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Reco4j is an open source project aims at developing a recommendation framework based on graph data sources. We choose graph databases for several reasons. They are NoSQL databases, so "schemaless". This means that it is possible to extend the basic data structure with intermediate information, i.e. similarity value between item and so on. Moreover, since every information is expressed with properties, nodes and relations, the recommendation process can be customized to work on every graph. Reco4j can be used on every graph where "user" and "item" is represented by node and the preferences are modelled as relationship between them. Current implementation leverage on Neo4j as first graph database integrated in our framework.

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/*
 * BasicRecommender.java
 * 
 * Copyright (C) 2013 Alessandro Negro 
 *
 * This program is free software: you can redistribute it and/or modify
 * it under the terms of the GNU General Public License as published by
 * the Free Software Foundation, either version 3 of the License, or
 * (at your option) any later version.
 *
 * This program is distributed in the hope that it will be useful,
 * but WITHOUT ANY WARRANTY; without even the implied warranty of
 * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
 * GNU General Public License for more details.
 *
 * You should have received a copy of the GNU General Public License
 * along with this program.  If not, see .
 */
package org.reco4j.recommender;

import java.util.ArrayList;
import java.util.Collection;
import java.util.Collections;
import java.util.List;
import org.apache.commons.math.random.RandomGenerator;
import org.reco4j.dataset.UserItemDataset;
import org.reco4j.graph.EdgeTypeFactory;
import org.reco4j.graph.IEdge;
import org.reco4j.graph.IEdgeType;
import org.reco4j.graph.IGraph;
import org.reco4j.graph.INode;
import org.reco4j.model.Rating;
import org.reco4j.util.IRecommenderConfig;

/**
 *
 * This class is the class that implements some basic method of the recommender.
 *
 * @author Alessandro Negro 
 */
public abstract class BasicRecommender
  implements IRecommender
{
  protected UserItemDataset userItemDataset;
  protected IEdgeType rankEdgeType;
  private TConfig config;
  protected String modelName;
  
  @Override
  public void setModelName(String modelName)
  {
    this.modelName = modelName;
  }
    
  public BasicRecommender(TConfig config)
  {
    this.config = config;
    rankEdgeType = EdgeTypeFactory.getEdgeType(IEdgeType.EDGE_TYPE_RANK, config.getGraphConfig());
  }

  @Override
  public void loadRecommender(IGraph learningDataSet)
  {
    //do nothing
  }

  @Override
  public void updateRecommender(IEdge newEdge, int operation)
  {
    throw new UnsupportedOperationException("Not supported yet.");
  }

  /**
   * @return the config
   */
  @Override
  public TConfig getConfig()
  {
    return config;
  }
  @Override
  public List recommendRandomUsers(Collection userSet, INode item, int limit)
  {
    ArrayList userList = new ArrayList(userSet);
    List results = new ArrayList(limit);
    Collections.shuffle(userList);
    
    for (int i = 0; i < limit; i++)
      results.add(new Rating(userList.get(i), item, 0, null));
    return results;
  }

  public UserItemDataset getUserItemDataset()
  {
    return userItemDataset;
  }

  public void setUserItemDataset(UserItemDataset userItemDataset)
  {
    this.userItemDataset = userItemDataset;
  }
  
}