This approach tends to teach to machines what humans like to listen to, without understanding what is recommended. It is a deaf approach that's trying to mimic the record dealer's behavior. It's not a DJ that builds a listening experience. It doesn't capture what the soundtrack of your life is. Collaborative filtering also tends to make predictable and familiar recommendations.
Building a simple but powerful recommendation system is much easier than you think. This guide explains innovations that make machine learning practical for business production settingsand demonstrates how even a small-scale development team can design an effective large-scale recommender. In this guide, Practical Machine Learning: Innovations in Recommendation, authors and Mahout committers Ted Dunning and Ellen Friedman shed light on a more approachable recommendation engine design and the business advantages for leveraging this innovative implementation style.
One pressing issue of product recommendation systems today is the scalability of algorithms with large, real-world datasets. It's possible that a recommendation algorithm will work well and produce accurate results with small datasets, yet may start producing inaccurate or inefficient results with large ones. In addition, some algorithms are computationally expensive to run – the larger the dataset, the longer it will take, and the more it will cost the business to analyse and make recommendations from it. Advanced, large-scale assessment methods are required to deal with both issues.
Reading groups domain is a new domain for group recommenders. In this paper we propose a web based group recommender system which is called BoRGo: Book Recommender for Reading Groups, for reading groups domain. BoRGo uses a new information filtering technique which uses the difference between positive and negative feedbacks about a feature of a user profile and also presents an interface for after recommendation processes like achieving a consensus on the reading list.