Using personality information in collaborative filtering for new User

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
Vincent Chu2010/10/6

Syllabus
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
2

Collaborative Filtering (CF) method
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Cold Start
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.
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5

Personality Model
Big Five Factor personality model
Openness to Experience 嘗試精神
Conscientiousness 盡責性
Extroversion 外向程度
Agreeableness 合群度
Neuroticism 情緒穩定度
6

Personality Model
10-Item Personality Inventory (TIPI)
Each item consists of two descriptions separated by a comma.
Each item was rated on a 7-point scale rating for 1(Strongly disagree) to 7(Strongly agree).
Ex. I see myself1. ( ) Extraverted , Enthusiastic 2. ( ) Critical, Quarrelsome
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Modified Pearson correlation
Original Pearson correlation
Modified Pearson correlation
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9
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

Let γ =2 , simr'(u,v)=min(3,2)2∗ simr(u,v)=0.715

Personality Pearson correlation
Personality Descriptor
Personality Pearson correlation
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simp(u,v)=−0.1

Let α =0.5 , simu,v=0.5∗simr′(u,v)+0.5∗ simp(u,v)=0.3075

Evaluation Metrics
Predictive Accuracy Metric
Mean absolute error(MAE)
Decision-Support Accuracy Metrics
Recall & Specificity
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Dataset
Statistical characteristic of Dataset
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Influence of Model Size
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Influence of Weight
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Influence of Training Size
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Conclusion
Limitations
Dataset is too small
Didn’t evaluate the performance of each similarity measure using a dataset whose user-item matrix has a higher density
Future work
17

Using personality information in collaborative filtering for new User

by Vincent Chu

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