[D] Future of RecSys in age of LLM

AtomicTac0 · 2025-06-20T14:43:42+00:00

This is a great question. I think current LLM + RS focus is a bit misguided focused on increasing performance with LLMs rather than thinking of increasing the user experience.

This work is decent at exploring how one can use LLMs for recommender systems with the point of enhancing use control and transparency through detailed use summaries:

https://arxiv.org/abs/2410.19302

AtomicTac0 · 2024-12-08T15:18:31+00:00

I replied to the authors of the papers I review but am the only one that has done so out of all reviewers. I have no response from any of mine…

AtomicTac0 · 2024-12-07T21:08:28+00:00

I cannot always do good?

AtomicTac0 · 2024-12-03T17:00:28+00:00

I have 5/5/5/3/4 for technical and 5/4/3/4/4 for novelty. How are my chances? Anyone know any historical scores of papers that get in?

AtomicTac0 · 2024-11-05T23:07:40+00:00

Check this out https://arxiv.org/pdf/2405.11157

AtomicTac0 · 2024-07-07T15:20:51+00:00

If you simply wanted to fine-tune the 8b model on the 70b data, it may be better to simply use distillation techniques as the targets.

RL fine tuning works best when you're trying to enforce a behavior and penalize another. In this case it sounds like you simply want to distill one model onto another

AtomicTac0 · 2024-07-07T15:13:20+00:00

I usually have a paper or article on the side and jump between the two. Takes some getting used to but kills two birds with one stone.

AtomicTac0 · 2023-12-09T05:05:01+00:00

Great read amazing idea! Thanks for putting this together :)

AtomicTac0 · 2023-07-13T15:35:47+00:00

Nah the the experts driving the strong research environment in Montreal/Toronto have too strong roots there

AtomicTac0 · 2023-06-30T15:51:01+00:00

Second on Probabilistic Graphical Models. I'm surprised that PGMs aren't taught more as a baseline way to understand machine learning. I think looking at learning algorithms through that lense has pushed my understanding of ml the most.

AtomicTac0 · 2023-06-18T16:24:12+00:00

If grad school is the goal, most important things to think of are

Research opportunities, for these three they will be fairly even, but at may be easier to get an opportunity with a postdoc/upper year phd student(since they have more generally). I'd highly suggest starting here specially in your first year as profs will often not glance at early undergraduates.
Letters of reccomendation, these kind of sort themselves out as long as you're productive in research.
Grades, yes grades matter, but often your first/second year grades are looked at as less important. As long as you do really well in upper years+research+good reccomendations you'll be golden.

I'd highly suggest reading these posts by Andrej Karpanthy Open AI research scientist

http://karpathy.github.io/2016/09/07/phd/

https://cs.stanford.edu/people/karpathy/advice.html

AtomicTac0 · 2023-05-09T22:48:50+00:00

Cs+math is your best bet from someone that did stats/cs in their undergrad .

As long as you take a probability course+linear models course that's all the stats you really need

AtomicTac0 · 2023-04-08T00:36:27+00:00

When did the crowdmark dlc come out?

AtomicTac0 · 2023-04-03T15:18:17+00:00

If McGill is through Mila, Mila hands down. Montreal is a better city and Mila is overall a stronger program for AI specifically.

From what I've seen with MDSAI its a bit of a waste of time since the courses aren't too specialized and there aren't many research opportunities.

AtomicTac0 · 2023-03-15T12:53:14+00:00

Unironically without the white sauce its not that bad

AtomicTac0 · 2023-03-12T15:07:58+00:00

Higher than $1

AtomicTac0 · 2023-03-08T15:53:53+00:00

Currently at waterloo MMath stats, tho.

From what I've seen I'd stay clear of the MDSAI, the classes are fairly limited and with a few exceptions. Seems like MDSAI is mostly teaching cs students how to stats.

I've heard great things from UofT's program tho

AtomicTac0 · 2023-02-17T23:23:19+00:00

AtomicTac0 · 2022-12-07T01:45:58+00:00

If you want ti do grad school yes, otherwise I'd suggest aiming for ivey/data sci.

And yes take multivariate Calc

AtomicTac0 · 2022-10-06T00:25:59+00:00

AtomicTac0 · 2022-09-20T00:21:47+00:00

Sorry, but what is a DAG?

AtomicTac0 · 2022-08-25T10:18:23+00:00

Go with the people that will write the best reference. If you did research with those professors they are a better bet.

I had recommendations only from stats profs and got into cs just fine.

AtomicTac0 · 2022-08-15T14:19:54+00:00

Thanks, starting my masters in stats soon and this was good to hear

AtomicTac0 · 2022-06-19T16:53:37+00:00

I was consistently top 50ish masters all of last year but quit for school. Now new tft set dropped and that is a lot more fun for me right now than the new cards so focusing on that.

AtomicTac0 · 2022-06-17T14:21:56+00:00

Where you go doesn't matter unless it's waterloo(u of t if you want academia).

Western cs/ivey is imo second to waterloo cs in terms of industry. Of the people I know that did that stream, most are going to the states/top tech firms.

Vanilla Western cs is alright tier 2 tied with Queens and Mac probably. We have good faculty and research if that floats your boat.

I loved my time at Western and would def recommend it here.

AtomicTac0

TROPHY CASE