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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 . More detailed documentation can be found at 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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For additional details on how pricing works, see Algorithmia pricing.

Internet access

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.

To understand more about how algorithm permissions work, see the permissions documentation.

1. Type your input

2. See the result

Running algorithm...

3. Use this algorithm

curl -X POST -d '{{input | formatInput:"curl"}}' -H 'Content-Type: application/json' -H 'Authorization: Simple YOUR_API_KEY'
View cURL Docs
algo auth
algo run algo://mahout/Recommendation/0.2.1 -d '{{input | formatInput:"cli"}}'
View CLI Docs
import (
  algorithmia ""

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)
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);
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)
View Scala Docs
var input = {{input | formatInput:"javascript"}};
           .then(function(output) {
View Javascript Docs
var input = {{input | formatInput:"javascript"}};
           .then(function(response) {
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

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

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
View Swift Docs
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