Recommender Systems

Abstract

Recommender systems are a hot topic in this age of immense data and web marketing. Shopping online is ubiquitous, but online stores, while eminently searchable, lack the same browsing options as the brick-and-mortar variety. Visiting a movie rental store in person, a customer can wander over to the science fiction section and casually look around without a particular author or title in mind. Online stores often offer a browsing option, and even allow browsing by genre, but often the number of options available is still overwhelming. Commercial sites try to counteract this overload by showing special deals, new options, and staff favorites, but the best marketing angle would be to recommend items that the user is likely to enjoy or need. Unless online stores want to hire psychics, they need a new technology. The field of machine learning has an ever-growing field of research in recommender systems, which fits the bill. “Recommender systems are systems that based on information about a user's past patterns and consumption patterns in general, recommend new items to the user.“ The research in this scope has led to the development of many methods to get through the opinion of other people, the relevant items for a specific person. Most of these methods work around the idea of finding similarities in people’s tastes, using Social Network platforms, such as Facebook and Twitter. The prediction for a specific person is then based on the opinion of the most similar user to the person present in the network. This procedure is known as Collaborative Filtering. The other approach is Content-based Filtering. But one approach isn’t enough in today’s time when internet access is easy, social network usage is high and there is a huge library of media content and inventory lists. A Hybrid Recommender System is our best bet to tackle the issue of suggestions. The idea of this project is to analyze different algorithms devised for making predictions and develop a system for recommending media content to the user according to his/her taste.