Triple Factorization-Based SNLF Representation With Improved Momentum-Incorporated AGD: A Knowledge Transfer Approach

Abstract

Symmetric, high-dimensional and sparse (SHiDS) networks usually contain rich knowledge regarding various patterns. To adequately extract useful information from SHiDS networks, a novel biased triple factorization-based (TF) symmetric and non-negative latent factor (SNLF) model is put forward by utilizing the transfer learning (TL) method, namely biased TL-incorporated TF-SNLF (BT $^{2}$ -SNLF) model. The proposed BT $^{2}$ -SNLF model mainly includes the following four ideas: 1) the implicit knowledge of the auxiliary matrix in the ternary rating domain is transferred to the target matrix in the numerical rating domain, facilitating the feature extraction; 2) two linear bias vectors are considered into the objective function to discover the knowledge describing the individual entity-oriented effect; 3) an improved momentum-incorporated additive gradient descent algorithm is developed to speed up the model convergence as well as guarantee the non-negativity of target SHiDS networks; and 4) a rigorous proof is provided to show that, under the assumption that the objective function is $L$ -smooth and $\mu$ -convex, when $t\geq t_{0}$ , the algorithm begins to descend and it can find an $\epsilon$ -solution within $O(ln((1+\frac{\mu L}{L(1+\mu )+8\mu })/\epsilon ))$ . Experimental results on six datasets from real applications demonstrate the effectiveness of our proposed T $^{2}$ -SNLF and BT $^{2}$ -SNLF models.