1. Background
Today’s world is a small, deeply interconnected web. The internet has seemingly infinite potential, and in the post COVID world, the effect of the internet on our lives will just increase. Businesses are developing advanced solutions in e-commerce space to win over clients and one common focus area is personalized recommendations. Recommendation systems enable businesses to maximize their ROI based on the information they can gather on each customer’s preferences and purchases.
While building the recommendation system it is very common to use a traditional collaborative filtering algorithm such as item-item and user-item filtering. Basically, these algorithms are item-based in the sense that they analyse item-item relations to produce item similarities and focused on learning similarities between users and items simultaneously to provide recommendations. What if the user or item information is not available, how to build a more robust recommendation system? This led us to try to discover techniques from deep learning that can be used to overcome this limitation.
We want to focus on an extension of the Word2Vec model, called Item2Vec for item-based Collaborative Filtering that produces embedding for items in a latent space. The method is capable of inferring item to item (items might refer to sequences or words) relations even when user/item information is not available, which basically means it learns the item similarities by embedding items in a low dimensional space regardless of the users.
2. Problem Statement
The task at hand is thus: Build a single item-based recommender system (Model) for an online website or a mobile app. More specifically we would be focussing on the below
- To show the recommendation in the context of explicit user interest in a specific item and in the context of an explicit user intent to purchase (This helps to obtain a larger share of sales by higher Click-Through Rates)
- Compare the results with traditional collaborative filtering algorithms.
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