Bay Area Recommender Systems explores state of the art techniques in building recommender systems and educates newcomers. Targeted at data scientists, applied researchers and machine learning engineers, the group will feature top researchers and practitioners from the bay area and around the world. We will form a community that shares knowledge and enthusiasm about these amazing systems that drive the global economy: collaborative filters, content based filters and hybrid recommender systems.
New to recommender systems? You use them daily: Amazon product recommendations, Netflix movie recommendations, Facebook friend suggestions or LinkedIn's People You May Know. They drive commerce and aid discovery across markets around the globe.
What is a recommender system? Wikipedia says... "Recommender systems typically produce a list of recommendations in one of two ways – through collaborative filtering or through content-based filtering (also known as the personality-based approach). Collaborative filtering approaches build a model from a user's past behavior (items previously purchased or selected and/or numerical ratings given to those items) as well as similar decisions made by other users. This model is then used to predict items (or ratings for items) that the user may have an interest in. Content-based filtering approaches utilize a series of discrete characteristics of an item in order to recommend additional items with similar properties. These approaches are often combined (see Hybrid Recommender Systems)."