Paper #1 - Maximizing Store Revenues using Tabu Search for Floor Space Optimization
Key Notes
- Floor space is a valuable and scarce asset for retailers
- Connected multi-choice knapsack problem with an additional global
- constraint and propose a tabu search based metaheuristic that exploits the
- multiple special neighborhood structures
- Over the last decade, the number of products competing for limited space increased by up to 30%
- The product mix of categories, merchandising rules, sales patterns and characteristics of display furniture
- (1) develop a statistical model to measure the space elasticity; and
- (2) formulate and solve an optimization problem for each store to determine the optimal assignment of planograms to maximize total revenue subject to certain business constraints
Paer #2 - Reversing ShopView analysis for planogram creation
Key Notes
- ShopView can build the planogram without the need of manually creating it in software
- OCR in the identification of products
- Planograms specifies the absolute physical locations of the products, and the amount of space each type of product should occupy
- Planogram compliance using template images
- Vision - Object Recognition based on attributes, Template and Feature Matching, Optical Character Recognition (OCR)
- Custom Dictionary - Implementing a custom dictionary for the OCR engine seemed a good strategy since at first glance it would improve the performance of the OCR algorithm
Paper #3 - Deep Learning based Recommender System: A Survey and New Perspectives
Key Notes
- Collaborative €ltering makes recommendations by
- learning from user-item historical interactions, either explicit (e.g. user’s previous ratings) or implicit feedback (e.g. browsing history)
- Content-based recommendation is based primarily on comparisons across items’ and users
- Hybrid model refers to recommender system that integrates two or more types of recommendation strategies
- Strengths of deep learning based recommendation models - Nonlinear Transformation, Sequence Modelling
Paper #4 - Fashion Retail: Forecasting Demand for New Items
Key Notes
- Merchandising Factors - Discount, Visibility, Promotion
- Derived Features - Age of Style, Trend and Seasonality, Cannibalisation
Paper #5 - Time Series Forecasting With Deep Learning: A Survey
More Reads
- A Survey Paper on Recommender Systems
- A Comparative Analysis of RNN and SVM
- Accurate Demand Forecasting for Retails with Deep Neural Networks
- A Worrying Analysis of Probabilistic Time-series Models for Sales Forecasting
- Implementing Reinforcement Learning Algorithms in Retail Supply Chains with OpenAI Gym Toolkit
- Sales Demand Forecast in E-commerce using a Long Short-Term Memory Neural Network Methodology
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