Recommendation - Algorithm by mahout

No algorithm description given

Overview Eventually we will expand this to allow different parameters and options for neighborhood and similarity functions. One of the more convenient datasets is the MovieLens 100k dataset at http://www.grouplens.org/system/files/ml-100k.zip . More detailed documentation can be found at https://mahout.apache.org/users/recommender/recommender-documentation.html Sample Input The sample input used below (movies.csv) has the following format: userId, itemId, reviewPoints 196,242,3 186,302,3 22,377,1 244,51,2 ... Information about Modes User-based recommendation In this mode, the recommender returns a set of item recommendations for each user, along with the predicted rating for the item. Think of this as generating recommendations based on user similarity. Item-based recommendation This returns, for each item, a list of similar items. Matrix Factorization recommendation (with Alternating Least Squares) An alternate and often more effective approach to recommendation that can be useful for uncovering latent explanatory factors. We plan to expose more of this soon, meanwhile, it acts as a user-based recommender. Upcoming Features We plan to add ALS on implicit feedback and weighted matrix factorization soon.

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This algorithm has Internet access. This is necessary for algorithms that rely on external services, however it also implies that this algorithm is able to send your input data outside of the Algorithmia platform.

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1. Type your input

2. See the result

Running algorithm...

curl -X POST -d '{{input | formatInput:"curl"}}' -H 'Content-Type: application/json' -H 'Authorization: Simple YOUR_API_KEY' https://api.algorithmia.com/v1/algo/mahout/Recommendation/0.2.1

View cURL Docs

algo auth
# Enter API Key: YOUR_API_KEY
algo run algo://mahout/Recommendation/0.2.1 -d '{{input | formatInput:"cli"}}'

View CLI Docs

import (
  algorithmia "github.com/algorithmiaio/algorithmia-go"
)

input := {{input | formatInput:"go"}}

var client = algorithmia.NewClient("YOUR_API_KEY", "")
algo, _ := client.Algo("algo://mahout/Recommendation/0.2.1")
resp, _ := algo.Pipe(input)
response := resp.(*algorithmia.AlgoResponse)
fmt.Println(response.Result)

View Go Docs

import com.algorithmia.*;
import com.algorithmia.algo.*;

String input = "{{input | formatInput:"java"}}";
AlgorithmiaClient client = Algorithmia.client("YOUR_API_KEY");
Algorithm algo = client.algo("algo://mahout/Recommendation/0.2.1");
AlgoResponse result = algo.pipeJson(input);
System.out.println(result.asJsonString());

View Java Docs

import com.algorithmia._
import com.algorithmia.algo._

val input = {{input | formatInput:"scala"}}
val client = Algorithmia.client("YOUR_API_KEY")
val algo = client.algo("algo://mahout/Recommendation/0.2.1")
val result = algo.pipeJson(input)
System.out.println(result.asJsonString)

View Scala Docs

var input = {{input | formatInput:"javascript"}};
Algorithmia.client("YOUR_API_KEY")
           .algo("algo://mahout/Recommendation/0.2.1")
           .pipe(input)
           .then(function(output) {
             console.log(output);
           });

View Javascript Docs

using Algorithmia;

var input = "{{input | formatInput:"cs"}}";
var client = new Client("YOUR_API_KEY");
var algorithm = client.algo("algo://mahout/Recommendation/0.2.1");
var response = algorithm.pipe<object>(input);
Console.WriteLine(response.result);

View .NET/C# Docs

var input = {{input | formatInput:"javascript"}};
Algorithmia.client("YOUR_API_KEY")
           .algo("algo://mahout/Recommendation/0.2.1")
           .pipe(input)
           .then(function(response) {
             console.log(response.get());
           });

View NodeJS Docs

import Algorithmia

input = {{input | formatInput:"python"}}
client = Algorithmia.client('YOUR_API_KEY')
algo = client.algo('mahout/Recommendation/0.2.1')
print algo.pipe(input)

View Python Docs

library(algorithmia)

input <- {{input | formatInput:"r"}}
client <- getAlgorithmiaClient("YOUR_API_KEY")
algo <- client$algo("mahout/Recommendation/0.2.1")
result <- algo$pipe(input)$result
print(result)

View R Docs

require 'algorithmia'

input = {{input | formatInput:"ruby"}}
client = Algorithmia.client('YOUR_API_KEY')
algo = client.algo('mahout/Recommendation/0.2.1')
puts algo.pipe(input).result

View Ruby Docs

use algorithmia::Algorithmia;

let input = {{input | formatInput:"rust"}};
let client = Algorithmia::client("YOUR_API_KEY");
let algo = client.algo("mahout/Recommendation/0.2.1");
let response = algo.pipe(input);

View Rust Docs

import Algorithmia

let input = "{{input | formatInput:"swift"}}";
let client = Algorithmia.client(simpleKey: "YOUR_API_KEY")
let algo = client.algo(algoUri: "mahout/Recommendation/0.2.1") { resp, error in
  print(resp)
}

View Swift Docs

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