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This Halloween, we’re unmasking the authors behind amazing Manning books! We’re continuing our series of under-the-mask interviews with Kim Falk, author of AI Recommender Systems!
What are you excited about right now in the tech world?
I’m excited that environmental cost is starting to be treated as a metric in recommender systems.
Training large models is energy-intensive, and serving them is, too. More broadly, it’s an interesting time for recommenders. When I wrote my first book Practical Recommender Systems, the recsys universe was much simpler. Now, beyond matrix factorization and content-based embeddings, the field includes sequential transformers, generative recommenders that predict semantic IDs the way language models predict words, and agents that call recommenders as tools. I also think the recommenders will shift from predicting clicks to making decisions under uncertainty, using off-policy evaluation and optimization.
What advice would you give to a younger you?
At this year’s RecSys conference, we saw an explosion of complex models. Instead of using a single LLM, people now stack several. Yet we still haven’t agreed on how to evaluate recommender systems, so what does an improved offline metric actually mean in production? In my experience, simpler models can solve most use cases. That’s why every chapter in my book asks whether the added complexity is needed and when it’s worth putting it into production.
Make one prediction.
In a few years, most people will get most of their recommendations through conversations with agents rather than from a feed or a website. That worries me: a single agent will decide what you see, and you won’t know what it left out. Diversity, serendipity, and avoiding self-reinforcing feedback loops will be more important than ever. But under the hood, it will still be retrieve, filter, score, and rank. The interface changes faster than the fundamentals, which is why the book teaches the fundamentals and then shows how new methods plug into them.
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