Implementasi dan Analisa Effective Missing Data Prediction pada Collaborative Filtering Recommender System

ABSTRAKSI: Recommender system adalah sistem yang dapat digunakan untuk memprediksi sebuah items dalam hal ini berupa movie, berdasarkan informasi yang diperoleh dari user, sehingga didapatkan rekomendasi berdasarkan profil penggunanya. Collaborative filtering adalah sebuah metoda dari recommender system yang memprediksi suatu item (movie) berdasarkan informasi yang sudah ada dari user atau item lainnya. Untuk mendapatkan hasil prediksi yang maksimal dapat dihasilkan dengan perhitungan similarity baik dari user maupun dari item.Tugas akhir ini menganalisis akurasi prediksi rating yang dihasilkan oleh recommender system setelah mengimplementasikan algoritma effective missing data prediction collaborative filtering. Dimana dalam mendapatkan nilai prediksi dari item item yang belum di rating ini berdasarkan penghitungan dari similarity dari user dan item, beserta menggunakan teknik pembobotan significance weighting. Data yang digunakan adalah data set IMDB(Internet Movie Data Base). Parameter yang digunakan dalam analisis adalah parameter Gamma ,tao, theta, etha dan lambda. Tugas akhir ini menganalisa tingkat akurasi prediksi rating yang dihasilkan dengan metoda evaluasi MAE (Mean Absolut Error)Akurasi prediksi yang dihasilkan oleh algoritma effective missing data prediction collaborative filtering lebih baik dibandingkan dengan classic collaborative filtering. Performansi terbaik terjadi pada saat memprediksi missing data dengan menggunakan informasi dari user maupun item.Kata Kunci : recommender system, collaborative filtering, similarity, missing valueABSTRACT: Recommender system is a system that can be used to predict the items in this case a movie, based on information obtained from users, so get recommendations based on user profiles. Collaborative filtering is a method of recommender systems that predict an item (movie) based on existing information from users or other items. To get the maximum prediction calculation of similarity is required either from user or from the item.This final rating analyze prediction accuracy generated by the recommender system after implementing effevtive missing data prediction algorithm collaborative filtering. Where in obtaining the predicted value of the items items that have not been in the rating is based on the calculation of the similarity of users and items, along with significance weighting weighting technique. The data used is the data set of IMDB (Internet Movie Data Base). The parameters used in the analysis is the parameter Gamma, tao, theta, Ethan and lambda. This final project will analyze the level of prediction accuracy ratings generated by the evaluation method of MAE (Mean Absolute Error)Prediction accuracy generated by the missing data prediction algorithm effevtive collaborative filtering is better than classic collaborative filtering. Best performance occurs when predicting the missing data by using information from the user or item.Keyword: recommender systems, collaborative filtering, similarity, missing valu

COMPARING THE COLLABORATIVE FILTERING ALGORITHM WITH NAIVE BAYES ON THE FILM RECOMMENDATION SYSTEM

The many movies that are circulating and the many platforms that provide movie streaming platforms raise a question, namely what algorithm is the most suitable for use in providing movie recommendations. Of course, each of these streaming platforms uses different algorithms and factors. In this study the author tries to compare two algorithms in providing movie recommendations based on the rating factor. The algorithm used is Collaborative Filtering with Cosine Similarity and also nave Bayes. Both authors tested using a dataset from movieLens.org as much as 10,000 data. And in the results, Collaborative Filtering got better results through MSE and RMSE testing than nave Bayes. But the prediction score of each movie in each algorithm has a similar and the same score because it only uses the rating factor

Message-Passing Inference on a Factor Graph for Collaborative Filtering

This paper introduces a novel message-passing (MP) framework for the collaborative filtering (CF) problem associated with recommender systems. We model the movie-rating prediction problem popularized by the Netflix Prize, using a probabilistic factor graph model and study the model by deriving generalization error bounds in terms of the training error. Based on the model, we develop a new MP algorithm, termed IMP, for learning the model. To show superiority of the IMP algorithm, we compare it with the closely related expectation-maximization (EM) based algorithm and a number of other matrix completion algorithms. Our simulation results on Netflix data show that, while the methods perform similarly with large amounts of data, the IMP algorithm is superior for small amounts of data. This improves the cold-start problem of the CF systems in practice. Another advantage of the IMP algorithm is that it can be analyzed using the technique of density evolution (DE) that was originally developed for MP decoding of error-correcting codes

USING FILTERS IN TIME-BASED MOVIE RECOMMENDER SYSTEMS

On a very high level, a movie recommendation system is one which uses data about the user, data about the movie and the ratings given by a user in order to generate predictions for the movies that the user will like. This prediction is further presented to the user as a recommendation. For example, Netflix uses a recommendation system to predict movies and generate favorable recommendations for users based on their profiles and the profiles of users similar to them. In user-based collaborative filtering algorithm, the movies rated highly by the similar users of a particular user are considered as recommendations to that user. But users’ preferences vary with time, which often affects the efficacy of the recommendation, especially in a movie recommendation system. Because of the constant variation of the preferences, there has been research on using time of rating or watching the movie as a significant factor for recommendation. If time is considered as an attribute in the training phase of building a recommendation model, the model might get complex. Most of the research till now does this in the training phase, however, we study the effect of using time as a factor in the post training phase and study it further by applying a genre-based filtering mechanism on the system. Employing this in the post training phase reduces the complexity of the method and also reduces the number of irrelevant recommendations

Web based Recommender Systems and Rating Prediction

This project implements a recommender system on large dataset of Netflix’s movies. This project also tries to improve recommender systems by incorporating confidence interval and genres of movies. This new approach enhances the performance and quality of service of recommender systems and gives better result than Netflix commercial recommender system, Cinematch

Recommender Systems by means of Information Retrieval

In this paper we present a method for reformulating the Recommender Systems problem in an Information Retrieval one. In our tests we have a dataset of users who give ratings for some movies; we hide some values from the dataset, and we try to predict them again using its remaining portion (the so-called "leave-n-out approach"). In order to use an Information Retrieval algorithm, we reformulate this Recommender Systems problem in this way: a user corresponds to a document, a movie corresponds to a term, the active user (whose rating we want to predict) plays the role of the query, and the ratings are used as weigths, in place of the weighting schema of the original IR algorithm. The output is the ranking list of the documents ("users") relevant for the query ("active user"). We use the ratings of these users, weighted according to the rank, to predict the rating of the active user. We carry out the comparison by means of a typical metric, namely the accuracy of the predictions returned by the algorithm, and we compare this to the real ratings from users. In our first tests, we use two different Information Retrieval algorithms: LSPR, a recently proposed model based on Discrete Fourier Transform, and a simple vector space model