predictioniogists

<?php
// Assuming we already have an ItemRec engine named 'similarapps'

// Get app ID
$app = getApp();

try {
// Retrieve similar apps for the current app
    $command = $client->getCommand(
        'itemsim_get_top_n',

predictioniogists

<?php
 
// Assuming we already have an ItemRec engine named 'appsrec'
 
// Get user ID and the current view's category
$uid = getUid();
$category = getCategory();
 
try {
// Retrieve apps recommendation for the current user and category

predictioniogists

<?php

// Assuming we already have an ItemSim engine named 'similarapps'

// Get a list of previous viewed app IDs for the current session
$previouslyViewedApps = getViewedAppsInThisSession();
$similarApps = array();

try {
    // Retrieve similar apps for each previously viewed app

predictioniogists

import io.prediction.ItemSimGetTopNRequestBuilder;

// You need to specify the name of the engine from which you want to retrieve the prediction results. 
// Add the following code to display similar sightseeing spots to the user after arriving a destination:

String[] destinationIds;
 
try {
 //retreive top 5 similar sightseeing spots from the item similarity engine
 String[] itypes = {"sightseeing"}

predictioniogists

// When a new destination is added:
try {
  // sightseeing spot type may be "museums", "landmarks", etc
  String[] itypes = {"destinations", "sightseeing", "sightseeing spot type 1", "sightseeing spot type 2"};
  client.createItem("destination ID or name", itypes);
} catch (Exception e) {
  handleException(e);
}

// When the logged-in user goes to a destination:

predictioniogists

import io.prediction.ItemRecGetTopNRequestBuilder;

// You need to specify the name of the engine from which you want to retrieve the prediction results. 
// Add the following code to display recommended drivers to the logged-in user:

String[] driverIds;

try {
  // Retrieve top 5 recommended drivers from the item recommendation engine.
  // Assuming client.identify("user id") is already called when the user logged in.

predictioniogists

import io.prediction.Client;
import io.prediction.UserActionItemRequestBuilder;
import io.prediction.CreateItemRequestBuilder;

// In the beginning of your application, you need to instantiate the PredictionIO client object.
// You need this client object to import data into PredictionIO and retrieve prediction results. 
// Add this line to the place where you do initialization:
Client client = new Client("your app key");

// Then add the following lines to your codes which handle the corresponding application logic:

predictioniogists

<ivy-module ...>
    ...
    <dependencies>
        <dependency org="io.prediction" name="client" rev="0.6.0" />
        ...
    </dependencies>
    ...
</ivy-module>

predictioniogists

<project ...>
    ...
    <dependencies>
        <dependency>
            <groupId>io.prediction</groupId>
            <artifactId>client</artifactId>
            <version>0.6.0</version>
        </dependency>
    </dependencies>
    ...

predictioniogists

# You need to specify the name of the engine from which you want to retrieve the prediction results. 
# When a user views a meal, you can suggest similar meals to the user by adding the following:  
begin
  # retreive top 5 similar courses from the item similarity engine
  iids = client.get_itemsim_top_n("the engine name", "the viewed meal ID", 5) # list of recommended meal IDs
rescue ItemSimNotFoundError => e
  iids = get_default_iids_list(5) # default behavior when there is no prediction results, e.g. recommends newest meals
  log_error(e) # log the error for debugging or analysis later
end