Unsolicited Recommendations: Tool to keep audience engaged

Unsolicited Recommendations: Tool to keep audience engaged

Domain: E-Commerce

In recent years, we all have seen an explosion in the ways that consumer e-commerce brands function. Most of them are using big data and AI for their advantage by leveraging user experience. Alexa, Siri, chatbots, drip-emails, and category recommendations are just some examples of how the brands have changed the buying patterns and behaviours. It would not be wrong to call it the tip of an AI ice-berg. This is how Artificial Intelligence development is bringing in the change in our everyday lifestyle.

ecommerce-case-study

E-commerce future technology

Today, the development of artificial intelligence has made the world a profit-driven global enterprise where sales need to happen regardless of what time of the day it is. Businesses need to be available to the customers at all hours of the day. In the sector of e-commerce, AI development can be helpful for the brands to achieve this objective by optimising their e-commerce framework. It helps companies to gather and investigate data in real-time, thereby facilitating more efficiency and competence in the business. This, in turn, has led to creating a more personalised experience by identifying different patterns of consumer behaviour.

Staying ahead of the time, and acing at what we do, here is the story of how we incorporated AI and machine learning application in an e-commerce framework.

Problem Statement:

Improve ‘In-Category’ and ‘Cross-Category’ product recommendation on E-Commerce websites across all categories using Artificial Intelligence

Definitions:

In-Category: Similar set of items (degree of similarity may vary)

Cross-Category: Items that complement each other

Technology and Situation Overview:

A recommendation is a type of information filtering system that uses AI algorithms to provide the most relevant items, and/or content to a user. These engines can incorporate a variety of data, including user purchase behaviour, user browsing behaviour, user demographics, and real-time triggers.

With the dearth of goods available for one to choose, customers usually tend to lose out on goods they have previously viewed or liked. In-app recommendations enable them to be spoilt for choice. Built with customer behaviour analysis at its core, the recommendations can keep them hooked enabling to view products that they might be interested in.

Why should we solve this problem?

Recommendation engines can help e-commerce players increase engagement, lift conversion rates, and improve revenue.

How did we solve this problem?

For In-Category recommendations, we used the client’s inventory data-set. Upon mapping and fragmenting the styles we developed a model using the below approach:

Trained a ResNet50 architecture network on Client’s Inventory Dataset using transfer learning. Generated cloth embeddings by doing a forward pass on trained ResNet50 keras and collected the embedding from the preceding connected layer using our client’s inventory Dataset, and later stored in an annoy file.

During real-time In-Category recommendation, the application took the image captured by the customer and passed it through the Resnet50 keras and collected the embedding for the same. Generated the In-Category recommendation using nearest cosine similarity measures over the Annoy Embedding file.

For Cross-Category recommendations, we collaborated with the client’s fashion Stylist to generate the fashion-sense dataset, and the model was build using the below approach:

Trained a multi-layered Bi-Lstm RNN network using the embedding file of the fashion-sense dataset to make the network learn about fashion sense.

During the real-time Cross-Category recommendation, the application took image captured by the customer and passed it through the Bi-Lstm RNN and generated the embedding of products of various categories which are related by networks fashion sense.

What technologies did we use?

  • Python
  • Matplotlib
  • Pytorch
  • Flask
  • Cv2
  • Google Cloud

What was the business Impact:

  • Enhanced Product Recommend System using AI development improved In-Category and Cross-Category Sales
  • Exclusive brand websites can enjoy the recommendation solutions which big e-commerce players regularly use
  • Algorithms are applicable for both web and mobile
  • Creating a brand-persona by acting as a virtual stylist by recommending the best fashion
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