Using Personality Information in Collaborative Filtering for New Usersrong Hu, Pearl PuProceedings of the 2nd ACM RecSys’1...
Syllabus<br /><ul><li>Introduction & Motivation
Collaborative Filtering (CF) method
Cold Start Problem
Personality-based Collaborative Filtering
Personality Model
Personality-based Similarity Measure
Empirical Analysis
Evaluation Metrics
Experiment Results
Conclusion</li></ul>2<br />
Collaborative Filtering (CF) method<br />3<br />
Cold Start<br /><ul><li>It is hard to generate proper recommendations for new items because there isn't enough taste data ...
Personality-based Collaborative Filtering<br />5<br />
Personality Model<br /><ul><li>Big Five Factor personality model
Openness to Experience 嘗試精神
Conscientiousness 盡責性
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Using personality information in collaborative filtering for new User

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  • Objectives for instruction and expected results and/or skills developed from learning.
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  • Relative vocabulary list.
  • Relative vocabulary list.
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  • Using personality information in collaborative filtering for new User

    1. 1. Using Personality Information in Collaborative Filtering for New Usersrong Hu, Pearl PuProceedings of the 2nd ACM RecSys’10 Workshop on Recommender Systems and the Social Web<br />Vincent Chu2010/10/6<br />
    2. 2. Syllabus<br /><ul><li>Introduction & Motivation
    3. 3. Collaborative Filtering (CF) method
    4. 4. Cold Start Problem
    5. 5. Personality-based Collaborative Filtering
    6. 6. Personality Model
    7. 7. Personality-based Similarity Measure
    8. 8. Empirical Analysis
    9. 9. Evaluation Metrics
    10. 10. Experiment Results
    11. 11. Conclusion</li></ul>2<br />
    12. 12. Collaborative Filtering (CF) method<br />3<br />
    13. 13. Cold Start<br /><ul><li>It is hard to generate proper recommendations for new items because there isn't enough taste data about the new items to make reliable correlations with other items.</li></ul>4<br />
    14. 14. Personality-based Collaborative Filtering<br />5<br />
    15. 15. Personality Model<br /><ul><li>Big Five Factor personality model
    16. 16. Openness to Experience 嘗試精神
    17. 17. Conscientiousness 盡責性
    18. 18. Extroversion 外向程度
    19. 19. Agreeableness 合群度
    20. 20. Neuroticism 情緒穩定度</li></ul>6<br />
    21. 21. Personality Model<br /><ul><li>10-Item Personality Inventory (TIPI)
    22. 22. Each item consists of two descriptions separated by a comma.
    23. 23. Each item was rated on a 7-point scale rating for 1(Strongly disagree) to 7(Strongly agree).
    24. 24. Ex. I see myself1. ( ) Extraverted , Enthusiastic 2. ( ) Critical, Quarrelsome </li></ul>7<br />
    25. 25. Personality-based Collaborative Filtering<br /><ul><li>Modified Pearson correlation
    26. 26. Original Pearson correlation
    27. 27. Modified Pearson correlation</li></ul>8<br />
    28. 28. Personality-based Collaborative Filtering<br />9<br />simr(u,v)=2−41−3.3+4−45−3.3+(6−4)(4−3.3)(2−4)2+(4−4)2+(6−4)2∗(1−3.3)2+(5−3.3)2+(4−3.3)2=68.39=0.715<br /> <br />Let γ =2 ,    simr'(u,v)=min(3,2)2∗ simr(u,v)=0.715<br /> <br />
    29. 29. Personality-based Collaborative Filtering<br /><ul><li>Personality Pearson correlation
    30. 30. Personality Descriptor
    31. 31. Personality Pearson correlation
    32. 32. Personality-based Collaborative Filtering</li></ul>10<br />
    33. 33. Personality-based Collaborative Filtering<br />11<br />simp(u,v)=−0.1<br /> <br />Let α =0.5 ,    simu,v=0.5∗simr′(u,v)+0.5∗ simp(u,v)=0.3075<br /> <br />
    34. 34. Evaluation Metrics<br /><ul><li>Predictive Accuracy Metric
    35. 35. Mean absolute error(MAE)
    36. 36. Decision-Support Accuracy Metrics
    37. 37. Recall & Specificity</li></ul>12<br />
    38. 38. Dataset <br /><ul><li>Statistical characteristic of Dataset</li></ul>13<br />
    39. 39. Influence of Model Size<br />14<br />
    40. 40. Influence of Weight<br />15<br />
    41. 41. Influence of Training Size<br />16<br />
    42. 42. Conclusion<br /><ul><li>Limitations
    43. 43. Dataset is too small
    44. 44. Didn’t evaluate the performance of each similarity measure using a dataset whose user-item matrix has a higher density
    45. 45. Future work</li></ul>17<br />

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