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

Copyright Owner and License

Authors

Creative Commons License

Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 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

Share

COinS