Recommendation in home improvement industry, challenges and opportunities

Abstract

Retail industry has been disrupted by the e-commerce revolution more than any other industry. Some giant retailers went out of business or filed for bankruptcy as a result of that like Sears and Toys R Us. However, some verticals in the retail industry are still robust and not been disrupted due to the lack of e-commerce solutions that convinced customers to turn their back to the existing physical stores in favor of the online experience. Home improvement is the best example of such vertical where e-commerce has not "yet" disrupted the domain and caused problems to the leading companies which still rely heavily on physical stores. That being said, home improvement retailers recognized the risk of not investing in building a robust online business that support their physical stores in a seamless experience so most of the leading retailers in this hundred-billion-dollar industry started building their in-house solutions for all the challenging problems to give their shoppers a seamless experience when they shop online. Recommender systems playing crucial role in this industry like any other online retailers. Therefore, it is very important to invest in building personalized, scalable, and reliable recommender system that proactively help shoppers discover products that engage them and match their intent and interest while on the website then reengage them with products and content that align with their interest after they leave the website via email or social media. As a Sr. Manager of Core Recommendations team at The Home Depot which is the largest home improvement retailer in the world, I deal with the challenges of building such recommender system utilizing the cutting-edge technologies in AI, machine learning, and data science. In this talk I would like to discuss and highlight the following challenges in the recommendations for home improvement: (1) Project-based recommendations: One of the unique aspects on home improvement retail is project-based shopping. Most of the visitors of home improvement retails are classified as "Do It Yourself" where those customers who are non-home improvement professionals, but they are interested in building or fixing something in their home themselves. For those customers they prefer to go to the physical store most of the time, so they can talk to a store associate about their project and get the associate help in getting the needed tools and materials for their project. It is very challenging to build similar experience online so I will talk about what we have done at Home Depot to build a project-based recommendation utilizing multi-modal learning to achieve that goal. (2) Item Related Groups (IRG): One of the most important recommendations on the home improvement portals is the Item Related Groups (IRG) which includes accessories (water filter is an accessory for a fridge), collections (faucet has shower head, towel bar, and towel ring which match the style as collection), and Parts (handler of a drawer). The challenges in recommending those different IRG vary from visual compatibility to functionality understanding. I will discuss how we are leveraging computer vision, Deep Learning, NLP, NLU, and domain knowledge to tackle these problems and generate high quality IRG recommendations. I will also cover in this talk the other challenges that face recommender systems in home improvement industry like the velocity of changing interest and intent and the sparsity of interactions between customers and products.