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The 13th International Conference on Knowledge Discovery and Data Mining

Predictive Discrete Latent Factor Models for Large Scale Dyadic Data

author: Deepak Agarwal, Yahoo Research

Description

We propose a novel statistical method to predict large scale dyadic response variables in the presence of covariate information. Our approach simultaneously incorporates the effect of covariates and estimates local structure that is induced by interactions among the dyads through a discrete latent factor model. The discovered latent factors provide a predictive model that is both accurate and interpretable.

We illustrate our method by working in a framework of generalized linear models, which include commonly used regression techniques like linear regression, logistic regression and Poisson regression as special cases.

We also provide scalable generalized EM-based algorithms for model fitting using both "hard" and "soft" cluster assignments. We demonstrate the generality and efficacy of our approach through large scale simulation studies and analysis of datasets obtained from certain real-world movie recommendation and internet advertising applications.

Categories

Top: Computer Science: Machine Learning: Clustering

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*Slides*
0:03	Predictive Discrete Latent Factor Models
0:37	Internet Advertising: Billion Dollar Industry
1:26	Recommender Systems
1:57	Click Fraud
2:39	Data
4:01	Problem Definition
5:15	Agenda
5:50	Existing Approaches
6:03	Non-Parametric Function Estimation
7:28	Random Effects Model
9:25	Generalized Linear Models
10:30	Unsupervised Approach
11:09	Clustering Animation
11:45	PDLF: High Level Overview
12:42	Model
13:15	Fitting Algorithm: Generalized EM
13:53	EM Algorithm Hard Clustering
14:04	Simulation Study on Movie Lens
14:14	Logistic Regression on Movie Lens
14:27	Experiments: Click Count Data
15:02	Co-Cluster Interactions: Plain Co-Clustering
15:49	Co-Cluster Interactions: PDLF
16:35	Prediction Results
16:51	Summary
17:36	Ongoing Work
18:30	Prediction Results (a)

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