The accepted abstract for my lightning talk at the AltRecSys workshop at RecSys2024 in Bari. (the image was not part of the submission) Almost any news source, whether a regular newspaper, LinkedIn, or similar, will tell you these days that Continue reading
Harald Stecks’s paper Is cosine similarity of embeddings really about similarity [1] states and mathematically proves that cosine similarity (CS) doesn’t always make sense when calculating similarity in recommender systems. For example, it might not work if normalization is performed incorrectly Continue reading
As an experienced reviewer of recommender systems articles, I have had the privilege of evaluating submissions for numerous large conferences, primarily on the industry track but also on the research track. A rarely discussed barrier to getting your article accepted Continue reading
LLMs are great and can do mind-boggling things with their language comprehension capabilities. They have generative abilities that make them seem like oracles, but please caution yourself because they are not. Stuffing an LLM into a recommender system does not Continue reading
Does a solution have to contain machine learning to be good or to tap into the voice of the many do we need to have an LLM? In many cases, the answer might be no, for sure not as Continue reading
January is the time when all the data science experiments have been ready for the last two months but have been delayed because of the code freeze of Christmas and maybe even Cyber weekend. As a data scientist, this is Continue reading
Happy New Year, everyone, Welcome to January, the month where most behavioural-based e-commerce recommender systems struggle. Hopefully, December was full of lots of transactions, so the system has a lot of good-quality data. But most customers don t have or want Continue reading
When you train machine learning models, you often have to randomize the data so that the model doesn t learn unintended patterns from the order of training data. In recommender systems, the same concern could be stated. Still, suppose you randomize Continue reading
Without turning this into a play on words, it s interesting that personalization is very personal. The more people I talk to about what personalization is, the more answers I get. This is one of these funny paradoxes because, basically, in Continue reading
A RAG framework contains an LLM paired with a knowledge base. A RAG process takes a query and assesses if it relates to subjects defined in the paired knowledge base. If yes, it searches its knowledge base to extract information Continue reading