A Game-Incorporated Alternative Least Squares-Based Approach to Latent Factor Analysis
Autor: | Rui Che, Ye Yuan |
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Rok vydání: | 2021 |
Předmět: |
0209 industrial biotechnology
Mathematical optimization Sequential game Computer science 02 engineering and technology Recommender system Missing data Least squares Matrix (mathematics) 020901 industrial engineering & automation Dimension (vector space) 020204 information systems Backward induction 0202 electrical engineering electronic engineering information engineering Point (geometry) |
Zdroj: | 2021 IEEE 6th International Conference on Big Data Analytics (ICBDA). |
Popis: | A latent factor analysis (LFA) model is highly efficient in a high-dimensional and sparse (HiDS) matrix from a big data-related application like a recommender system (RS). Alternating least squares (ALS) is a frequently-adopted learning algorithm when building an LFA model. However, the computational cost of an ALS model is cubic with the dimension of the latent factor (LF) space, which yields the dilemma that large LF leads to high computational cost while small LF dimension results in accuracy loss. From this point of view, this paper carefully investigates the connections between a dynamic game method and an ALS algorithm, and find that the ALS-based training process is actually playing game among LFs related to the same entity. From this point of view, this paper proposes a game-incorporated alternating least squares (GALS) algorithm for building an LFA model efficiently. Its main idea is two-fold, a) considering LFs corresponding to the same entity as players and their learning objectives as strategies, and b) making these players choose strategies to achieve highest gains based on a single LF-dependent ALS algorithm following the principle of Backward Induction widely accepted in dynamic game methods. Experimental results indicate that with a GALS algorithm, an LFA model is able to decrease its computational cost as well as maintain its prediction accuracy for missing data of an HiDS matrix. |
Databáze: | OpenAIRE |
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