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Context-aware User Modelling and Generation of Recommendations in Recommender Systems

Abstrakt

Recommender systems are software tools and techniques which aim at suggesting new items that may be of interest to a user. This dissertation is focused on four problems in recommender systems domain. The first one is context-awareness, i.e. how to obtain relevant contextual information, how to model user preferences in a context and use them to make predictions. The second one is multi-domain recommendation, which aim at suggesting items from many domains using user ratings from all of them. The third one is new user cold-start problem which occurs when a new user registers into a recommender system. He will not receive interesting recommendations just because the system does not know his preferences yet. The last problem is the need of generating explanations on how recommender system works. Two contextual user models and appropriate recommendation algorithms were proposed. First model is based on existing SIM method. The ontology-based contextual pre-filtering technique associated with the model allows dynamic generalization of contextual parameters values and generation of multi-domain recommendations. The second model consists of contextual conditional preferences. The post-filtering method associated with it, which is called re-rankCCP, allows to generate explanations for users and contextual recommendations in new user cold-start situations as well as in typical scenarios.

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Informacje szczegółowe

Kategoria:
Doktoraty, rozprawy habilitacyjne, nostryfikacje
Typ:
praca doktorska pracowników zatrudnionych w PG oraz studentów studium doktoranckiego
Język:
angielski
Rok wydania:
2018
Bibliografia: test
  1. Context as a box (from [10]). . . . . . . . . . . . . . . . . . . . . . . . . . . . 5 otwiera się w nowej karcie
  2. 2.2 The magic box (from [94]). . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6 otwiera się w nowej karcie
  3. Push and pop (from [11]). . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7
  4. Shifting (from [11]). . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7 otwiera się w nowej karcie
  5. Classification of ontologies by the level of language formalization (from [108]). 10 otwiera się w nowej karcie
  6. Structured-Interpretation Model (from [106]). . . . . . . . . . . . . . . . . . . 13 otwiera się w nowej karcie
  7. An example of SIM ontology (based on [106]). . . . . . . . . . . . . . . . . . . 14
  8. An example of SIM ontology (from [38]). . . . . . . . . . . . . . . . . . . . . . 14 otwiera się w nowej karcie
  9. Partial Definition of CONON upper ontology (from [107]). . . . . . . . . . . . 15
  10. The SOUPA ontology (from [22]). . . . . . . . . . . . . . . . . . . . . . . . . . 16
  11. Examples of context taxonomies (from [44]). . . . . . . . . . . . . . . . . . . . 16 otwiera się w nowej karcie
  12. The PRISSMA vocabulary (from [23]). . . . . . . . . . . . . . . . . . . . . . . 17 otwiera się w nowej karcie
  13. Concepts and relations of RSCtx representing the time dimension (from [68]). 17 S in BPR method (from [90]). . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 28 otwiera się w nowej karcie
  14. Graphical model representation of LDA (from [13]). . . . . . . . . . . . . . . 30
  15. The CP-Net for choosing an outfit for a formal evening (from [16]). . . . . . . 34
  16. An example of the Contextual Ontological User Profile. . . . . . . . . . . . . 42
  17. The schema of the Ontology-based Contextual Pre-filtering Approach. . . . . 43 otwiera się w nowej karcie
  18. Post-filtering with re-rankCCP algorithm. . . . . . . . . . . . . . . . . . . . . 59 otwiera się w nowej karcie
  19. Chart for choosing the number of clusters for director variable. . . . . . . . 63 otwiera się w nowej karcie
  20. Boxplots for unalikeability analysis for LDOS-CoMoDa dataset (user level). . . . 69 otwiera się w nowej karcie
  21. Boxplots for unalikeability analysis for Unibz-STS dataset (user level). . . . . 70 otwiera się w nowej karcie
  22. Boxplots for unalikeability analysis for Restaurant & consumer dataset (user level). . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 71 otwiera się w nowej karcie
  23. The Class Structure of the LibRec Library (from [42]). . . . . . . . . . . . . . 73 otwiera się w nowej karcie
  24. The Class Structure of the CARSKit Library (from [112]). . . . . . . . . . . . 74 7.7 Values of MAE and RMSE for different methods applied on Restaurant & consumer dataset. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 75 otwiera się w nowej karcie
  25. Values of MAE for different methods applied on LDOS-CoMoDa dataset. . . . . 76 otwiera się w nowej karcie
  26. Values of RMSE for different methods applied on LDOS-CoMoDa dataset. . . . 76 7.10 Values of MAE for different methods applied on Unibz-STS dataset. . . . . . 77 otwiera się w nowej karcie
  27. Values of RMSE for different methods applied on Unibz-STS dataset. . . . . 77 otwiera się w nowej karcie
  28. Values of MAE for different methods applied on MovieTweetings dataset. . . 78 otwiera się w nowej karcie
  29. Values of RMSE for different methods applied on MovieTweetings dataset. . 78 otwiera się w nowej karcie
  30. Values of MAE for different methods applied on ConcertTweets dataset with numerical rating scale. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 79 otwiera się w nowej karcie
  31. Values of RMSE for different methods applied on ConcertTweets dataset with numerical rating scale. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 79 otwiera się w nowej karcie
  32. Values of MAE for different methods applied on ConcertTweets dataset with descriptive rating scale. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 80 otwiera się w nowej karcie
  33. Values of RMSE for different methods applied on ConcertTweets dataset with descriptive rating scale. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 80 otwiera się w nowej karcie
  34. Connections between context types in COUP for multi-domain recommendations. 88 List of Tables otwiera się w nowej karcie
  35. The ALC language constructors. . . . . . . . . . . . . . . . . . . . . . . . . . 11 4.1 Exemplary catalog of movies. . . . . . . . . . . . . . . . . . . . . . . . . . . . 20 4.2 Exemplary preference profile. . . . . . . . . . . . . . . . . . . . . . . . . . . . 20 4.3 Exemplary matrix of user-item ratings. . . . . . . . . . . . . . . . . . . . . . . 21 4.4 Exemplary product assortment: digital cameras (from [31]). . . . . . . . . . . 23 4.5 Input data requirements of recommendation algorithms (from [56]). . . . . . . 24 4.6 POI ratings in contexts (from [110]). . . . . . . . . . . . . . . . . . . . . . . . 32 4.7 Rating matrix transformed by item splitting (from [110]). . . . . . . . . . . . 32 otwiera się w nowej karcie
  36. An example of a test set. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 36 4.9 Ratings predicted by two RSs: RS1 and RS2 for the test set from Tab. 4.8. . 36 5.1 Example for rating prediction with COUP. . . . . . . . . . . . . . . . . . . . 44 5.2 Sample user preferences in the movie domain of Alice, Bob and Carol. . . . . 46 5.3 Sample user preferences in the restaurant domain of Alice, Bob and Carol. . . 47 5.4 Preferences of Alice, Bob and Carol after ontology-based contextual pre-filtering for Alice on Saturady with a friend (both domains). . . . . . . . . . . . . . . 47
  37. The trip decision example. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 51 otwiera się w nowej karcie
  38. 2 The subset of training set for trip decision example. . . . . . . . . . . . . . . 52 otwiera się w nowej karcie
  39. Sample user profiles of Alice, Bob and Carol. . . . . . . . . . . . . . . . . . . 54
  40. Contextual parameters from LDOS-CoMoDa dataset. . . . . . . . . . . . . . . . 62 otwiera się w nowej karcie
  41. Basic statistics of four datasets: LDOS-CoMoDa (CoMoDa), Unibz-STS (STS), Restaurant & consumer (RC) and MovieTweetings (MT). . . . . . . . . . . 62
  42. Contextual parameters from Unibz-STS dataset. . . . . . . . . . . . . . . . . 64 otwiera się w nowej karcie
  43. Contextual parameters from Restaurant & consumer dataset. . . . . . . . . 64
  44. Statistics on the data contained in ConcertTweets dataset. . . . . . . . . . . 66 otwiera się w nowej karcie
  45. Unalikeability analysis for LDOS-CoMoDa dataset. . . . . . . . . . . . . . . . . 68
  46. Unalikeability analysis for Unibz-STS dataset. . . . . . . . . . . . . . . . . . . 70
  47. Unalikeability analysis for Restaurant & consumer dataset. . . . . . . . . . . 71
  48. Measures for the typical scenario for LDOS-CoMoDa dataset. . . . . . . . . . . 83 otwiera się w nowej karcie
  49. Measures for the typical scenario for MovieTweetings dataset. . . . . . . . . 83 otwiera się w nowej karcie
  50. Measures for the typical scenario for ConcertTweets dataset with numerical rating scale. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 83 otwiera się w nowej karcie
  51. Measures for the typical scenario for Restaurant & consumer dataset. . . . . 84 otwiera się w nowej karcie
  52. Measures for the typical scenario for Unibz-STS dataset. . . . . . . . . . . . . 84
  53. Measures for the new user cold-start scenario for LDOS-CoMoDa dataset. . . . 85 otwiera się w nowej karcie
  54. Measures for the new user cold-start scenario for Unibz-STS dataset. . . . . . 86 otwiera się w nowej karcie
  55. Measures for the new user cold-start scenario for Restaurant & consumer dataset. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 86 otwiera się w nowej karcie
  56. Measures for the new user cold-start scenario for ConcertTweets dataset. . . 87 otwiera się w nowej karcie
  57. Measures for the new user cold-start scenario for MovieTweetings dataset. . 87 otwiera się w nowej karcie
  58. Results obtained for multi-domain recommendations with ontology-based con- textual pre-filtering. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 88 otwiera się w nowej karcie
  59. The number of respondents and evaluated recommendation list for each version of the questionnaire. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 90 otwiera się w nowej karcie
  60. The number of respondents and evaluated recommendation list for question- naires that contained explanations. . . . . . . . . . . . . . . . . . . . . . . . . 91 otwiera się w nowej karcie
  61. Results obtained with interactive questionnaires. . . . . . . . . . . . . . . . . 91 Bibliography otwiera się w nowej karcie
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