Kinda sad when there is a huge literature on sequential recommenders and people can't be bothered to read it. On the other hand, maybe that's an American thing. I'm kinda shocked when I read arXiv papers and come to the conclusion that all the interesting work is going on in India and China and the U.S. looks like a backwater. (e.g. many of the problems such as "plays the same song over and over again" and "gets stuc…
So much this. I know their point is to show what these models can do, but it just one more example of people shoehorning LLMs in, instead of finding the right tool for the job or caring about performance. They could have even layered AI on top of a recommender.
... real research in sequential recommender uses transformer models in a few different ways, including fine-tuned LLMs. There is a lot more to using LLMs than "write a prompt for a blisteringly expensive frontier model" but if you looked at what people post to HN you wouldn't know. (Hint: the "prompt engineers" will enjoy being poor, the people who know a little more might keep their jobs a little longer)
Overall it is depressing that poorly done demos get so much play. LinkedIn is flooded with AI slop and slop posts about AI and it's just so awful to see an image that ranks the top 20 books in some order but thinks a Harry Potter book has a cover that looks like a https://en.wikipedia.org/wiki/Ukiyo-e print and uses the cover of The Hobbit for a different book. The idiots who prompt this slop don't notice or don't care and neither do the 50 people who upvoted it and worse if they are Grok fans they think it is better the worse it is, like they'd think the best calculator app only gives 69 and 420 as answers.