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Recommender Systems simply put, are AI algorithms that utilize features from a reviewed, liked or a purchased product to suggest additional products to consumers. These recommendations can be based on factors such as past purchases, demographic info, their search history, time spent reviewing the product or a like, dislike or a comment left behind by these consumers.
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The idea of Recommender Systems is that if you can narrow down the pool of selection options for your customers to a few meaningful and relevant choices, they are more likely to make a purchase now, as well as come back for more down the road.
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This research is a part of the Honors Research Program conducted by Stevens Institute of Technology and I would like to express my sincere gratitude to Professor Hong Man for being my mentor and guiding me through this project.
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Through the research topic, I hope to explore about how organizations such as Spotify, Netflix and YouTube leverage Recommender Systems to enjoy a high user share, as well as learn about the essence of approaches such as Natural Language Processing, Neural Networks and LSTM in optimizing user recommendations, making them more relevant and meaningful.