EuroPython 2022 - A Network Embeddings based Recommendation Model with multi-factor consideration - presented by ABHISHEK [Liffey Hall 1 on 2022-07-14] The method consists of three main steps: First, network embedding formulation performed on each user specific behavior network; Then, embeddings weight distribution estimated through intermediate layers of network with final layer for target (item purchased as labels); Finally, both factors: (a) Learned weights from implicit data (cross-domain) and (b) explicit factors from domain data used by multi-factorization method for recommendations. The proposed method transfers knowledge across implicit and explicit factors and associated dimensions. The suggested approach tested real-world data with evidence of outperforming existing algorithms with significant lift in recommendation accuracy. Empirical experimentation outcomes illustrate the potential of both factors for making effective recommendations. This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License http://creativecommons.org/licenses/by-nc-sa/4.0/