Java tutorial
import java.io.BufferedReader; import java.io.File; import java.io.FileReader; import java.io.IOException; import java.net.URISyntaxException; import java.util.ArrayList; import java.util.Collection; import java.util.HashMap; import java.util.List; import java.util.Random; import org.apache.mahout.cf.taste.common.TasteException; import org.apache.mahout.cf.taste.impl.recommender.GenericItemBasedRecommender; import org.apache.mahout.cf.taste.impl.similarity.GenericItemSimilarity; import org.apache.mahout.cf.taste.impl.similarity.GenericItemSimilarity.ItemItemSimilarity; import org.apache.mahout.cf.taste.model.DataModel; import org.apache.mahout.cf.taste.recommender.RecommendedItem; import org.apache.mahout.cf.taste.similarity.ItemSimilarity; /*Contributors: Kjersti Fagerholt, Roar Gjvaag, Ragnhild Krogh, Espen Strmjordet, Audun Sther, Hanne Marie Trelease, Eivind Halmy Wolden "Copyright 2015 The TAG CLOUD/SINTEF project Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with the License. You may obtain a copy of the License at http://www.apache.org/licenses/LICENSE-2.0 Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License." */ /** * Creates a list of recommendations for a user based on the recommendation technique of content based filtering. * The recommendations are inserted into the database. * * @author Audun Sther * @author Kjersti Fagerholt * @author Eivind Halmy Wolden * @author Hanne Marie Trelease */ public class ContentBasedRecommender { /** The location of this file. Used to locate the similarities.csv-file */ File fileLocation = null; /** The connection to the database */ DatabaseConnection conn = null; /** The user we are making the recommendations for */ long userId; /** The list of recommendations we are producing */ List<RecommendedItem> recommendations; /** Tells us whether we should make brand new recommendations (="false") or if we should make recommendations of items that is not in the front end array (="true") */ String add; /** * Constructor * * @param userId the user we are making recommendations for * @param add tells us if we should add to existing list or make a new recommendations list * @throws TasteException thrown if there is something wrong with the DatabaseConnection */ public ContentBasedRecommender(long userId, String add) throws TasteException { this.userId = userId; this.add = add; conn = new DatabaseConnection("content" + userId); } /** * Method the creates a list of recommendations. * Already rated stories are excluded, as are stories present in the front end array if add="true" * * @throws TasteException thrown if there if something went wrong with Mahout */ public void runContentBasedRecommender() throws TasteException { /*Find out where this file is located*/ try { fileLocation = new File(this.getClass().getProtectionDomain().getCodeSource().getLocation().toURI()); } catch (URISyntaxException e) { e.printStackTrace(); } /*"content"+userId is the name of the view we shall create*/ conn.setConnection(); /*Create a temporary view the includes all preferences values for this user*/ conn.createView((int) userId); /*Sets the dataModel based on the data in the created view*/ conn.setDataModel(); DataModel model = conn.getDataModel(); /*Gets all the info from the similarites.csv file into a list of objects accepted by Mahout*/ Collection<ItemItemSimilarity> sim = getStorySimilarities(); /*GenericItemBasedRecommender need an ItemSimilarity-object as input, so create an instance of this class.*/ ItemSimilarity similarity = new GenericItemSimilarity(sim); /*Create a new Recommender-instance with our datamodel and story similarities*/ GenericItemBasedRecommender recommender = new GenericItemBasedRecommender(model, similarity); /* Compute the recommendations. model.getNumItems() is the number of recommendations we want (we don't really want that many, * but we don't know how many of the top items the user already have rated), don't worry about the null, * and true tells the recommender that we want to include already known items*/ List<RecommendedItem> recommendations = recommender.recommend(userId, model.getNumItems(), null, true); /*Find the stories that the user have rated*/ HashMap<Integer, Integer> ratedStories = conn.getRated((int) userId); ArrayList<Integer> frontendStories = new ArrayList<>(); /* Find the stories already present in the recommendations list at front end * These stories should not be recommended again*/ if (add.equals("true")) { frontendStories = conn.getStoriesInFrontendArray((int) userId); } int ranking = 1; Random rand = new Random(); int randomDislikedRanking = rand.nextInt(6) + 5; ArrayList<DatabaseInsertObject> itemsToBeInserted = new ArrayList<>(); ArrayList<Long> idsToBeInserted = new ArrayList<>(); for (RecommendedItem recommendation : recommendations) { /* To get a story outside of the users preferences, finds the least recommended story */ if (randomDislikedRanking == ranking) { /*Make sure the false recommendation is not already in the front end array or already among the top ten recommendation (may happen if the user doesn't have many not seen/not rated stories left) */ for (int i = 1; i < recommendations.size(); i++) { long dislikedStoryId = recommendations.get(recommendations.size() - i).getItemID(); if (!frontendStories.contains((int) dislikedStoryId) && !idsToBeInserted.contains(dislikedStoryId) && ratedStories.get((int) dislikedStoryId) == null) { itemsToBeInserted.add(new DatabaseInsertObject((int) userId, "DF." + dislikedStoryId, "FalseRecommendation", 1, 0, ranking, recommendations.get(recommendations.size() - i).getValue())); idsToBeInserted.add(dislikedStoryId); System.out.print("False recommend: "); System.out.println(dislikedStoryId); break; } } ranking++; if (ranking > 10) { break; } continue; } /*If the item has not been rated,is not already in the recommendation list at front end or already a false recommendation we insert it*/ if ((ratedStories.get((int) recommendation.getItemID()) == null) && !frontendStories.contains((int) recommendation.getItemID()) && !idsToBeInserted.contains(recommendation.getItemID())) { /*Get the 30 items that had most influence on the recommendation*/ List<RecommendedItem> becauseItems = recommender.recommendedBecause(userId, recommendation.getItemID(), 30); int counter = 1; ArrayList<RecommendedItem> explanationItems = new ArrayList<>(); for (RecommendedItem because : becauseItems) { /*Add story to explanation if this story has been rated and the rating is good*/ if (!explanationItems.contains(because) && ratedStories.get((int) because.getItemID()) != null && ratedStories.get((int) because.getItemID()) > 2) { explanationItems.add(because); counter++; } if (counter > 3) { break; } } /*Gets the titles of the explanation-stories and creates a string*/ String explanation = conn.createExplanation(explanationItems); itemsToBeInserted.add(new DatabaseInsertObject((int) userId, "DF." + recommendation.getItemID(), explanation, 0, 0, ranking, recommendation.getValue())); idsToBeInserted.add(recommendation.getItemID()); System.out.println(recommendation); ranking++; } /*When we got 10 new recommendations, we're happy*/ if (ranking > 10) { break; } } this.recommendations = recommendations; /*Delete the current recommendations stored in stored_story that has not been seen by the user*/ conn.deleteRecommendations((int) userId); /*Insert the 10 items we found*/ conn.insertUpdateRecommendValues(itemsToBeInserted); /*Drop our temporary view*/ conn.dropView(); conn.closeConnection(); } /** * Reading the story similarities from file and adding them to a collection of ItemItemSimilarity-objects * * @return the collection of ItemItemSimilarity-objects */ private Collection<ItemItemSimilarity> getStorySimilarities() { Collection<ItemItemSimilarity> res = new ArrayList<ItemItemSimilarity>(); BufferedReader br = null; try { /*This gets the path to the storytelling-backend folder*/ String url = fileLocation.getParentFile().getParentFile().getParentFile().toString(); br = new BufferedReader(new FileReader(new File(url + "/personalization/similarities.csv"))); String line = br.readLine(); while (line != null) { String[] values = line.split(","); long item1 = Long.parseLong(values[0]); long item2 = Long.parseLong(values[1]); double value = Double.parseDouble(values[2]); res.add(new ItemItemSimilarity(item1, item2, value)); line = br.readLine(); } } catch (Exception e) { e.printStackTrace(); } finally { try { br.close(); } catch (IOException e) { e.printStackTrace(); } } return res; } /** * Return the list of recommendations * * @return the list of recommendations created above */ public List<RecommendedItem> getRecommendations() { return this.recommendations; } }