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EuroPythonCode of ConductLive 📹

A Network Embeddings based Recommendation Model with multi-factor consideration

Liffey Hall 1
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30 minutes


Recommendation systems are increasingly in demand to provide a personalized customer experience for diversified product mix offerings. Traditionally we use interaction information based on user preferences and item characteristics. This brings in collaborative filtering-driven recommendations with higher accuracy and relevance. However, such a method has certain limitations in utilizing implicit information like cross-domain specific factors that are equally important for making personalized recommendations. We propose an improvised way of using network embeddings based matrix factorization technique with multi-factors to make a match between both implicit and explicit features resulting in more accurate recommendation.

TalkPyData: Machine Learning, Stats


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.

The speaker


Abhishek is a leading Innovation Specialist and a distinguished digital transformation leader who has worked across diversified domains retail, finance, logistics and enterprise technology. He has 13+ years of strong global business transformation experience in applied data science practice with a key focus on Artificial Intelligence, Machine Learning and NLP across various sectors. Currently working at Microsoft and delivered impactful outcomes by playing pivotal role in setting up AI CoEs while working for Maersk, Visa, Fidelity Investments and Dell. His active research interest reciprocated by 6 patents (US/ Denmark), 3 trade secrets and 5 international publications in top-peer reviews journals and conferences. He holds Master’s in industrial management & Engineering from Indian Institute of Technology Kanpur with specialization in Machine Learning & Natural Language Processing.

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