ContentSimilarityMeasurement

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Given two documents/text (strings), this algorithm returns a similarity measurement value  between 0 and 1, 1 for text that are purely same and 0 for that are purely unrelated. It involves transforming each text into a vectors in a k - dimensional space model, then compute the cosine similarity ( i.e. dot product of the vectors) between them. This algorithm is very useful in content based recommendation engine for recommending products having similar attributes like title, materials, fabric, color, care tips, patterns for the ecommerce domain. Ex:- Input:- Suppose, I have two products (taken from fashion sites) like http://www.limeroad.com/olive-green-cotton-kurti-mystique-india-p10083527?df_type=new_home, http://www.limeroad.com/green-cotton-kurti-mystique-india-p10083532 having title /description ["olive green cotton kurta",  " green cotton kurta "] Similarity Index:- 0.8660254037844387 So, for a particular items, one can recommend similar/related items from the large datasets.

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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' https://api.algorithmia.com/v1/algo/amitkumargaur/TextSimilarityMeasurement/0.15.0
View cURL Docs
algo auth
# Enter API Key: YOUR_API_KEY
algo run algo://amitkumargaur/TextSimilarityMeasurement/0.15.0 -d '{{input | formatInput:"cli"}}'
View CLI 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://amitkumargaur/TextSimilarityMeasurement/0.15.0");
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://amitkumargaur/TextSimilarityMeasurement/0.15.0")
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://amitkumargaur/TextSimilarityMeasurement/0.15.0")
           .pipe(input)
           .then(function(output) {
             console.log(output);
           });
View Javascript Docs
var input = {{input | formatInput:"javascript"}};
Algorithmia.client("YOUR_API_KEY")
           .algo("algo://amitkumargaur/TextSimilarityMeasurement/0.15.0")
           .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('amitkumargaur/TextSimilarityMeasurement/0.15.0')
print algo.pipe(input)
View Python Docs
library(algorithmia)

input <- {{input | formatInput:"r"}}
client <- getAlgorithmiaClient("YOUR_API_KEY")
algo <- client$algo("amitkumargaur/TextSimilarityMeasurement/0.15.0")
result <- algo$pipe(input)$result
print(result)
View R Docs
require 'algorithmia'

input = {{input | formatInput:"ruby"}}
client = Algorithmia.client('YOUR_API_KEY')
algo = client.algo('amitkumargaur/TextSimilarityMeasurement/0.15.0')
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('amitkumargaur/TextSimilarityMeasurement/0.15.0');
let response = algo.pipe(input);
View Rust Docs
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