Criteria Chains: A Novel Multi-Criteria Recommendation Approach

Yong ZhengIllinois Institute of Technology

Chicago, IL, 60616, USA

ACM Conference on Intelligent User InterfacesLimassol, Cyprus, March 13-16, 2017

Recommender System (RS)

• RS: item recommendations tailored to user tastes

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Traditional RS: Ratings By Users on Items

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Red

Mars

Juras-

sic

Park

Lost

World

2001

Found

ation

Differ-

ence

Engine

Recommender

Systems

User

Profile

Neuro-

mancer2010

Recommendations

4

Multi-Criteria Recommender Systems

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Multi-Criteria Recommender Systems

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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.

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Multi-Criteria Recommender Systems

Research Problems in Multi-Criteria RS

Step

2

Multi-Criteria RatingsStep 1 Step 1

Step 1. Learn from knowledge to predict multi-criteria ratingsStep 2. Aggregate multi-criteria ratings to predict the overall rating.

Linear Regression:

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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

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Criteria Chains

Assumptions in Criteria Chains

• Multi-criteria ratings can be viewed as contexts

• Ratings can be predicted in a chain

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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 RoomNext, take U3’s rating on room as contexts, User + Item + Room Check-inAgain, take previous predictions as contexts, User + Item + Room + Check-in ServiceThe prediction process works like a chain: Room Check-in Service

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Criteria Chains

The sequence of the chain matters

• Random Sequence

• Rank by Lower Prediction Errors

• Rank by Information Gain

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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

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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

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Experimental Results

The overall result demonstrates CCA and CCC outperform baselines, CCC is the best

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Experimental Results

Criteria Chains is able to improve the predictions on individual ratings on criterion

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Experimental Results

Information Gain is the best way to produce the optimal sequence

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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 ZhengIllinois Institute of Technology

Chicago, IL, 60616, USA

ACM Conference on Intelligent User InterfacesLimassol, Cyprus, March 13-16, 2017