Modeling Temporal Adoptions Using Dynamic Matrix Factorization

Modeling Temporal Adoptions Using Dynamic Matrix Factorization

The problem of recommending items to users is relevant to many applications and the problem has often been solved using methods developed from Collaborative Filtering (CF). Collaborative Filtering model-based methods such as Matrix Factorization have been shown to produce good results for static rat...

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Bibliographic Details
Main Authors: CHUA, Freddy Chong-Tat, Oentaryo, Richard Jayadi, LIM, Ee Peng
Format: text
Language:English
Published: Institutional Knowledge at Singapore Management University 2013
Subjects:
Collaborative filtering
Matrix decomposition
Recommender systems
Data models
Vectors
Kalman filters
Predictive models
Mathematical model
Heuristic algorithms
Probabilistic logic
Dynamic Matrix Factorization
Kalman Filter
Linear Dynamical Systems
State Space Models
Databases and Information Systems
Numerical Analysis and Scientific Computing
Online Access:https://ink.library.smu.edu.sg/sis_research/1974
https://ink.library.smu.edu.sg/context/sis_research/article/2973/viewcontent/C84___Modeling_Temporal_Adoptions_Using_Dynamic_Matrix_Factorization__ICDM2013_.pdf
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Institution: Singapore Management University
Language: English
Description
Summary:The problem of recommending items to users is relevant to many applications and the problem has often been solved using methods developed from Collaborative Filtering (CF). Collaborative Filtering model-based methods such as Matrix Factorization have been shown to produce good results for static rating-type data, but have not been applied to time-stamped item adoption data. In this paper, we adopted a Dynamic Matrix Factorization (DMF) technique to derive different temporal factorization models that can predict missing adoptions at different time steps in the users' adoption history. This DMF technique is an extension of the Non-negative Matrix Factorization (NMF) based on the well-known class of models called Linear Dynamical Systems (LDS). By evaluating our proposed models against NMF and TimeSVD++ on two real datasets extracted from ACM Digital Library and DBLP, we show empirically that DMF can predict adoptions more accurately than the NMF for several prediction tasks as well as outperforming TimeSVD++ in some of the prediction tasks. We further illustrate the ability of DMF to discover evolving research interests for a few author examples.

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