Find recommendations on similar album - gracenote

we're looking to use the Gracenote API to retrieve similar album from a seed album.
Example
Pearl Jam - Ten
You might like:
Soundgarden - Superunknown
Nirvana - Smell Like Teen Spirit
etc
We'd need the cover art as well.
We're not looking at the audio player for now, just suggestion on similar album with cover art.
Do we have to use the Rythm API? Is not clear to me since in the website Gracenote talks only about radio station or similar.
Any suggestion idea would be very appreciated!
Best,
F.

You can do what you're asking by using the recommend method in the Rhythm API:
https://c*******.web.cddbp.net/webapi/json/1.0/radio/recommend?artist_name=pearl%20jam&max_tracks_per_artist=1&select_extended=cover&client=YOUR_CLIENT_ID&user=A_USER_ID
Here's what I get in the first 5 results (excluding Pearl Jam itself):
Nirvana - Nevermind
Soundgarden - Superunknown
Stone Temple Pilots - Purple
The Verve Pipe - Villains
By using select_extended=cover, you'll get a link to the cover art as well.

Related

Short post with hashtag

I'm looking for a feature on WordPress website.
I want to have a block with single or double line news which will be related to each other with hashtags.
Example:
Kotaku is reporting that 2020's Call of Duty is now a Treyarch-led Call of Duty: Black Ops 5 as opposed to a new game by Raven Software and Sledgehammer Games. #callofduty #blackops #COD
Watch Dogs Legion is Officially Confirmed by Ubisoft; More Info Coming at E3 #watchdogs #E3 #ubisoft
When the user clicks on tags will have access to news like the above examples with the same tag.
Is there any plugin or code for it?
Thanks for helping.

How to read a list of values into a data table in a sandbox?

I have a list of data. It's all a single column, each row is a comment from a post asking for book recommendations. Here's an example, containing the first 2 entries:
"My recommendations from books I read this year:<p>Bad Blood : Man, this book really does read like a Hollywood movie screenplay. The rise and fall of Theranos, documented through interviews with hundreds of ex-employees by the very author who came up with the first expose of Theranos. Truly shows the flaws in the "fake it before you make it" mindset and how we glorify "geniuses".<p>Shoe Dog : Biography of the founder of Nike. Really liked how it's not just a book glorifying the story of Nike, but tells the tale of how much effort, balance and even pure luck went into making the company the household name it is today.<p>Master Algorithm : It's a book about the different fields of Machine learning (from Bayesian to Genetic evolution algos) and talks about the pros and cons of each and how these can play together to create a "master algorithm" for learning. It's a good primer for people entering the field and while it's not a DIY, it shows the scope of the problem of learning as a whole.<p>Three Body Problem: Finally, after years of people telling me to read this (on HN and off), I read the trilogy (Remembrance of Earth's Past), and I must say, the series does live up to the hype. Not only is it fast paced and deeply philosophical, but it's presented in a format very accessible to casual readers as well (unlike many hard sci-fi books which seem to revel in complexity). If I had to describe this series in a single line, it's "What would happen if China was the country that made first contact with an alien race?"","A selection:<p>Sapiens (Yuval Noah Harari, 2014 [English]) - A bit late to the party on this one. Mostly enjoyed it, especially the early ancient history stuff, but I felt it got a bit contrived in the middle - like the author was forcing it. Overall a good read though.<p>How to Invent Everything (Ryan North, 2018) - First book I've pre-ordered in a long time. A look at the history of civilization and technology through a comedic lens. Pretty funny and enjoyable.<p>The Rise of Theodore Roosevelt (Edmund Morris, 1979) - Randomly happened across this book while browsing a used bookstore for some stuff to read on a summer vacation. Loved it. It's big, but reads pretty quick for a biography. I've been a fan of TR since I first really learned about him in High School and I would recommend this for anyone interested in TR/The West/Americana.<p>Jaws (Peter Benchley, 1974) - Quite a bit darker than the movie.<p>Sharp Objects (Gillian Flynn, 2006) - I enjoyed Gone Girl (book and film) so I wanted to read this before the HBO series. To be honest...not my cup of tea. It was <i>okay</i>.<p>The Art of Racing in the Rain (Garth Stein, 2008) - Made me cry on an airplane. Thankfully my coworkers were on a different flight."
(Notice, comments are separated by ",")
I'm trying to load this list into a data table in an R sandbox (rapporter.net). But because of browser security, I can't load a local file (fread, read.table).
How can I read raw data into a data table in R?

How does one fetch a web page in Julia?

I am developing an application that fetches & parses web pages in Julia. I wish to learn how to fetch a url, and also how to fetch a url from beyond an authenticating proxy in Julia.
A basic approach could be like this. This is accessing Reddit's cat forum "aww" using the JSON and Requests packages:
using JSON, Requests
subreddit = "aww"
first_request = get("http://www.reddit.com/r/$(subreddit)/new/.json")
request_json = JSON.parse(first_request.data)
for children in request_json["data"]["children"]
for child in children["data"]
if first(child) == "title" println(last(child)) end
end
end
with predictable results:
Zeus this is reddit, reddit meet Zeus
My blue eyed baby all grown up :)
She had a long walk (:
Charley isn't too picky with where he sleeps
My puppy accidentally did a role-poly
Meet Oliver, Oliver Wood.
Happy World Turtle Day!
Check out my new adorable boxer pup!
My handsome guard cat was being all picturesque this morning
Fluffy cow!
Our Husky/St Bernard mix named Gracie.
Tuxedo Derp!
She just wanted to snuggle and go to sleep. He wasn't having it.
Milo Greetings
This cat has prettier eyes than any human.
20+ Asshole Cats Being Shamed
Too bad this isn't a trick and it's actually stuck to his nose.
Blue family!
Just a puppy eating ice cream

GraceNote - generate playlist with music of a given country

I would like to use GraceNote to generate play-lists which contain songs likely to appeal to, or, at least, be known to, residents of a given country. E.G, Japan, Korea, Turkey, Brazil, France ...
They don't necessarily have to be in the local language, as I don't think that I can do that with GraceNote (can I ?), but local artists would be nice. Is there any way, for instance, to query and generate a playlist using artist origin?
I realize that something like Gangnam Style might be known in most countries ;-) and that play-list generation is inexact when used this way, but I would be happy with a 70 or 80% "I know that song" reaction.
Can it be done? If so, how? #cweichen, can you help?
It seems likely you are referring the the Rhythm API. As you probably can see from the function definition, you cannot create a playlist using 'ARTIST_ORIGIN'.
The closest thing I can think of is creating a playlist (aka radio station) using on a popular song in the given country as a seed.
You may try configuring the 'focus_similarity' value to get a wider variety of songs. This is just a suggestion and I am not sure if this will get you what your looking for.
*Pygn currently does not support 'focus_similarity' configuration but it should not be too difficult to add yourself.

Describing a Cypher Query for multi-level relationship in Neo4j?

I'm developing a small affiliate structure to understand the concept of graph databases better, as well learn Neo4J and see what it can offer me. I've been with RDBMS for years now and Cypher is pretty rough. I'm trying to build a very simple affiliate system:
Affiliate Joe has referred Mary, Bob and Mark. So, i create all their nodes and create the "referred" relationship. Now Mary refers Julie, Jessica and Joan. Bob refers Billy and Baxter. Mark refers Michael and Marx. And their referrals keep referring people.
For each referral that one of Joe's original referrals, Joe earns a "generation". His first generation is Mary, Bob and Mark. His second generation is Julie, Jessica, Joan, Billy, Baxter, Michael and Marx.
Now, with a Cypher query, how can i discover his generations and, of course, discover their number? Their place in the tree? How can i know who is from his 3rd or 4th generation, and who they are?
My mind is twisting here, hope you guys can help.
Vinny,
look at http://tinyurl.com/7vryzwz, is this what you are lookad for, basically
START referrer=node(1)
MATCH path=referrer-[:referred*1..]->refferee
RETURN referrer,refferee, length(path) as generation
ORDER BY length(path) asc

Resources