Example Analysis of mahout Technology

In this issue, the editor will bring you an example analysis of mahout technology. The article is rich in content and analyzes and narrates it from a professional point of view. I hope you can get something after reading this article.

/ / first get all the books borrowed by the user PreferenceArray preferencesFromUser = getDataModel (). GetPreferencesFromUser (userID); / / get the books borrowed by the readers below, and those who have also borrowed those books, take all the books borrowed by the readers as candidates FastIDSet possibleItemsIDs = new FastIDSet (); for (long itemID: preferredItemIDs) {PreferenceArray itemPreferences = dataModel.getPreferencesForItem (itemID); int numUsersPreferringItem = itemPreferences.length (); for (int index = 0; index < numUsersPreferringItem) Index++) {possibleItemsIDs.addAll (dataModel.getItemIDsFromUser (itemPreferences.getUserID (index);}} possibleItemsIDs.removeAll (preferredItemIDs); / / calculate the similarity between all candidate items and every book the reader has borrowed, double [] similarities = getSimilarity (). ItemSimilarities (itemID, preferencesFromUser.getIDs ()); boolean foundAPref = false; double totalSimilarity = 0.0 For (double theSimilarity: similarities) {if (! Double.isNaN (theSimilarity)) {foundAPref = true; totalSimilarity + = theSimilarity;}} return foundAPref? (float) totalSimilarity: after Float.NaN; / /, take the 10 books with the highest similarity and return List topItems = TopItems.getTopItems (howMany, possibleItemIDs.iterator (), rescorer, estimator). The above is the analysis of the mahout technology shared by the editor. If you happen to have similar doubts, please refer to the above analysis for understanding. If you want to know more about it, you are welcome to follow the industry information channel.