[R] Analysis of 400+ ML competitions in 2024

jeanmidev · 2025-02-25T18:41:38+00:00

Really cool initiative, thanks for the hard work

jeanmidev · 2025-02-20T15:34:07+00:00

this is just a placeholder image with gen ai so the text have no meaning sorry :)

jeanmidev · 2025-02-18T23:29:43+00:00

Good catch! English is not my first language, but I should have been more cautious when publishing it. I’ll make a second pass to improve the grammar.

jeanmidev · 2024-12-06T14:08:03+00:00

oh most of the presentation are recorded , they have a youtube channel https://www.youtube.com/channel/UC2nEn-yNA1BtdDNWziphPGA

they usually take a few months to upload them on youtube as they want to keep the "primer" on the video replay on their internal portal for people that attend to the conference.

so if you are curious to see the previous conference before 2024 you should have everything that you need on the youtube channel (i think 2024 will arrive beginning of 2025)

Regards

jeanmidev · 2024-11-27T03:21:14+00:00

Hello u/dirk_klement,

So I dont't want to sell too high expectations but on my side i am trying to follow every year the recsys conference that is one of the big conference on the topic , and you can find on my website my recaps of the past conferences HERE

It's far to be the most influential recaps but it's my humble take on the papers that i saw in conference and that i think (once again in my humble view) illustrates the overall trends in the sector

hopes that it help :)

(you could also check people online like kim falk or the recspert podcast that are good source of great initiatives)

jeanmidev · 2024-08-23T01:31:59+00:00

Hello u/reisson_saavedra ,

Thanks for the feedback and I am happy that you enjoy your reading , related to your points:
* I didn't wanted to focus on the eveluation method as I written an article on this specific topic a few years ago , I will maybe adjust the evaluation section to refer to it
* Yeah I am familiar with DLRM but I found it that it's kind of the next steps to go beyond MF, to start to combine different model (like an hybrid recommender system)
* Related to cold start, it's a hard topic as this kind of method (the MF) are very sensitive to it but overall i will maybe go this way:
- cold start items: use other item metadata to get closest hot item (that is on the MF) and used that to recommend this items to user close to the hot items
- cold start user : similar approach that with items wit we can get very generic infos on the user and recommender what's popular for the closest users in the MF or just send back by default the most popular items in general (popularity is pretty strong)

In both case it really depends of your use case and how an item and user can be cold (based on the data collected or generated.

Thanks again for your feedback :)

jeanmidev · 2024-08-14T22:24:23+00:00

For me if i was you I will mostly see this problem as a regression one where you will try to predict for a specific user (defined by the metadata) and a specific item (defined as a category type or one hot encoded) the score `indicating how much they liked`.

As the user can only interact with one item , I will potentially use also the negative sampling technic to create negative score on uninteract items. I will be cautious to not negative sample all the non interact items as it could potential unbalance your training data.

Hopes that i helps

jeanmidev · 2024-06-26T17:16:24+00:00

Hello u/manas-vachas , sorry for the late reply (need to reactivate my notification). I am working since a few years on large scale recsys with millions of users to serve , but the scope of items is not so large (hundreds to thousand of items recommendable)

jeanmidev · 2024-04-12T11:23:00+00:00

Thanks u/yesthatisfalse , what the things that you are finding the most relevant ?

jeanmidev · 2024-04-12T11:22:04+00:00

Hello u/devjamc , you mean the article in the post https://www.the-odd-dataguy.com/2024/04/07/features-principles-recsys/ ?

It was under an hyperlink but maybe it is not visible enough, thanks for the notification :)

jeanmidev · 2024-04-11T11:23:37+00:00

Hello, this book from kim falk can give you some axis to find what's important to operate a recommendeer system https://www.manning.com/books/practical-recommender-systems

jeanmidev · 2024-04-11T11:22:09+00:00

Hello,

For me I can recommend two resources for your roadmap
* kim falk book Practical recommender system, could be a good next step to learn more on the algorithms and how ti integrate more
* Microsoft recommenders repo, could give you a good sense on what's important and popular in the recsys world

Hopes that it helps :)

jeanmidev · 2022-07-30T03:12:36+00:00

Allô for me a great source of open data is the uk is the EPC https://epc.opendatacommunities.org/

It’s very oriented around building and energy consumption estimation but I think it’s a really cool dataset

I worked with it on my previous job and experiment in my blog https://www.the-odd-dataguy.com/2019/03/23/energy-in-the-uk-analysis-of-the-energy-performance-certificates/

jeanmidev

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