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does anybody here know a lot about recommendation algorithms? The one I'm interested in seems obvious to me, but it's never implemented so maybe it doesn't work

does anybody here know a lot about recommendation algorithms? The one I'm interested in seems obvious to me, but it's never implemented so maybe it doesn't work, or it serves outliers and not the mainstream so why bother. The data is there, but I don't get to mine it. (heheh "mine" it) I don't want to know what's popular, I want to know what other people who share my past tastes are watching currently. Doesn't matter the size of my niche, why can't i find my niche, and be shown my cohort? I just don't feel it's an impossible task to find, for example, a set of people who enjoyed Deadpool 1, Guardians of the Galaxy 1, but think the entire rest of the superhero cavalcade is utter garbage. ok, I'll allow Kickass also, and a grudging nod to Iron Man 1. (as an illustration, I just searched to look up the name of Iron Man, I could only remember Tony Stark. Top search results were "a fictional character", "played by R Downey Jr", "Marvel cinematic universe"... You see, none of those is Iron Man 1. I don't care about the horde of people who care about a supposed cinematic universe. We (in my cohort) liked the first one, and what we liked about it wasn't continued in the 2nd. How about preferences that show I like a few early films with say George Clooney, but after that, no. Julia Roberts, same. I remember when Meryl Streep was a new actress, but now she's a red flag. I'm sure my tastes aren't shared by everybody, but I'm also sure, since I can articulate reasons, there must be others who in general share them, people who are willing to watch what's new and trendy in some way, but then not beat the dead horse. Oh, Spielberg, a few winners, but overall "nope nope nope", the way he aims at sentiment is a zero for me. I originally had this idea back in the 80's when Consumer Reports had their monthly mail-in bingo card for what current movies you liked, then they would tabulate what was popular among their readers. That type of "best of" list is entirely missing co-variance, which I think is where I live.

anjc 3y ago
That's called collaborative filtering (https://en.wikipedia.org/wiki/Collaborative_filtering https://en.wikipedia.org/wiki/Collaborative_filtering) and is perhaps the most battle-hardened and most effective approaches in recommender systems. Even now, novel deep learning approaches implement the concept, but simple naive approaches are still as/more effective. The first paper published on it specifically in the field was in the 90s but the seeds of it go back to the 70s. It would have made a good thesis in the 80s :) Part of why it's so effective are for the reasons you outline, in that it can find items that you'll probably like, that aren't similar to items you already like, based on people that have similar tastes to you.
- fsckboy 3y ago
  perfect info, yes, it's a way of discovering things that bore no relationship to what you had already seen, except that your cohort also liked them. You triggered me to remember additional elements of my idea. the 80's saw the arrival of the CD-ROM, and I thought "Consumer Reports can't publish a detailed chart of everybody's taste, but I'll bet their movie voting database would fit on a CD-ROM, and a piece of statistical software could figure out what movies each person would like." But I didn't think people would pay enough for the service to get a new CD-ROM in the mail every month, and if it didn't stay up to date with the latest VHS releases :) it wouldn't be useful, and Consumers Union was such a ... purist/hairshirt type organization how could anybody even work with them.
alexhutcheson 3y ago
This course is a great intro to some of the “workhorse” models for recommendation systems: https://developers.google.com/machine-learning/recommendation https://developers.google.com/machine-learning/recommendatio... The same general methods are also relevant to other matching problems, like search. Disclosure: Work at Google, but not on anything related to this course.
- Cyphase 3y ago
  Thank you for using "disclosure", not "disclaimer".

anjc 3y ago

That's called collaborative filtering (https://en.wikipedia.org/wiki/Collaborative_filtering https://en.wikipedia.org/wiki/Collaborative_filtering) and is perhaps the most battle-hardened and most effective approaches in recommender systems. Even now, novel deep learning approaches implement the concept, but simple naive approaches are still as/more effective. The first paper published on it specifically in the field was in the 90s but the seeds of it go back to the 70s. It would have made a good thesis in the 80s :) Part of why it's so effective are for the reasons you outline, in that it can find items that you'll probably like, that aren't similar to items you already like, based on people that have similar tastes to you.

fsckboy 3y ago
perfect info, yes, it's a way of discovering things that bore no relationship to what you had already seen, except that your cohort also liked them. You triggered me to remember additional elements of my idea. the 80's saw the arrival of the CD-ROM, and I thought "Consumer Reports can't publish a detailed chart of everybody's taste, but I'll bet their movie voting database would fit on a CD-ROM, and a piece of statistical software could figure out what movies each person would like." But I didn't think people would pay enough for the service to get a new CD-ROM in the mail every month, and if it didn't stay up to date with the latest VHS releases :) it wouldn't be useful, and Consumers Union was such a ... purist/hairshirt type organization how could anybody even work with them.

alexhutcheson 3y ago

This course is a great intro to some of the “workhorse” models for recommendation systems: https://developers.google.com/machine-learning/recommendation https://developers.google.com/machine-learning/recommendatio... The same general methods are also relevant to other matching problems, like search. Disclosure: Work at Google, but not on anything related to this course.

Cyphase 3y ago
Thank you for using "disclosure", not "disclaimer".