Publication Type
Conference Proceeding Article
Publication Date
12-2013
Abstract
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.
Keywords
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
Discipline
Databases and Information Systems | Numerical Analysis and Scientific Computing
Research Areas
Data Management and Analytics
Publication
IEEE 13th International Conference on Data Mining: ICDM 2013: Proceedings, 7-10 December 2013, Dallas, Texas
First Page
91
Last Page
100
ISBN
9780769551081
Identifier
10.1109/ICDM.2013.25
Publisher
IEEE Computer Society
City or Country
Los Alamitos, CA
Citation
CHUA, Freddy Chong-Tat; Oentaryo, Richard Jayadi; and LIM, Ee Peng.
Modeling Temporal Adoptions Using Dynamic Matrix Factorization. (2013). IEEE 13th International Conference on Data Mining: ICDM 2013: Proceedings, 7-10 December 2013, Dallas, Texas. 91-100. Research Collection School Of Information Systems.
Available at: http://ink.library.smu.edu.sg/sis_research/1974
Copyright Owner and License
Authors
Creative Commons License
This work is licensed under a Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License.
Additional URL
http://doi.ieeecomputersociety.org/10.1109/ICDM.2013.25
Included in
Databases and Information Systems Commons, Numerical Analysis and Scientific Computing Commons