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Improving recommendation systems and search in the age of LLMs

eugeneyan.com

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Re: Improving recommendation systems and search in the age of LLMs

#61

Post by curious_cat_163 »

novia wrote: post #59

I started listening to this article (using a text to speech model) shortly after waking up. I thought it was very heavy on jargon. Like, it was written in a way that makes the author appear very intelligent without necessarily effectively conveying information to the audience. This is something that I've often seen authors do in academic papers, and my one published research paper (not first author) is no exception.…

To me, it reads like a survey paper intended for (and maybe written by) a researcher about to start a new project. I am not a researcher in this space but I have dabbled elsewhere, so it is somewhat accessible. The degree to which one leverages existing jargon in their writing is a choice, of course.

I am curious -- what would have made it more effective at conveying information to you? Different people learn differently but I wonder how people get beyond the hurdles of jargon.

Re: Improving recommendation systems and search in the age of LLMs

#62

Post by Melatonic »

appleorchard46 wrote: post #52
UrineSqueegee wrote: post #49

Earlier quoted context omitted.

I have the exact opposite experience, recently when a playlist I have is over, I find that every recommended track that plays after, I love so much I end up putting in my playlist

I liked when you could make a playlist radio and do that manually. That's been removed now of course.

On desktop I believe you can still take any of your playlists and tell it to generate a "similar" playlist. Works really well.

Re: Improving recommendation systems and search in the age of LLMs

#63

Post by novia »

curious_cat_163 wrote: post #61
novia wrote: post #59

I started listening to this article (using a text to speech model) shortly after waking up. I thought it was very heavy on jargon. Like, it was written in a way that makes the author appear very intelligent without necessarily effectively conveying information to the audience. This is something that I've often seen authors do in academic papers, and my one published research paper (not first author) is no exception.…

To me, it reads like a survey paper intended for (and maybe written by) a researcher about to start a new project. I am not a researcher in this space but I have dabbled elsewhere, so it is somewhat accessible. The degree to which one leverages existing jargon in their writing is a choice, of course. I am curious -- what would have made it more effective at conveying information to you? Different people learn differe…

Yeah I'm not sure if it's just me and my learning style or if researchers purposefully use terminology that's obstructive to understanding to maintain walled gardens. I don't think my reading comprehension level is particularly low!

Usually the best way to learn about things like this for me is to see some actual code or to write things myself, but the lack of coding examples in the text isn't the thing that I find troubling. I don't know, it's just.. like, excessively pointer heavy?

Maybe if you've been in the field long enough, reading a particular term will instantly conjure up an idea of a corresponding algorithm or code block or something and that's what I'm missing.

Re: Improving recommendation systems and search in the age of LLMs

#64

Post by HeatrayEnjoyer »

desdenova wrote: post #39
thorum wrote: post #16

In the age of local LLMs I’d like to see a personal recommendation system that doesn’t care about being scalable and efficient. Why can’t I write a prompt that describes exactly what I’m looking for in detail and then let my GPU run for a week until it finds something that matches?

Why can't you? Just run the biggest model you can find out of swap and wait a long time for it to finish. You'll obviously see more focus on smaller models, because most people aren't willing to wait weeks for their slop, and also don't have server GPU clusters to run huge models.

> Just run the biggest model you can find out of swap

This kills the SSD

Re: Improving recommendation systems and search in the age of LLMs

#65

Post by brookst »

amelius wrote: post #57
brookst wrote: post #56

Earlier quoted context omitted.

How is that better than embeddings? You’re using embeddings to get a finite list of keywords, throwing out the extra benefits of embeddings (support for every human language, for instance), using a conventional index, and then going back to embeddings space for the final LLM? That whole thing can be simplified to: compute and store embeddings for docs, compute embeddings for query, find most similar docs.

Yes, you can do the "old school search" part with embeddings.

Ah, I had interpreted “old school search” to mean classic text indexing and Boolean style search. I’d argue that if it’s using embeddings and cosine similarity, it’s not old school. But that’s just semantics.

Re: Improving recommendation systems and search in the age of LLMs

#66

Post by [deleted] »

novia wrote: post #59

I started listening to this article (using a text to speech model) shortly after waking up. I thought it was very heavy on jargon. Like, it was written in a way that makes the author appear very intelligent without necessarily effectively conveying information to the audience. This is something that I've often seen authors do in academic papers, and my one published research paper (not first author) is no exception.…

[deleted]

Re: Improving recommendation systems and search in the age of LLMs

#67

Post by 7d7n »

novia wrote: post #59

I started listening to this article (using a text to speech model) shortly after waking up. I thought it was very heavy on jargon. Like, it was written in a way that makes the author appear very intelligent without necessarily effectively conveying information to the audience. This is something that I've often seen authors do in academic papers, and my one published research paper (not first author) is no exception.…

Thank you for the feedback! I'm sorry you found it jargony/less accessible than you'd like.

The intended audience was my team and fellow practitioners; assuming some understanding of the jargon allowed me to skip the basics and write more concisely.

Re: Improving recommendation systems and search in the age of LLMs

#69

Post by wildrhythms »

x1xx wrote: post #34

> Spotify saw a 9% increase in exploratory intent queries, a 30% rise in maximum query length per user, and a 10% increase in average query length—this suggests the query recommendation updates helped users express more complex intents To me it's not clear that it should be interpreted as an improvement: what I read in this summary is that users had to search more and to enter longer queries to get to what they neede…

No you don't understand, more queries = more engagement!

Re: Improving recommendation systems and search in the age of LLMs

#70

Post by softwaredoug »

A lot of teams can do a lot with search with just LLMs in the loop on query and index side doing enrichment that used to be months-long projects. Even with smaller, self hosted models and fairly naive prompts you can turn a search string into a more structured query - and cache the hell out of it. Or classify documents into a taxonomy. All backed by boring old lexical or vector search engine. In fact I’d say if you’re NOT doing this you’re making a mistake.
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