Online-updating regularized kernel matrix factorization models for large-scale recommender systems

Abstract

Regularized matrix factorization models are known to generate high quality rating predictions for recommender systems. One of the major drawbacks of matrix factorization is that once computed, the model is static. For real-world applications dynamic updating a model is one of the most important tasks. Especially when ratings on new users or new items come in, updating the feature matrices is crucial.In this paper, we generalize regularized matrix factorization (RMF) to regularized kernel matrix factorization (RKMF). Kernels provide a flexible method for deriving new matrix factorization methods. Furthermore with kernels nonlinear interactions between feature vectors are possible. We propose a generic method for learning RKMF models. From this method we derive an online-update algorithm for RKMF models that allows to solve the new-user/ new-item problem. Our evaluation indicates that our proposed online-update methods are accurate in approximating a full retrain of a RKMF model while the runtime of online-updating is in the range of milliseconds even for huge datasets like Netflix.

Implementasi Dan Analisis Online – Updating Regularization Kernel Matrix Factorization Model Pada Sistem Rekomendasi

“…Faktorisasi Matriks merupakan salah satu metode yang tangguh untuk menghasilkan rekomendasi jika dibandingkan dengan metode k-NN klasik [1]. Pada Faktorisasi Matriks, data User dan Item dibentuk ke dalam sebuah bentuk matriks R yang kemudian mengalami fase training untuk menghasilkan sebuah rekomendasi.…”

“…[2] Nilai Interaksi User-Item pada MF dinyatakan dalam perhitungan dot product dari setiap user dan item, Sebagai berikut [1] …”

“…Beberapa metode kernel yang populer pada Kernel Matrix Factorization adalah sebagai berikut [1]: ( , ℎ ) = ( , ℎ ) (8) ( , ℎ ) = 1 + ( , ℎ ) (9) ( , ℎ ) = − || − ℎ || 2 (10) ( , ℎ ) = , + ( , ℎ ) (11)…”

Factorization Techniques for Predicting Student Performance

“…In this work, we make use of matrix factorization (Rendle and Schmidt-Thieme, 2008;Koren et al, 2009), which is known to be one of the most successful methods for rating prediction, outperforming other state-of-the-art methods (Bell and Koren, 2007) and tensor factorization (Kolda and Bader, 2009;Dunlavy et al, 2010) to take into account the sequential effect.…”

Matrix and Tensor Factorization for Predicting Student Performance

“…In this work, we make use of matrix factorization (Rendle and Schmidt-Thieme, 2008;Koren et al, 2009), which is known to be one of the most successful methods for rating prediction, outperforming other state-of-the-art methods (Bell and Koren, 2007) and tensor factorization (Kolda and Bader, 2009;Dunlavy et al, 2010) to take into account the sequential effect. We will briefly describe these techniques in the following subsections.…”