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 Items Items 5 4 3 2 1 1 Users Users Rating matrix X Response matrix R Data model: probabilistic matrix factorization (θ=(U, V)) Response model: variants of soft assignment of Bernoulli distribution with parameters μ

A Unified Framework for Reputation Estimation in Online Rating Systems Motivation Online Rating Systems are easily manipulated by malicious users for various reasons. To promote sales of certain items To build up personal image

A Unified Framework for Reputation Estimation in Online Rating Systems Motivation Online Rating Systems are easily manipulated by malicious users for various reasons. To promote sales of certain items To build up personal image We propose a user reputation estimation framework Three ingredients of the framework Prediction Model ℋ Given a CF model ℋ that can predict entries of 𝑅, assume 𝑟 𝑖𝑗 ~𝒩 ℋ 𝑖,𝑗 , 𝜎 2 The log-likelihood of observing 𝑟 𝑖𝑗 is ℒ 𝑖𝑗 =𝐶− 1 2 𝜎 2 𝑟 𝑖𝑗 −ℋ 𝑖,𝑗 2 The unexpectedness is 𝑠 𝑖𝑗 = 𝑟 𝑖𝑗 −ℋ 𝑖,𝑗 2 Penalty Function Summarize the unexpectedness { 𝑠 𝑖𝑗 } into 𝑠 𝑖 or 𝑠 𝑗 Link Function Link the 𝑠 𝑖 or 𝑠 𝑗 to 𝑐 𝑖 Model Prediction Model Penalty Function Link Function Mizzaro's algorithm ℋ 𝑖,𝑗 = 𝑖∈ 𝒰 𝑗 𝑐 𝑖 𝑟 𝑖𝑗 / 𝑖∈ 𝒰 𝑗 𝑐 𝑖 𝑠 𝑗 = 𝑖∈ 𝒰 𝑗 𝑐 𝑖 𝑐 𝑖 = 𝑗∈ ℐ 𝑖 𝑠 𝑗 (1− 𝑠 𝑖𝑗 / 𝑠 𝑚𝑎𝑥 𝑗∈ ℐ 𝑗 𝑠 𝑗 Laureti's algorithm Same as above 𝑠 𝑖 = 1 ℐ 𝑖 𝑗∈ ℐ 𝑖 𝑠 𝑖𝑗 𝑐 𝑖 = 𝑠 𝑖 +𝜖 −𝛽 De Kerchove's algorithm 𝑐 𝑖 =1−𝑘× 𝑠 𝑖 Li's L1-AVG ℋ 𝑖,𝑗 = 1 𝒰 𝑗 𝑖∈ 𝒰 𝑗 𝑟 𝑖𝑗 𝑐 𝑖 𝑠 𝑖 = 1 ℐ 𝑖 𝑗∈ ℐ 𝑖 𝑠 𝑖𝑗 𝑐 𝑖 =1−𝜆 𝑠 𝑖 Li's L2-AVG 𝑐 𝑖 =1− 𝜆 2 𝑠 𝑖 Li's L1-MAX 𝑠 𝑖 = max 𝑗∈ ℐ 𝑖 𝑠 𝑖𝑗 Li's L2-MAX 𝑠 𝑖 = max 𝑗∈ ℐ 𝑖 𝑠 𝑖𝑗 Li's L1-MIN 𝑠 𝑖 = min 𝑗∈ ℐ 𝑖 𝑠 𝑖𝑗 Li's L2-MIN 𝑠 𝑖 = min 𝑗∈ ℐ 𝑖 𝑠 𝑖𝑗

A Unified Framework for Reputation Estimation in Online Rating Systems We propose a user reputation estimation framework Previous methods assume an item has an intrinsic quality. Depending on the situation, taste view might be more suitable. Personalized prediction model PMF is used, which assumes a low-rank Matrix Factorization structure ℒ= 1 2 𝑖,𝑗,𝑟 ∈𝒬 𝑟− 𝑈 𝑖 𝑇 𝑉 𝑗 2 + 𝜆 𝑈 2 𝑈 + 𝜆 𝑉 2 𝑉 Penalty function is 𝑠 𝑖 = 1 ℐ 𝑖 𝑗∈ ℐ 𝑖 𝑠 𝑖𝑗 Link function is 𝑐 𝑖 =1 − 𝑠 𝑖 Intrinsic value view VS. Taste View