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DESCRIPTION
Response Aware Model-Based Collaborative Filtering Motivation Rating data contain explicit scores and implicit response patterns The rated items are not randomly selected Rating matrix X User selected items Randomly selected items Items Users Data model: probabilistic matrix factorization ( θ=(U, V) )TRANSCRIPT
Poster Spotlights
Conference on Uncertainty in Artificial IntelligenceCatalina Island, United StatesAugust 15-17, 2012
Session: Wed. 15 August 2012, 15:10 - 15:30 pmID: 211
Response Aware Model-Based Collaborative Filtering
Guang Ling1, Haiqin Yang1, Michael R. Lyu1, Irwin King1,2
1The Chinese University of Hong Kong2AT&T Labs Research, San Francisco
Response Aware Model-Based Collaborative Filtering
Motivation Rating data contain explicit scores and implicit response patterns
Rating matrix XUser selected items
Items
Use
rs
Data model: probabilistic matrix factorization (θ=(U, V))
5 45 3
3 21 5
2 4
Response Aware Model-Based Collaborative Filtering
Motivation Rating data contain explicit scores and implicit response patterns The rated items are not randomly selected
Rating matrix XUser selected items Randomly selected items
Items
Use
rs
Data model: probabilistic matrix factorization (θ=(U, V))
5 45 3
3 21 5
2 4
Response Aware Model-Based Collaborative Filtering
Motivation Rating data contain explicit scores and implicit response patterns The rated items are not randomly selected
Goal: How to integrate users’ response patterns into a successful matrix factorization, Probabilistic Matrix Factorization, to avoid bias parameter estimation
Rating matrix XUser selected items Randomly selected items
Items
Use
rs
Data model: probabilistic matrix factorization (θ=(U, V))
5 45 3
3 21 5
2 4
Response Aware Model-Based Collaborative Filtering
Motivation Rating data contain explicit scores and implicit response patterns The rated items are not randomly selected
Goal: How to integrate users’ response patterns into a successful matrix factorization, Probabilistic Matrix Factorization, to avoid bias parameter estimation
Rating matrix XUser selected items Randomly selected items
1 1 0 0 00 1 0 1 01 0 0 1 01 0 1 0 00 1 0 0 1
Items
Use
rs
Items
Use
rs
Response matrix R
Data model: probabilistic matrix factorization (θ=(U, V))
Response model: variants of soft assignment of Bernoulli distribution with parameters μ
5 45 3
3 21 5
2 4
Response Aware Model-Based Collaborative Filtering
Motivation Rating data contain explicit scores and implicit response patterns The rated items are not randomly selected
Goal: How to integrate users’ response patterns into a successful matrix factorization, Probabilistic Matrix Factorization, to avoid bias parameter estimation
Experiments Three recommender protocols Synthetic and Yahoo! datasets RAPMF performs better on randomly selected items
Rating matrix XUser selected items Randomly selected items
1 1 0 0 00 1 0 1 01 0 0 1 01 0 1 0 00 1 0 0 1
Items
Use
rs
Items
Use
rs
Response matrix R
Synthetic dataset Yahoo! dataset
Data model: probabilistic matrix factorization (θ=(U, V))
Response model: variants of soft assignment of Bernoulli distribution with parameters μ
5 45 3
3 21 5
2 4