#1: Practical Recommender Systems with Kim Falk
Recsperts - Recommender Systems Experts
English - October 08, 2021 12:02 - 1 hour - 73 MB - ★★★★★ - 1 ratingTechnology Science Mathematics recommender systems machine learning artificial intelligence personalization search data science information retrieval Homepage Download Apple Podcasts Google Podcasts Overcast Castro Pocket Casts RSS feed
In this first interview we talk to Kim Falk, Senior Data Scientist, multiple RecSys Industry Chair and author of the book "Practical Recommender Systems". We introduce into recommenders from a practical perspective discussing the fundamental difference between content-based and collaborative filtering as well as the cold-start problem - no mathematical deep-dive yet, but expect it to follow. In addition, we reason what constitutes good recommendations and briefly touch on a couple of ways of finding that out.
Looking a bit into the history of the recommender systems community, we touch on the Netflix Prize that was running from 2006 to 2009 as well as on the RecSys - the leading conference in recommender systems, where we also met for the first time.
In the end, we discuss a couple of challenges the field faces, in particular associated with approaches based on deep learning. Besides that, Spiderman will accompany our conversation at certain times. Plus many practical recommendations included on how to get started. Stay tuned!
Links from this Episode:
Kim Falk on LinkedIn and TwitterBook: Practical Recommender Systems (Manning) (get 37% discount with the code podrecsperts37 during checkout)GitHub Repository for PRS BookACM Conference on Recommender Systems 2021 (Amsterdam)Recommender Systems Specialization at CourseraAmazon.com Recommendations: Item-to-Item Collaborative FilteringNetflix PrizeNetflix Prize dataset on KaggleNew York Times: A $1 Million Research Bargain for Netflix, and Maybe a Model for OthersEvaluation Measures for Information RetrievalPaper by Dacrema et al. (2019): Are We Really Making Much Progress? A Worrying Analysis of Recent Neural Recommendation Approaches (best paper award at RecSys 2019)Recommending music on Spotify with Deep LearningMovieLens RecommendersGeneral Links:
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