We have seen a variety of Recommender Systems. But we left an important issue aside: How do we evaluate RecSys? Before answering that question per se, I want to make emphasys on something. Using just one error metric can give us a limited view of how these systems work. We should always try to evaluate with different methods our models, almost as picky as your ex, but prorizing quick iteration with the lowest cost possible.
Discover how to use Python--and some essential machine learning concepts--to build programs that can make recommendations. She helps you learn the concepts behind how recommendation systems work by taking you through a series of examples and exercises. Once you're familiar with the underlying concepts, Lillian explains how to apply statistical and machine learning methods to construct your own recommenders. She demonstrates how to build a popularity-based recommender using the Pandas library, how to recommend similar items based on correlation, and how to deploy various machine learning algorithms to make recommendations. At the end of the course, she shows how to evaluate which recommender performed the best.
The lights, the sounds, the joyful lap-dancing abound. Or so I've heard (I haven't actually been there). As they say, whatever happens in Vegas, stays in Vegas; we all know what happens in Vegas, but perhaps culinary experiences do not top that list. For the 5th project in Metis' Data Science Bootcamp, I decided to have a go at using Yelp's Kaggle Dataset to build a distance-based recommender system. In this project, I hypothesize food being an afterthought, that people are indecisive, constantly hangry and want to be told what restaurants are good within their immediate proximity while still taking into account personal preferences and visit histories.
Recommender systems are practically a necessity for keeping a site's content current, useful, and interesting to visitors. Recommender systems are everywhere, helping you find everything from movies to jobs, restaurants to hospitals, even romance. Practical Recommender Systems goes behind the curtain to show readers how recommender systems work and, more importantly, how to create and apply them for their site. This hands-on guide covers scaling problems and other issues they may encounter as their site grows.
This project continued to evolve and we explored the design space of a contextual software command recommender system and completed a six-week user study (Li et al. 2011). We then expanded the scope of our project by implementing CommunityCommands, a fully functional and deployable recommender system. During a one-year period, the recommender system was used by more than 1100 users. In this article, we discuss how our practical system architecture was designed to leverage Autodesk's existing customer involvement program (CIP) data to deliver in-product contextual recommendations to end users. We also present our system usage data and payoff, and provide an in-depth discussion of the challenges and design issues associated with developing and deploying the software command recommender system.