Abstract
One of the key tasks for recommender systems is the prediction of personalized sequential behavior. There are two primary means of modeling sequential patterns and long-term user preferences: Markov chains and matrix factorization, respectively. Together, they provide a unified approach to predicting user actions. In spite of their strengths in tackling dense data, however, these methods struggle with the sparsity issues often present in real-world datasets. In approaching this problem, we propose combining similarity-based methods (demonstrably helpful for sequentially unaware item recommendation) with Markov chains to offer individualized sequential recommendations. This approach, called GPS (a factorized group preference-based similarity model), further leverages the idea of group preference along with user preference to introduce a greater array of interactions between users—which in turn eases the problem of data sparsity and cold users and cuts down on the assumption of a strong independency within various factors. By applying our method to a range of large, real-world datasets, we demonstrate quantitatively that GPS outperforms several state-of-the-art methods, particularly in cases with sparse datasets. Regarding qualitative findings, GPS also grasps personalized interactions and can provide recommendations that are both on-target and meaningful.
| Original language | English |
|---|---|
| Pages (from-to) | 394-411 |
| Number of pages | 18 |
| Journal | Information Sciences |
| Volume | 481 |
| DOIs | |
| Publication status | Published - 2019 May 1 |
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Keywords
- Group preference
- Recommender systems
- Sequential recommendation
- Similarity models
ASJC Scopus subject areas
- Software
- Control and Systems Engineering
- Theoretical Computer Science
- Computer Science Applications
- Information Systems and Management
- Artificial Intelligence
Cite this
GPS : Factorized group preference-based similarity models for sparse sequential recommendation. / Yang, Yeongwook; Hooshyar, Danial; Lim, Heui Seok.
In: Information Sciences, Vol. 481, 01.05.2019, p. 394-411.Research output: Contribution to journal › Article
}
TY - JOUR
T1 - GPS
T2 - Factorized group preference-based similarity models for sparse sequential recommendation
AU - Yang, Yeongwook
AU - Hooshyar, Danial
AU - Lim, Heui Seok
PY - 2019/5/1
Y1 - 2019/5/1
N2 - One of the key tasks for recommender systems is the prediction of personalized sequential behavior. There are two primary means of modeling sequential patterns and long-term user preferences: Markov chains and matrix factorization, respectively. Together, they provide a unified approach to predicting user actions. In spite of their strengths in tackling dense data, however, these methods struggle with the sparsity issues often present in real-world datasets. In approaching this problem, we propose combining similarity-based methods (demonstrably helpful for sequentially unaware item recommendation) with Markov chains to offer individualized sequential recommendations. This approach, called GPS (a factorized group preference-based similarity model), further leverages the idea of group preference along with user preference to introduce a greater array of interactions between users—which in turn eases the problem of data sparsity and cold users and cuts down on the assumption of a strong independency within various factors. By applying our method to a range of large, real-world datasets, we demonstrate quantitatively that GPS outperforms several state-of-the-art methods, particularly in cases with sparse datasets. Regarding qualitative findings, GPS also grasps personalized interactions and can provide recommendations that are both on-target and meaningful.
AB - One of the key tasks for recommender systems is the prediction of personalized sequential behavior. There are two primary means of modeling sequential patterns and long-term user preferences: Markov chains and matrix factorization, respectively. Together, they provide a unified approach to predicting user actions. In spite of their strengths in tackling dense data, however, these methods struggle with the sparsity issues often present in real-world datasets. In approaching this problem, we propose combining similarity-based methods (demonstrably helpful for sequentially unaware item recommendation) with Markov chains to offer individualized sequential recommendations. This approach, called GPS (a factorized group preference-based similarity model), further leverages the idea of group preference along with user preference to introduce a greater array of interactions between users—which in turn eases the problem of data sparsity and cold users and cuts down on the assumption of a strong independency within various factors. By applying our method to a range of large, real-world datasets, we demonstrate quantitatively that GPS outperforms several state-of-the-art methods, particularly in cases with sparse datasets. Regarding qualitative findings, GPS also grasps personalized interactions and can provide recommendations that are both on-target and meaningful.
KW - Group preference
KW - Recommender systems
KW - Sequential recommendation
KW - Similarity models
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U2 - 10.1016/j.ins.2018.12.053
DO - 10.1016/j.ins.2018.12.053
M3 - Article
AN - SCOPUS:85059581276
VL - 481
SP - 394
EP - 411
JO - Information Sciences
JF - Information Sciences
SN - 0020-0255
ER -