moro, Italy - 08.06.12
  • 40. outline (1/2) • recommender systems • content-based recommender systems (CBRS) • vector space models • VSM for CBRS • strengths and weaknesses Cataldo Musto - Enhanced Vector Space Models for Content-based Recommender Systems - Ph.D. defense - University of Bari Aldo Moro, Italy - 08.06.12
  • 41. outline (2/2) • eVSM: enhanced vector space models • semantics in VSMs • dimensionality reduction in VSMs • modeling negation in VSMs • applications and experimental evaluation • movie recommendation • Philips TV-guides personalization Cataldo Musto - Enhanced Vector Space Models for Content-based Recommender Systems - Ph.D. defense - University of Bari Aldo Moro, Italy - 08.06.12
  • 42. recommender systems. Cataldo Musto - Enhanced Vector Space Models for Content-based Recommender Systems - Ph.D. defense - University of Bari Aldo Moro, Italy - 08.06.12
  • 43. definition guiding the Recommender Systems have the goal of users in a personalized way to interesting or useful objects in a large space of possible options. Burke, 2002 (*) (*) Robin D. Burke: Hybrid Recommender Systems: Survey and Experiments. UMUAI, volume 12, issue 4, 331-370 (2002) Cataldo Musto - Enhanced Vector Space Models for Content-based Recommender Systems - Ph.D. defense - University of Bari Aldo Moro, Italy - 08.06.12
  • 44. suggestions • Examples • books or news to read • music to be listened to • movies worth to be watched • restaurants, etc. Cataldo Musto - Enhanced Vector Space Models for Content-based Recommender Systems - Ph.D. defense - University of Bari Aldo Moro, Italy - 08.06.12
  • 45. Some maths (1/2) • Let • U set of users • I set of items • Given • user u ∈ U • item i ∈ I Cataldo Musto - Enhanced Vector Space Models for Content-based Recommender Systems - Ph.D. defense - University of Bari Aldo Moro, Italy - 08.06.12
  • 46. Some maths (2/2) • A recommender system should predict how relevant item i is for user u by defining a scoring function • f: U×I→[0,1] = scoring function • The items with the highest value of f are labeled as relevant and returned to the user Cataldo Musto - Enhanced Vector Space Models for Content-based Recommender Systems - Ph.D. defense - University of Bari Aldo Moro, Italy - 08.06.12
  • 47. classes of RSs • In literature many approaches for building RSs have been introduced. • Collaborative Recommender Systems • Content-based Recommender Systems • Knowledge-based Recommender Systems • Demographic-based Recommender Systems • Social Recommender Systems • Hybrid Recommender Systems Cataldo Musto - Enhanced Vector Space Models for Content-based Recommender Systems - Ph.D. defense - University of Bari Aldo Moro, Italy - 08.06.12
  • 48. classes of RSs • In literature many approaches for building RSs have been introduced. • Collaborative Recommender Systems FOCUS • Content-based Recommender Systems • Knowledge-based Recommender Systems • Demographic-based Recommender Systems • Social Recommender Systems • Hybrid Recommender Systems Cataldo Musto - Enhanced Vector Space Models for Content-based Recommender Systems - Ph.D. defense - University of Bari Aldo Moro, Italy - 08.06.12
  • 49. content-based recommenders Suggest items similar to those liked in the past by the user Cataldo Musto - Enhanced Vector Space Models for Content-based Recommender Systems - Ph.D. defense - University of Bari Aldo Moro, Italy - 08.06.12
  • 50. content-based recommenders key concepts • Each item has to be described through a set of textual features • Movie plots, content of news, book summaries, Cataldo Musto - Enhanced Vector Space Models for Content-based Recommender Systems - Ph.D. defense - University of Bari Aldo Moro, Italy - 08.06.12
  • 51. content-based recommenders key concepts • User profile contains the features that often occur in the items the user liked • A profile of a user interested in basketball will contain keywords related to it (example: basketball teams, players or competitions) Cataldo Musto - Enhanced Vector Space Models for Content-based Recommender Systems - Ph.D. defense - University of Bari Aldo Moro, Italy - 08.06.12
  • 52. content-based recommenders key concepts • Recommendations are provided by calculating the overlap between the features stored in the user profile and those that occur in the item. • The bigger the overlap, the higher the relevance Cataldo Musto - Enhanced Vector Space Models for Content-based Recommender Systems - Ph.D. defense - University of Bari Aldo Moro, Italy - 08.06.12
  • 53. content-based recommenders example: news recommendations Items User Profile User is interested in ♥ news articles about sports, football, ♥ cycling, etc. Cataldo Musto - Enhanced Vector Space Models for Content-based Recommender Systems - Ph.D. defense - University of Bari Aldo Moro, Italy - 08.06.12
  • 54. content-based recommenders example: news recommendations Items Recommendations ♥ ♥ Cataldo Musto - Enhanced Vector Space Models for Content-based Recommender Systems - Ph.D. defense - University of Bari Aldo Moro, Italy - 08.06.12
  • 55. content-based recommenders example: news recommendations Items Recommendations ♥ X ♥ Cataldo Musto - Enhanced Vector Space Models for Content-based Recommender Systems - Ph.D. defense - University of Bari Aldo Moro, Italy - 08.06.12
  • 56. content-based recommenders example: news recommendations Items Recommendations ♥ X ♥ Cataldo Musto - Enhanced Vector Space Models for Content-based Recommender Systems - Ph.D. defense - University of Bari Aldo Moro, Italy - 08.06.12
  • 57. main building block vector space model the most adopted IR model (*) (*) Gerard Salton: A Vector Space Model for Automatic Indexing, Communications of the ACM, vol. 18, nr. 11, pages 613–620 Cataldo Musto - Enhanced Vector Space Models for Content-based Recommender Systems - Ph.D. defense - University of Bari Aldo Moro, Italy - 08.06.12
  • 58. vector space model (VSM) Testo • Given a set of n features (vocabulary) Testo • f={ f1, f2 ... fn } • Given a set of M items • Each document (item) is represented as a point a an n-dimensional vector space • I = (wi in the itemw is the weight of i feature .....w ) -f1 fn fi Cataldo Musto - Enhanced Vector Space Models for Content-based Recommender Systems - Ph.D. defense - University of Bari Aldo Moro, Italy - 08.06.12
  • 59. VSM representation football news sports news politics news politics news Cataldo Musto - Enhanced Vector Space Models for Content-based Recommender Systems - Ph.D. defense - University of Bari Aldo Moro, Italy - 08.06.12
  • 60. research question Is it possible to exploit VSM for a recommendation scenario? Cataldo Musto - Enhanced Vector Space Models for Content-based Recommender Systems - Ph.D. defense - University of Bari Aldo Moro, Italy - 08.06.12
  • 61. VSM for CBRS how to adapt it? • In VSM each item is represented as a vector • User profile vector space representation as well needs a • How? • For example, by combining vectors of the items (documents) the user liked in the past Cataldo Musto - Enhanced Vector Space Models for Content-based Recommender Systems - Ph.D. defense - University of Bari Aldo Moro, Italy - 08.06.12
  • 62. VSM representation user profile football news sports news politics news politics news Cataldo Musto - Enhanced Vector Space Models for Content-based Recommender Systems - Ph.D. defense - University of Bari Aldo Moro, Italy - 08.06.12
  • 63. VSM representation Recommendation task seen as user profile similarity calculation football news between vectors sports news politics news politics news Cataldo Musto - Enhanced Vector Space Models for Content-based Recommender Systems - Ph.D. defense - University of Bari Aldo Moro, Italy - 08.06.12
  • 64. VSM representation recommender systmem suggests user profile football and football news sports news sports news politics news politics news Cataldo Musto - Enhanced Vector Space Models for Content-based Recommender Systems - Ph.D. defense - University of Bari Aldo Moro, Italy - 08.06.12
  • 65. Can this model be improved? Yes. Cataldo Musto - Enhanced Vector Space Models for Content-based Recommender Systems - Ph.D. defense - University of Bari Aldo Moro, Italy - 08.06.12
  • 66. VSM weaknesses • Modeling Negation • VSM does not model negative evidences • 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 • What a specific user dislikes is not considered Cataldo Musto - Enhanced Vector Space Models for Content-based Recommender Systems - Ph.D. defense - University of Bari Aldo Moro, Italy - 08.06.12
  • 67. VSM weaknesses • High Dimensionality • As the number of documents grows, the number of features grows as well • Large vector spaces are difficult to manage Cataldo Musto - Enhanced Vector Space Models for Content-based Recommender Systems - Ph.D. defense - University of Bari Aldo Moro, Italy - 08.06.12
  • 68. VSM weaknesses •Language issues • Does not manage the latent semantic of documents • String matching-based approach • A CBRS based on VSM cannot understand the information it manages apple ? Cataldo Musto - Enhanced Vector Space Models for Content-based Recommender Systems - Ph.D. defense - University of Bari Aldo Moro, Italy - 08.06.12
  • 69. VSM weaknesses •Language issues • Representation is language-dependant • User profile built in a language can not be exploited to provide recommendation of items described in another language • It would be good to receive (e.g.) recommendation about news written by english newspapers even if I expressed my interest only on italian news articles! Cataldo Musto - Enhanced Vector Space Models for Content-based Recommender Systems - Ph.D. defense - University of Bari Aldo Moro, Italy - 08.06.12
  • 70. How to catch these issues? Cataldo Musto - Enhanced Vector Space Models for Content-based Recommender Systems - Ph.D. defense - University of Bari Aldo Moro, Italy - 08.06.12
  • 71. a novel recommendation framework based on VSM eVSM enhanced Vector Space Model (*) (*) Cataldo Musto: Enhanced Vector Space Models for Content-based Recommender Systems, RECSYS 2010, pages 361-364 Cataldo Musto - Enhanced Vector Space Models for Content-based Recommender Systems - Ph.D. defense - University of Bari Aldo Moro, Italy - 08.06.12
  • 72. eVSM goals • To introduce a CBRS based on VSM • To catch representation issues of VSM •No Semantics •High Dimensionality •No modeling of Negative Information •Language-dependant recommendations Cataldo Musto - Enhanced Vector Space Models for Content-based Recommender Systems - Ph.D. defense - University of Bari Aldo Moro, Italy - 08.06.12
  • 73. a novel recommendation framework based on VSM eVSM step 1: modeling semantics step 2: dimensionality reduction step 3: modeling negation step 4: building user profiles step 5: providing suggestions Cataldo Musto - Enhanced Vector Space Models for Content-based Recommender Systems - Ph.D. defense - University of Bari Aldo Moro, Italy - 08.06.12
  • 74. how to improve the semantic modeling in VSMs? distributional models (Firth, 1957) Firth, J.R. A synopsis of linguistic theory 1930-1955. In Studies in Linguistic Analysis, pp. 1-32, 1957. Cataldo Musto - Enhanced Vector Space Models for Content-based Recommender Systems - Ph.D. defense - University of Bari Aldo Moro, Italy - 08.06.12
  • 75. distributional models “meaning is its use” L.Wittgenstein Cataldo Musto - Enhanced Vector Space Models for Content-based Recommender Systems - Ph.D. defense - University of Bari Aldo Moro, Italy - 08.06.12
  • 76. distributional models insight by analyzing large corpus of textual data it is possible to infer information about the usage (about the meaning) of the terms. Cataldo Musto - Enhanced Vector Space Models for Content-based Recommender Systems - Ph.D. defense - University of Bari Aldo Moro, Italy - 08.06.12
  • 77. distributional models insight by analyzing large corpus of textual data it is possible to infer information about the usage (about the meaning) of the terms. example Cataldo Musto - Enhanced Vector Space Models for Content-based Recommender Systems - Ph.D. defense - University of Bari Aldo Moro, Italy - 08.06.12
  • 78. Distributional Models term/context matrix c1 c2 c3 c4 c5 c6 c7 c8 c9 t1 ✔ ✔ ✔ ✔ t2 ✔ ✔ ✔ ✔ t3 ✔ ✔ ✔ t4 ✔ ✔ ✔ ✔ Cataldo Musto - Enhanced Vector Space Models for Content-based Recommender Systems - Ph.D. defense - University of Bari Aldo Moro, Italy - 08.06.12
  • 79. distributional models • Key: definition of what is the ‘context’ • Different granularities are possible • Document • Paragraph • Sentence • Sliding window of words Cataldo Musto - Enhanced Vector Space Models for Content-based Recommender Systems - Ph.D. defense - University of Bari Aldo Moro, Italy - 08.06.12
  • 80. Distributional Models term/context matrix c1 c2 c3 c4 c5 c6 c7 c8 c9 t1 ✔ ✔ ✔ ✔ t2 ✔ ✔ ✔ ✔ t3 ✔ ✔ ✔ t4 ✔ ✔ ✔ ✔ Cataldo Musto - Enhanced Vector Space Models for Content-based Recommender Systems - Ph.D. defense - University of Bari Aldo Moro, Italy - 08.06.12
  • 81. distributional models beer vs. glass: good overlap c1 c2 c3 c4 c5 c6 c7 c8 c9 t1 ✔ ✔ ✔ ✔ t2 ✔ ✔ ✔ ✔ t3 ✔ ✔ ✔ t4 ✔ ✔ ✔ ✔ Cataldo Musto - Enhanced Vector Space Models for Content-based Recommender Systems - Ph.D. defense - University of Bari Aldo Moro, Italy - 08.06.12
  • 82. distributional models beer vs. spoon: no overlap c1 c2 c3 c4 c5 c6 c7 c8 c9 t1 ✔ ✔ ✔ ✔ t2 ✔ ✔ ✔ ✔ t3 ✔ ✔ ✔ t4 ✔ ✔ ✔ ✔ Cataldo Musto - Enhanced Vector Space Models for Content-based Recommender Systems - Ph.D. defense - University of Bari Aldo Moro, Italy - 08.06.12
  • 83. distributional models recap models for representing terms/ documents in large vector spaces light semantics it is simple to calculate similarities between words but the high dimensionality problem is even worsened! Cataldo Musto - Enhanced Vector Space Models for Content-based Recommender Systems - Ph.D. defense - University of Bari Aldo Moro, Italy - 08.06.12
  • 84. a novel recommendation framework based on VSM eVSM step 1: modeling semantics step 2: dimensionality reduction step 3: modeling negation step 4: building user profiles step 5: providing suggestions Cataldo Musto - Enhanced Vector Space Models for Content-based Recommender Systems - Ph.D. defense - University of Bari Aldo Moro, Italy - 08.06.12
  • 85. Random Indexing (Sahlgren, 2005) Sahlgren, M. An Introduction to Random Indexing. Proceedings of the Methods and Applications of Semantic Indexing Workshop, TKE 2005. Cataldo Musto - Enhanced Vector Space Models for Content-based Recommender Systems - Ph.D. defense - University of Bari Aldo Moro, Italy - 08.06.12
  • 86. dimensionality reduction random indexing • Strenghts • Incremental approach • Based on distributional hypothesis • Builds a small-scale semantic vector space representation Cataldo Musto - Enhanced Vector Space Models for Content-based Recommender Systems - Ph.D. defense - University of Bari Aldo Moro, Italy - 08.06.12
  • 87. random indexing • Input • n-dimensional term-document matrix • Output • k-dimensional term-context matrix • k << n • Approximation built upon distributional hypothesis • Based on contexts, but much more compact! Cataldo Musto - Enhanced Vector Space Models for Content-based Recommender Systems - Ph.D. defense - University of Bari Aldo Moro, Italy - 08.06.12
  • 88. random indexing dimensionality reduction d1 d2 d3 d4 d5 . . . dn c1 c2 c3 c4 c5 . . . ck t1 t1 t2 n >> k t2 t3 t3 t4 t4 t5 t5 term/document matrix term/context matrix Cataldo Musto - Enhanced Vector Space Models for Content-based Recommender Systems - Ph.D. defense - University of Bari Aldo Moro, Italy - 08.06.12
  • 89. random indexing dimensionality reduction d1 d2 d3 d4 d5 . . . dn c1 c2 c3 c4 c5 . . . ck t1 t1 t2 n >> k t2 k is a simple t3 t3 parameter of the model t4 t4 t5 t5 term/document matrix term/context matrix Cataldo Musto - Enhanced Vector Space Models for Content-based Recommender Systems - Ph.D. defense - University of Bari Aldo Moro, Italy - 08.06.12
  • 90. random indexing dimensionality reduction d1 d2 d3 d4 d5 . . . dn c1 c2 c3 c4 c5 . . . ck t1 t1 t2 n >> k t2 the smaller , the k more the efficiency t3 t3 and the loss of t4 t4 information t5 t5 term/document matrix term/context matrix Cataldo Musto - Enhanced Vector Space Models for Content-based Recommender Systems - Ph.D. defense - University of Bari Aldo Moro, Italy - 08.06.12
  • 91. random indexing some literature • Roots • Sparse distributed representations (Kanerva, 1988) • Studies about Random Projection • State of the art applications • Clustering text documents (Kohonen, 2000) • Image data compression (Bingham, 2001) • Information Retrieval (Basile, 2010) • Collaborative filtering (Cisielczyk, 2010) • Never exploited for CBRS. Cataldo Musto - Enhanced Vector Space Models for Content-based Recommender Systems - Ph.D. defense - University of Bari Aldo Moro, Italy - 08.06.12
  • 92. How to obtain the smaller k-dimensional representation? Cataldo Musto - Enhanced Vector Space Models for Content-based Recommender Systems - Ph.D. defense - University of Bari Aldo Moro, Italy - 08.06.12
  • 93. random indexing algorithm • (1) Definition of the context. • Document ? Paragraph ? Sentence ? Word ? • (2) Each ‘context’ is assigned a context vector. • Dimension of the vector = k • Allowed values = {-1, 0, 1} • Constraints: non-zero elements have to be much smaller • Values distributed in a random way Cataldo Musto - Enhanced Vector Space Models for Content-based Recommender Systems - Ph.D. defense - University of Bari Aldo Moro, Italy - 08.06.12
  • 94. random indexing context vectors k=8 rc1 = (0, 0, -1, 1, 0, 0, 0, 0) rc2 = (1, 0, 0, 0, 0, 0, 0, -1) rc3 = (0, 0, 0, 0, 0, -1, 1, 0) rc4 = (-1, 1-, 0, 0, 0, 0, 0, 0) rc5 = (0, 0, 0, -1, 1, 0, 0, 0) Cataldo Musto - Enhanced Vector Space Models for Content-based Recommender Systems - Ph.D. defense - University of Bari Aldo Moro, Italy - 08.06.12
  • 95. random indexing algorithm • (3) The vector space representation of a term t is obtained by combining the random vectors of the contexts it occurs in. rc1 = (0, 0, -1, 1, 0, 0, 0, 0) rc2 = (1, 0, 0, 0, 0, 0, 0, -1) rc3 = (0, 0, 0, 0, 0, -1, 1, 0) t1 ∈ {c1, c2} rc4 = (-1, 1-, 0, 0, 0, 0, 0, 0) rc5 = (0, 0, 0, -1, 1, 0, 0, 0) Cataldo Musto - Enhanced Vector Space Models for Content-based Recommender Systems - Ph.D. defense - University of Bari Aldo Moro, Italy - 08.06.12
  • 96. random indexing algorithm • (3) The vector space representation of a term t is obtained by combining the random vectors of the contexts it occurs in. rc1 = (0, 0, -1, 1, 0, 0, 0, 0) t1 ∈ {c1, c2} rc2 = (1, 0, 0, 0, 0, 0, 0, -1) rc3 = (0, 0, 0, 0, 0, -1, 1, 0) rc1 = (0, 0, -1, 1, 0, 0, 0, 0) rc4 = (-1, 1-, 0, 0, 0, 0, 0, 0) rc2 = (1, 0, 0, 0, 0, 0, 0, -1 rc5 = (0, 0, 0, -1, 1, 0, 0, 0) t1 = (1, 0, -1, 1, 0, 0, 0, -1) Cataldo Musto - Enhanced Vector Space Models for Content-based Recommender Systems - Ph.D. defense - University of Bari Aldo Moro, Italy - 08.06.12
  • 97. random indexing algorithm • (3) The vector space representation of a term t is obtained by combining the random vectors of the contexts it occurs in. • (4) The vector space representation of a document d is obtained by combining the vector space representation of the terms that occur in the document. Cataldo Musto - Enhanced Vector Space Models for Content-based Recommender Systems - Ph.D. defense - University of Bari Aldo Moro, Italy - 08.06.12
  • 98. random indexing algorithm • (3) The vector space representation of a term t is obtained by combining the random vectors of the contexts it occurs in. output: WORDSPACE • (4) The vector space representation of a document d is obtained by combining the vector space representation of the terms that occur in the document. Cataldo Musto - Enhanced Vector Space Models for Content-based Recommender Systems - Ph.D. defense - University of Bari Aldo Moro, Italy - 08.06.12
  • 99. random indexing algorithm • (3) The vector space representation of a term t is obtained by combining the random vectors of the contexts it occurs in. output: DOCSPACE • (4) The vector space representation of a document d is obtained by combining the vector space representation of the terms that occur in the document. Cataldo Musto - Enhanced Vector Space Models for Content-based Recommender Systems - Ph.D. defense - University of Bari Aldo Moro, Italy - 08.06.12
  • 100. random indexing WordSpace DocSpace c1 c2 c3 c4 c5 . . . ck c1 c2 c3 c4 c5 . . . ck t1 d1 t2 Uniform d2 t3 Representation d3 t4 d4 t5 d5 Comparison between Comparison between terms documents Cataldo Musto - Enhanced Vector Space Models for Content-based Recommender Systems - Ph.D. defense - University of Bari Aldo Moro, Italy - 08.06.12
  • 101. Dimensionality reduction is obtained upon a set of random vectors Does it sound weird? Cataldo Musto - Enhanced Vector Space Models for Content-based Recommender Systems - Ph.D. defense - University of Bari Aldo Moro, Italy - 08.06.12
  • 102. random indexing theoretical basis • Johnson-Lindenstauss Lemma (*) • Distance between points are approximately preserved. • Constraint: orthogonal vectors • Random Indexing vectors are nearly-ortoghonal. • The loss of information depends on the parameter k (*) Johnson, W and Lindenstauss, J. Extensions of lipschitz maps into a Hilbert space. Contemporary Mathematics, 1984 Cataldo Musto - Enhanced Vector Space Models for Content-based Recommender Systems - Ph.D. defense - University of Bari Aldo Moro, Italy - 08.06.12
  • 103. random indexing johnson-lindenstrauss lemma Cataldo Musto - Enhanced Vector Space Models for Content-based Recommender Systems - Ph.D. defense - University of Bari Aldo Moro, Italy - 08.06.12
  • 104. a novel recommendation framework based on VSM eVSM step 1: modeling semantics step 2: dimensionality reduction step 3: modeling negation step 4: building user profiles step 5: providing suggestions Cataldo Musto - Enhanced Vector Space Models for Content-based Recommender Systems - Ph.D. defense - University of Bari Aldo Moro, Italy - 08.06.12
  • 105. quantum negation (Widdows, 2007) Sahlgren, M. An Introduction to Random Indexing. Proceedings of the Methods and Applications of Semantic Indexing Workshop, TKE 2005. Cataldo Musto - Enhanced Vector Space Models for Content-based Recommender Systems - Ph.D. defense - University of Bari Aldo Moro, Italy - 08.06.12
  • 106. negation in VSMs state of the art • State-of-the-art approaches: poor theoretical background • Post-retrieval filtering, Rocchio Algorithm (Rocchio, 1971) • Widdows proposed a different point of view • Negation view as a form of orthogonality between vectors • Vision inherited from Quantum Logic Cataldo Musto - Enhanced Vector Space Models for Content-based Recommender Systems - Ph.D. defense - University of Bari Aldo Moro, Italy - 08.06.12
  • 107. negation in VSMs Quantum Negation • Some theory • Given vector a and vector b • Through quantum negation it is possible to defined a vector a not b (a ∧¬b) • Projection of vector a on the subspace orthogonal to those generated by vector b Cataldo Musto - Enhanced Vector Space Models for Content-based Recommender Systems - Ph.D. defense - University of Bari Aldo Moro, Italy - 08.06.12
  • 108. quantum negation application to CBRS • Vector A models positive feedbacks • Information about what a user likes • Vector B models negative feedbacks • Information about what a user does not like • Vector A not B combines both information sources Cataldo Musto - Enhanced Vector Space Models for Content-based Recommender Systems - Ph.D. defense - University of Bari Aldo Moro, Italy - 08.06.12
  • 109. eVSM building blocks - recap • Distributional Models • Light semantic modeling • Random Indexing (Sahlgren, 2005) • Incremental technique for dimensionality reduction • Quantum Negation (Widdows, 2007) • Negation operator based on Quantum Logic Cataldo Musto - Enhanced Vector Space Models for Content-based Recommender Systems - Ph.D. defense - University of Bari Aldo Moro, Italy - 08.06.12
  • 110. eVSM building blocks - recap • A content-based recommendation framework needs to: • Represent items • Build user profiles • Provide suggestions • Random Indexing and Quantum Negation provide a novel representation model. Cataldo Musto - Enhanced Vector Space Models for Content-based Recommender Systems - Ph.D. defense - University of Bari Aldo Moro, Italy - 08.06.12
  • 111. a novel recommendation framework based on VSM eVSM step 1: modeling semantics step 2: dimensionality reduction step 3: modeling negation step 4: building user profiles step 5: providing suggestions Cataldo Musto - Enhanced Vector Space Models for Content-based Recommender Systems - Ph.D. defense - University of Bari Aldo Moro, Italy - 08.06.12
  • 112. eVSM building user profiles • Represent profiles in eVSM • Vector space representation • Obtained by combining the vectors of the items the user liked • How? • Four different profiling models Cataldo Musto - Enhanced Vector Space Models for Content-based Recommender Systems - Ph.D. defense - University of Bari Aldo Moro, Italy - 08.06.12
  • 113. User Profiles Random Indexing-based (RI) Items Rating Threshold VSM representation of RI-based profile for user u Cataldo Musto - Enhanced Vector Space Models for Content-based Recommender Systems - Ph.D. defense - University of Bari Aldo Moro, Italy - 08.06.12
  • 114. User Profiles Quantum Negation-based (QN) Positive User Profile Vector Negative User Profile Vector VSM representation of QN-based profile for user u Cataldo Musto - Enhanced Vector Space Models for Content-based Recommender Systems - Ph.D. defense - University of Bari Aldo Moro, Italy - 08.06.12
  • 115. User Profiles Weighted Random Indexing-based (w-RI) Items Rating Threshold Higher weight given to the documents with higher rating Cataldo Musto - Enhanced Vector Space Models for Content-based Recommender Systems - Ph.D. defense - University of Bari Aldo Moro, Italy - 08.06.12
  • 116. User Profiles Weighted Quantum Negation-based (w-QN) Positive User Profile Vector Negative User Profile Vector VSM representation of wQN-based profile for user u Cataldo Musto - Enhanced Vector Space Models for Content-based Recommender Systems - Ph.D. defense - University of Bari Aldo Moro, Italy - 08.06.12
  • 117. a novel recommendation framework based on VSM eVSM step 1: modeling semantics step 2: dimensionality reduction step 3: modeling negation step 4: building user profiles step 5: providing suggestions Cataldo Musto - Enhanced Vector Space Models for Content-based Recommender Systems - Ph.D. defense - University of Bari Aldo Moro, Italy - 08.06.12
  • 118. eVSM providing suggestions - monolingual scenario DocSpace c1 c2 c3 c4 c5 . . . ck d1 d2 d3 d4 p P All the items are vectors in a DocSpace Cataldo Musto - Enhanced Vector Space Models for Content-based Recommender Systems - Ph.D. defense - University of Bari Aldo Moro, Italy - 08.06.12
  • 119. eVSM providing suggestions - monolingual scenario DocSpace c1 c2 c3 c4 c5 . . . ck d1 d2 d3 d4 p profile is a vector in a DocSpace Cataldo Musto - Enhanced Vector Space Models for Content-based Recommender Systems - Ph.D. defense - University of Bari Aldo Moro, Italy - 08.06.12
  • 120. eVSM providing suggestions - monolingual scenario DocSpace c1 c2 c3 c4 c5 . . . ck d1 d2 d3 d4 p Similarity calculation between p and each item Cataldo Musto - Enhanced Vector Space Models for Content-based Recommender Systems - Ph.D. defense - University of Bari Aldo Moro, Italy - 08.06.12
  • 121. Some maths (1/2) • Let • U set of users • I set of items • Given • active user u ∈ U Cataldo Musto - Enhanced Vector Space Models for Content-based Recommender Systems - Ph.D. defense - University of Bari Aldo Moro, Italy - 08.06.12
  • 122. Some maths (2/2) • For each couple (u, ij) • For both user u and item i a vector space representation is provided • u = (fu1, fu2 ... fun) • i = (fi1, fi2 ... fin) • Calculate sim(u, ij) • Cosine similarity • Order ij in a descending similarity order • Return the top-k elements Cataldo Musto - Enhanced Vector Space Models for Content-based Recommender Systems - Ph.D. defense - University of Bari Aldo Moro, Italy - 08.06.12
  • 123. Similarity-based recommendations Relevance of an item seen as a form of similarity The most similar items are returned to the target user Cataldo Musto - Enhanced Vector Space Models for Content-based Recommender Systems - Ph.D. defense - University of Bari Aldo Moro, Italy - 08.06.12
  • 124. What about multilanguage recommendations? Cataldo Musto - Enhanced Vector Space Models for Content-based Recommender Systems - Ph.D. defense - University of Bari Aldo Moro, Italy - 08.06.12
  • 125. eVSM providing suggestions - multilingual scenario • eVSM for multilingual recommendations • Assumption • The distribution of the terms is (almost) language- independent drink bere beer / birra glass bicchiere Cataldo Musto - Enhanced Vector Space Models for Content-based Recommender Systems - Ph.D. defense - University of Bari Aldo Moro, Italy - 08.06.12
  • 126. eVSM providing suggestions - multilingual scenario • eVSM for multilingual recommendations • Assumption • The distribution of the terms is (almost) language- independent • The position of concept of in a WordSpace beer will be always the same, regardless the language! Cataldo Musto - Enhanced Vector Space Models for Content-based Recommender Systems - Ph.D. defense - University of Bari Aldo Moro, Italy - 08.06.12
  • 127. (english) WordSpace beer wine spoon dog Cataldo Musto - Enhanced Vector Space Models for Content-based Recommender Systems - Ph.D. defense - University of Bari Aldo Moro, Italy - 08.06.12
  • 128. (italian) WordSpace relationships between terms stay birra regardless the language! vino cucchiaio cane Cataldo Musto - Enhanced Vector Space Models for Content-based Recommender Systems - Ph.D. defense - University of Bari Aldo Moro, Italy - 08.06.12
  • 129. eVSM providing suggestions - multilingual scenario DocSpace for L1 DocSpace for L2 c1 c2 c3 c4 c5 . . . ck c1 c2 c3 c4 c5 . . . ck Parallel d1 DocSpaces d1 d2 Built upon the d2 same d3 d3 set of d4 random d4 d5 vectors d5 (italian) (english) Cataldo Musto - Enhanced Vector Space Models for Content-based Recommender Systems - Ph.D. defense - University of Bari Aldo Moro, Italy - 08.06.12
  • 130. eVSM providing suggestions - multilingual scenario DocSpace for L1 DocSpace for L2 c1 c2 c3 c4 c5 . . . ck c1 c2 c3 c4 c5 . . . ck Parallel d1 DocSpaces d1 d2 Built upon the d2 same d3 d3 set of d4 random d4 p vectors d5 L1 user profile in L1 (italian) Cataldo Musto - Enhanced Vector Space Models for Content-based Recommender Systems - Ph.D. defense - University of Bari Aldo Moro, Italy - 08.06.12
  • 131. eVSM providing suggestions - multilingual scenario DocSpace for L1 DocSpace for L2 c1 c2 c3 c4 c5 . . . ck c1 c2 c3 c4 c5 . . . ck Parallel d1 DocSpaces d1 d2 Built upon the d2 same d3 d3 set of d4 random d4 p vectors p L1 L1 we can project user profile in the DocSpace of english items Cataldo Musto - Enhanced Vector Space Models for Content-based Recommender Systems - Ph.D. defense - University of Bari Aldo Moro, Italy - 08.06.12
  • 132. eVSM providing suggestions - multilingual scenario DocSpace for L1 DocSpace for L2 c1 c2 c3 c4 c5 . . . ck c1 c2 c3 c4 c5 . . . ck Parallel d1 DocSpaces d1 d2 Built upon the d2 same d3 d3 set of d4 random d4 p vectors p L1 L1 similarity computations of italian profile with english items to build multilingual recommendations Cataldo Musto - Enhanced Vector Space Models for Content-based Recommender Systems - Ph.D. defense - University of Bari Aldo Moro, Italy - 08.06.12
  • 133. Multilingual recommendations come with no costs. Thanks to distributional hypothesis. Cataldo Musto - Enhanced Vector Space Models for Content-based Recommender Systems - Ph.D. defense - University of Bari Aldo Moro, Italy - 08.06.12
  • 134. experimental evaluation applications Cataldo Musto - Enhanced Vector Space Models for Content-based Recommender Systems - Ph.D. defense - University of Bari Aldo Moro, Italy - 08.06.12
  • 135. evaluation of eVSM • selected experiments • movie recommendation • monolingual scenario • Cataldo Musto, Giovanni Semeraro, Pasquale Lops, Marco de Gemmis: Random Indexing and Negative User Preferences for Enhancing Content-Based Recommender Systems. EC-Web 2011. 270-281 • multilingual scenario • Cataldo Musto, Fedelucio Narducci, Pierpaolo Basile, Pasquale Lops, Marco de Gemmis, Giovanni Semeraro: Cross-Language Information Filtering: Word Sense Disambiguation vs. Distributional Models. AI*IA 2011 • epg personalization • Cataldo Musto, Fedelucio Narducci, Pasquale Lops, Giovanni Semeraro, Marco de Gemmis, Mauro Barbieri, Jan H. M. Korst,Verus Pronk, Ramon Clout. Enhanced Semantic TV-Show Representation for personalized electronic program guides. UMAP 2012 (to be presented) Cataldo Musto - Enhanced Vector Space Models for Content-based Recommender Systems - Ph.D. defense - University of Bari Aldo Moro, Italy - 08.06.12
  • 136. movie recommendation ‘in vitro’ experiments • Goal: to provide users with recommendations about movies worth to be watched. • Subset of 100k MovieLens dataset + Wikipedia content • Monolingual and Multilingual settings Cataldo Musto - Enhanced Vector Space Models for Content-based Recommender Systems - Ph.D. defense - University of Bari Aldo Moro, Italy - 08.06.12
  • 137. monolingual experiment parameter tuning • Size of context vectors • k = 50, 100, 200, 400 • 99% reduction of DocSpace • original size: 25k • Profiling models • RI, w-RI, QN- w-QN • Weighted vs. Unweighted • With negations vs. without negation Cataldo Musto - Enhanced Vector Space Models for Content-based Recommender Systems - Ph.D. defense - University of Bari Aldo Moro, Italy - 08.06.12
  • 138. experimental design experiments • Experiment 1 • Do the weighting scheme and the introduction of a negation operator improve the predictive accuracy of the recommendation models? • Experiment 2 • How do the model perform with respect to other state of the art approaches? Cataldo Musto - Enhanced Vector Space Models for Content-based Recommender Systems - Ph.D. defense - University of Bari Aldo Moro, Italy - 08.06.12
  • 139. experiment 1 size=100 - Movielens dataset 87 86.69 RI WRI QN WQN 86.25 86.17 85.7485.8 85.61 85.57 85.4685.43 85.5 85.36 85.29 85.03 84.84 84.9 84.7884.8184.84 84.75 84 p@1 P@3 P@5 P@10 Weighted vs Unweighted: improvement under 0.2% Cataldo Musto - Enhanced Vector Space Models for Content-based Recommender Systems - Ph.D. defense - University of Bari Aldo Moro, Italy - 08.06.12
  • 140. experiment 1 size=100 - Movielens dataset 87 86.69 RI WRI QN WQN 86.25 86.17 85.7485.8 85.61 85.57 85.4685.43 85.5 85.36 85.29 85.03 84.84 84.9 84.7884.8184.84 84.75 84 p@1 P@3 P@5 P@10 Weighted vs Unweighted: improvement under 0.2% Cataldo Musto - Enhanced Vector Space Models for Content-based Recommender Systems - Ph.D. defense - University of Bari Aldo Moro, Italy - 08.06.12
  • 141. experiment 1 size=100 - Movielens dataset 87 86.69 RI WRI QN WQN 86.25 86.17 Peak: +0.52 85.8 85.74 85.61 85.57 85.4685.43 85.5 85.36 85.29 85.03 84.84 84.9 84.7884.8184.84 84.75 84 p@1 P@3 P@5 P@10 However, differences are not statistically significant Cataldo Musto - Enhanced Vector Space Models for Content-based Recommender Systems - Ph.D. defense - University of Bari Aldo Moro, Italy - 08.06.12
  • 142. experiment 1 size=400 - Movielens dataset 87 RI WRI QN WQN 86.25 86.01 85.94 85.82 85.59 85.6 85.48 85.55 85.5285.5585.58 85.52 85.5 85.32 85.34 85.24 84.94 84.86 84.75 84 p@1 P@3 P@5 P@10 Negation vs No-negation: improvement under 0.5% Cataldo Musto - Enhanced Vector Space Models for Content-based Recommender Systems - Ph.D. defense - University of Bari Aldo Moro, Italy - 08.06.12
  • 143. experiment 1 size=100 - Movielens dataset 87 86.69 RI WRI QN WQN 86.25 86.17 Gap: +1.08 85.8 85.74 85.61 85.57 85.46 85.43 85.5 85.36 85.29 85.03 84.84 84.9 84.78 84.81 84.84 84.75 84 p@1 P@3 P@5 P@10 Some exception, P@1 and P@3 , comparison W-RI vs. W-QN Cataldo Musto - Enhanced Vector Space Models for Content-based Recommender Systems - Ph.D. defense - University of Bari Aldo Moro, Italy - 08.06.12
  • 144. experiment 1 size=100 - Movielens dataset 87 86.69 RI WRI QN WQN 86.25 86.17 85.74 85.8 85.61 85.57 85.5 85.29 85.36 Gap: +0.77 85.46 85.43 85.03 84.84 84.9 84.78 84.81 84.84 84.75 84 p@1 P@3 P@5 P@10 The use of negation operator improves the accuracy in a significant way. Cataldo Musto - Enhanced Vector Space Models for Content-based Recommender Systems - Ph.D. defense - University of Bari Aldo Moro, Italy - 08.06.12
  • 145. experiment 1 size=100 - Movielens dataset 87 86.69 RI WRI QN WQN 86.25 86.17 Gap: +1.08 85.8 85.74 85.61 85.57 85.46 85.43 85.5 85.36 85.29 85.03 84.84 84.9 84.78 84.81 84.84 84.75 84 p@1 P@3 P@5 P@10 Peaks in P@1 and P@3 are statistically significant Cataldo Musto - Enhanced Vector Space Models for Content-based Recommender Systems - Ph.D. defense - University of Bari Aldo Moro, Italy - 08.06.12
  • 146. experiment 1 size=100 - Movielens dataset 87 86.69 RI WRI QN WQN 86.25 86.17 85.74 85.8 85.61 85.57 85.46 85.43 85.5 85.36 85.29 85.03 84.84 84.9 84.78 84.81 84.84 84.75 84 p@1 P@3 P@5 P@10 Generally speaking, W-QN configuration outperforms the others. Cataldo Musto - Enhanced Vector Space Models for Content-based Recommender Systems - Ph.D. defense - University of Bari Aldo Moro, Italy - 08.06.12
  • 147. experiment 1 size=100 - Movielens dataset 87 86.69 RI WRI QN WQN 86.25 86.17 85.74 85.8 85.61 Gap: +1.4% 85.57 85.46 85.43 85.5 85.36 85.29 85.03 84.84 84.9 84.78 84.81 84.84 84.75 84 p@1 P@3 P@5 P@10 The combined use of weigthing and negation significally improves the accuracy Cataldo Musto - Enhanced Vector Space Models for Content-based Recommender Systems - Ph.D. defense - University of Bari Aldo Moro, Italy - 08.06.12
  • 148. experiment 1 impact of negation operator and weighting scheme context vectors - size 50 100 200 400 P@1 ✔ ✔ ✔ P@3 ✔ ✔ ✔ P@5 P@10 ✔ ✔ = statistical significance Cataldo Musto - Enhanced Vector Space Models for Content-based Recommender Systems - Ph.D. defense - University of Bari Aldo Moro, Italy - 08.06.12
  • 149. experiment 1 impact of negation operator and weighting scheme context vectors - size 50 100 200 400 P@1 ✔ ✔ ✔ P@3 ✔ ✔ ✔ P@5 P@10 ✔ The combined use of weigthing and negation significally improves the accuracy Cataldo Musto - Enhanced Vector Space Models for Content-based Recommender Systems - Ph.D. defense - University of Bari Aldo Moro, Italy - 08.06.12
  • 150. experiment 2 87 size=400 - Movielens dataset eVSM VSM 86.25 85.94 86.01 LSI Bayes 85.58 85.52 85.5 85.39 85.27 84.97 84.85 84.77 84.75 84.75 84.7 84.7 84.58 84.47 84.5 84.43 84 p@1 P@3 P@5 P@10 Gap always around 1% Cataldo Musto - Enhanced Vector Space Models for Content-based Recommender Systems - Ph.D. defense - University of Bari Aldo Moro, Italy - 08.06.12
  • 151. experiment 2 87 size=400 - Movielens dataset eVSM VSM 86.25 85.94 86.01 LSI Bayes 85.58 85.52 85.5 85.39 85.27 84.97 84.85 84.77 84.75 84.75 84.7 84.7 84.58 84.47 84.5 84.43 84 p@1 P@3 P@5 P@10 Significant Improvement Cataldo Musto - Enhanced Vector Space Models for Content-based Recommender Systems - Ph.D. defense - University of Bari Aldo Moro, Italy - 08.06.12

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