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Criteria Chains: A Novel Multi-Criteria
Recommendation Approach
Yong Zheng
Illinois Institute of Technology
Chicago, IL, 60616, USA
ACM Conference on Intelligent User Interfaces
Limassol, Cyprus, March 13-16, 2017
Recommender System (RS)
• RS: item recommendations tailored to user tastes
2
Traditional RS: Ratings By Users on Items
3
Red
Mars
Juras-
sic
Park
Lost
World
2001
Found
ation
Differ-
ence
Engine
Recommender
Systems
User
Profile
Neuro-
mancer
2010
Recommendations
4
Multi-Criteria Recommender Systems
5
Multi-Criteria Recommender Systems
6
Multi-Criteria Recommender Systems
• Traditional RS:
• Multi-Criteria RS:
R0 is a user’s overall rating on the item. R1, R2, …, Rk are ratings on item aspects.
7
Multi-Criteria Recommender Systems
Research Problems in Multi-Criteria RS
Step2
Multi-Criteria RatingsStep 1 Step 1
Step 1. Learn from knowledge to predict multi-criteria ratings
Step 2. Aggregate multi-criteria ratings to predict the overall rating.
Linear Regression:
8
Multi-Criteria Recommender Systems
There are two solutions to improve it:
• Improve the predicted multi-criteria ratings
• Better utilize them to estimate the overall rating
The contributions by Criteria Chains:
• Better predict multi-criteria ratings
• Figure out a new way to aggregate these ratings
9
Criteria Chains
Assumptions in Criteria Chains
• Multi-criteria ratings can be viewed as contexts
• Ratings can be predicted in a chain
10
Criteria Chains
Assumptions in Criteria Chains
• Multi-criteria ratings can be viewed as contexts
• Ratings can be predicted in a chain
First, predict U3’s rating on Room
Next, take U3’s rating on room as contexts, User + Item + Room  Check-in
Again, take previous predictions as contexts, User + Item + Room + Check-in  Service
The prediction process works like a chain: Room  Check-in  Service
11
Criteria Chains
The sequence of the chain matters
• Random Sequence
• Rank by Lower Prediction Errors
• Rank by Information Gain
12
Criteria Chains
How to predict the final overall rating?
• Criteria Chain: Aggregation Model (CCA)
Linear regression by predicted multi-criteria ratings
• Criteria Chain: Contextual Model (CCC)
Direct prediction by viewing the predicted multi-
criteria ratings as context information
• Criteria-Independent Contextual Model (CIC)
This is a baseline approach. We predicted multi-
criteria ratings independently and use them as
context to predict the final overall rating
13
Experimental Setting
• Data Sets
• Evaluations
– Five-fold Cross Validation
– Rating Prediction: Mean Absolute Error (MAE)
– Top-N Recommendation: Precision, Recall, NDCG
– We use CAMF_C for context-aware recommendations
User Item Rating # of Criteria
TripAdvisor 1,502 14,300 22,130 7
Yahoo!Movie 2,162 3,075 49,351 4
14
Experimental Results
The overall result demonstrates CCA and CCC outperform baselines, CCC is the best
15
Experimental Results
Criteria Chains is able to improve the predictions on individual ratings on criterion
16
Experimental Results
Information Gain is the best way to produce the optimal sequence
17
Conclusions and Future Work
• Criteria Chains work better than baseline approaches
• Criteria Chains take correlations among multiple
criteria into consideration
• Information Gain is the best way to produce chain
• Using multi-criteria ratings as contexts, CCC, is the
best approach after predictions on multiple ratings
• Future Work: figure out optimal ways to generate the
chain sequence in addition to information gain.
Criteria Chains: A Novel Multi-Criteria
Recommendation Approach
Yong Zheng
Illinois Institute of Technology
Chicago, IL, 60616, USA
ACM Conference on Intelligent User Interfaces
Limassol, Cyprus, March 13-16, 2017

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[IUI 2017] Criteria Chains: A Novel Multi-Criteria Recommendation Approach

  • 1. Criteria Chains: A Novel Multi-Criteria Recommendation Approach Yong Zheng Illinois Institute of Technology Chicago, IL, 60616, USA ACM Conference on Intelligent User Interfaces Limassol, Cyprus, March 13-16, 2017
  • 2. Recommender System (RS) • RS: item recommendations tailored to user tastes 2
  • 3. Traditional RS: Ratings By Users on Items 3 Red Mars Juras- sic Park Lost World 2001 Found ation Differ- ence Engine Recommender Systems User Profile Neuro- mancer 2010 Recommendations
  • 6. 6 Multi-Criteria Recommender Systems • Traditional RS: • Multi-Criteria RS: R0 is a user’s overall rating on the item. R1, R2, …, Rk are ratings on item aspects.
  • 7. 7 Multi-Criteria Recommender Systems Research Problems in Multi-Criteria RS Step2 Multi-Criteria RatingsStep 1 Step 1 Step 1. Learn from knowledge to predict multi-criteria ratings Step 2. Aggregate multi-criteria ratings to predict the overall rating. Linear Regression:
  • 8. 8 Multi-Criteria Recommender Systems There are two solutions to improve it: • Improve the predicted multi-criteria ratings • Better utilize them to estimate the overall rating The contributions by Criteria Chains: • Better predict multi-criteria ratings • Figure out a new way to aggregate these ratings
  • 9. 9 Criteria Chains Assumptions in Criteria Chains • Multi-criteria ratings can be viewed as contexts • Ratings can be predicted in a chain
  • 10. 10 Criteria Chains Assumptions in Criteria Chains • Multi-criteria ratings can be viewed as contexts • Ratings can be predicted in a chain First, predict U3’s rating on Room Next, take U3’s rating on room as contexts, User + Item + Room  Check-in Again, take previous predictions as contexts, User + Item + Room + Check-in  Service The prediction process works like a chain: Room  Check-in  Service
  • 11. 11 Criteria Chains The sequence of the chain matters • Random Sequence • Rank by Lower Prediction Errors • Rank by Information Gain
  • 12. 12 Criteria Chains How to predict the final overall rating? • Criteria Chain: Aggregation Model (CCA) Linear regression by predicted multi-criteria ratings • Criteria Chain: Contextual Model (CCC) Direct prediction by viewing the predicted multi- criteria ratings as context information • Criteria-Independent Contextual Model (CIC) This is a baseline approach. We predicted multi- criteria ratings independently and use them as context to predict the final overall rating
  • 13. 13 Experimental Setting • Data Sets • Evaluations – Five-fold Cross Validation – Rating Prediction: Mean Absolute Error (MAE) – Top-N Recommendation: Precision, Recall, NDCG – We use CAMF_C for context-aware recommendations User Item Rating # of Criteria TripAdvisor 1,502 14,300 22,130 7 Yahoo!Movie 2,162 3,075 49,351 4
  • 14. 14 Experimental Results The overall result demonstrates CCA and CCC outperform baselines, CCC is the best
  • 15. 15 Experimental Results Criteria Chains is able to improve the predictions on individual ratings on criterion
  • 16. 16 Experimental Results Information Gain is the best way to produce the optimal sequence
  • 17. 17 Conclusions and Future Work • Criteria Chains work better than baseline approaches • Criteria Chains take correlations among multiple criteria into consideration • Information Gain is the best way to produce chain • Using multi-criteria ratings as contexts, CCC, is the best approach after predictions on multiple ratings • Future Work: figure out optimal ways to generate the chain sequence in addition to information gain.
  • 18. Criteria Chains: A Novel Multi-Criteria Recommendation Approach Yong Zheng Illinois Institute of Technology Chicago, IL, 60616, USA ACM Conference on Intelligent User Interfaces Limassol, Cyprus, March 13-16, 2017