Are there any personalized recommendation engines/sites that you trust?
In the 2000s I used to use a service called last.fm (originally called Audioscrobbler) that would track the music I listened to and give me recommendations based on that. It was able to give me some really great personalized suggestions, but that came at the expense of me handing over significant amounts of personal data.
In prioritizing privacy, I feel like I've stepped away from a lot of the big recommendation engines because they're tied to data-hungry companies I am in the process of disengaging with (e.g. Goodreads is owned by Amazon). I can still find stuff I like, but it's often the result of manual searching that turns up popular recommendations that work for me, rather than less well-known or acutely relevant things. last.fm was good at giving me less "obvious" recommendations and would find music I was unlikely to find on my own. I want that, but for all of my media: books, movies, etc.
There's a second concern in that I also feel like I can't trust platforms like Netflix, who seem to prioritize their content over that of other studios. Their recommendations feel weighted in their favor, not mine.
What I want is an impartial recommendation engine that gives me high quality personalized suggestions without a huge privacy cost.1 Is this a pipe dream, or are there examples of this kind of thing out there?
1. I don't mind handing over some of my specific interest data in order to get good recommendations for myself and help a site's algorithms cater to others, as I get that's how these things work. I just don't like the idea of my interests being even more data for a company that already has thousands of intimate data points on me.
I mentioned MovieLens in another comment thread.
It's a non-commercial movie recommendation engine that's been around since 1997, part of the University of Minnesota's GroupLens project. The privacy policy indicates that GroupLens may share anonymized data.
I hadn't used it for at least the last seven or eight years, since we'd been seeing less theatrical or cable content, and more Netflix. On revisiting recently, I was surprised to find that it still gave acceptably predictive recommendation scores for current movies, based on prior ratings.
As I noted in the linked thread, though, the rating system is still insensitive to narrow preferences and exclusions, like disliked actors or foreign movie genres.
They had a project for book recommendations, but it seems a little dusty and unloved.
My biggest gripe with MovieLens is how slow it all is. I could forgive less than optimal recommendations if every page didn't take 10 seconds to load [1]. Browsing through pages becomes excruciating.
[1]: no it's not my internet, I have a gigabit connection, it's not my computer, I have an 8-core beast and no it's not my browser, this happens regardless of which one I choose
It's absolutely bad, especially by comparison with the very lean and quick GNOD sites.
But MovieLens is a legacy project that's been left lying around to gather data for UMN's GroupLens. It's not being groomed for venture capital investment and founder cash-out, or tweaked for maximal advertising eyeballs.
As long as it's functional and not exploitive, I'm using it on a casual basis and can live with the slowness.
Also, not sure where you're accessing from, browser, etc. I just tested MovieLens on mobile, and while there's perceptible page load delay, it's around 1 - 2 seconds, not 10.
I signed up for this and plan to spend some time soon feeding it some data about my preferences. Like you said in another comment, I'm glad my data is going towards university research and this platform isn't just another data funnel that's cashing in on its users. This is the kind of thing I was hoping for when I made this thread!
Shame their book service isn't up to date though. I watch movies very infrequently, but I read a lot more often.
Well, I just had a taste of the marvels of the 21st Century - some guy in Germany flung together a bunch of ML-based tools that seem to do as well as the old shiny things like Audioscrobbler.
Check out the Global Network of Discovery site.
It's got a music recommender which seems to have been fed enough data to hit the high points. The music graph function, when tested on a common band, allowed tracing of nearly all the bands listed after "Pink Floyd" in a thread on /r/ifyoulikeblank mentioned above.
It looks like most of the ad revenue comes from https://www.productchart.com/ - which is getting referral money from Amazon. That's a tool I didn't need to know about, because it's easy to zoom in on maximum specifications for a given price, or start at maximum specs and see how little they need to dial back to get a thing (phones, monitors, laptops, 3-D printers, etc.) at a price you're willing to pay. It's very fast.
Footnote: I just took Gnoosic for a test drive, feeding in three artists - Nina Simone, Moby, and The Detroit Cobras, to cover a few bases. Gnoosic came back with BellRays, and I'm actually delighted with the discovery.
This is really cool!
GNOD is a great find! Thank you for sharing. It's quite privacy friendly, and based on a few test runs I did with it, it seems to give decent recommendations. I put a couple new books on my to-read list!
There were some items I entered that it didn't know, but I like that, in adding them, I'm helping to grow its database.
I think GNOOKS doesn't really have enough depth to provide great book recommendations yet. My quick test with Tibor Fischer, Nancy Kress, and Iain Banks came back with Samuel Johnson, Erasmus, and Sheri S. Tepper, only one of whom might be tangentially related to the others. Some other test triplets also produced Samuel Johnson and Erasmus as suggestions, so those authors might be dummy defaults.
When I was trying out some trios, I had one that gave some really imbalanced recommendations, and I realized it was because one of the authors wasn't in the system, so it was going with the other two only. Another time I got some seemingly bad recommendations was when I used an author who was in the system but who only had one connection when I pulled her up on the literature map.
It definitely has room to grow, and I wish it were book-specific rather than author-specific, but it still fits the bill for what I was looking for.