Triple Factorization-Based SNLF Representation With Improved Momentum-Incorporated AGD: A Knowledge Transfer Approach
Ming Li, Yan Song, Derui Ding, Ran Sun · IEEE Transactions on Knowledge and Data Engineering · 2024
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.