phprecommender

PHP based recommender system that can be used to predict values, find similar items or recommend items to user.

Movielens small dataset file ratings.csv is added. Example codes use this file to build dataset for test cases

Distance calculation methods

• Cosine of angle
• Manhattan distance
• Euclidean distance

Complete set of examples using MovieLens dataset ratings.csv

include autoload.php and add use statements for classes

include "src/autoload.php";

use UglyRecommender\DistanceCalculator;
use UglyRecommender\VectorsUnequalException;
use UglyRecommender\NeighborsNotFoundException;
use UglyRecommender\RecommenderSystem;
use UglyRecommender\DataHelper\CsvLoadHelper;

Loading Dataset

Every dataset is different in format but our class RecommenderSystem expects data to be normalized in a specific format before it is passed to the class for operations we are using MovieLens dataset "ratings.csv" throughout for our examples each row in ratings.csv is in the format userid,movieid,rating_given

This recommender system use DataMatrix class to represet dataset. We will use CsvLoadHelper to construct DataMatrix filled with values from "ratings.csv"

Construct data matrix

$dataMatrix=CsvLoadHelper::load("ratings.csv");

Fill missing values so matrix is in correct dimension

//we will fill missing values with 0 for this dataset
$dataMatrix->setMissingValues(0);

Create instance of RecommenderSystem and set parameters

$recommender=new RecommenderSystem();  
$recommender->setDataMatrix($dataMatrix);  
$recommender->setUnknownValue(0); //this tells RecommenderSystem that missing values are filled with 0

GET MOVIE RECOMMENDATONS FOR USER "448"

Use Cosine similarity

$recommender->setDistanceMethod("cosine");
//get recommendations for user "448".Maximum neighbors(similar users) to use=100,Maximum results to bring=5
$recommendations=$recommender->getRecommendations("448",100,5);

Use Manhattan distance

$recommender->setDistanceMethod("manhattan");
$recommendations=$recommender->getRecommendations("448",100,5)

PREDICT RATING TO BE GIVEN BY USER "448" to MOVIE "5"

//use maximum 100 neighbors to predict
$value=$recommender->predict("448","5",100);

PREDICT RATING TO BE GIVEN BY USER "448" to MOVIE "5" using weighted neighbors

//use maximum 100 neighbors to predict and use weights for nearest 10 neighbors
$value=$recommender->predict("448","5",100,[1.3,1.8,1.7,1.4,1.2,1.1,1.05,1.12,1.13]);

GET MAXIMUM 15 USERS SIMILAR TO USER 448

$recommender->setDistanceMethod("cosine"); //use cosine similarity
$similar_users=$recommender->getNearestNeighbors("448","",15);

GET MAXIMUM 15 USERS SIMILAR TO USER 448 WHO HAS WATCHED MOVIE "5"

$recommender->setDistanceMethod("cosine");
$similar_users=$recommender->getNearestNeighbors("448","5",15)

Future updates

• Adding Error calculation methods
• Adjust weights depending on error value
• Include hamming distance to use string based data
• Matrix factorization