EASE Em: Hybrid Recommendation System for Item Cold Start Problem
S. Aditya, Mihir Rajora, Indu B Singh · 2021 International Conference on Smart Generation Computing, Communication and Networking (SMART GENCON) · 2021
Recommender Systems play a very important role in online services nowadays, with the huge repository of content being generated every day. While these systems are used on large scales, they still face a lot of problems. The item cold start is one of the most famous problems in recommender systems. While systems can develop an initial image of new user profiles through various methods, new items face a problem surfacing into user recommendations. This is because they have low to almost zero interactions, and most recommender systems use methods like collaborative filtering to share similar items to similar user profiles. This is an important problem to be dealt with. Otherwise, recommendation feeds get saturated with already popular content, which harms new creators on platforms that might be creating innovative content. We propose a hybrid recommender system: EASE’Em, that seeks to solve this problem by generating cold start items to users based on the item similarities and user profiles. Our model first uses an AutoEncoder recommendation system to fill a sparse user interaction matrix to develop a better understanding of user profiles. We then project item attributes and user profiles into a common low dimensional space and then use matrix factorization techniques to generate top-N recommendations for each user present in the set. We then compare our model generations with standard item-based recommender models on a subset of the Million Song Dataset and compare their results.