Integrating Segmentation and Similarity in Melodic Analysis

The recognition of melodic structure depends on both the segmentation into structural units, the melodic motifs, and relations of motifs which are mainly determined by similarity. Existing models and studies of segmentation and motivic similarity cover only certain aspects and do not provide a comprehensive or coherent theory. In this paper an Integrated Segmentation and Similarity Model (ISSM) for melodic analysis is introduced. The ISSM yields an interpretation similar to a paradigmatic analysis for a given melody. An interpretation comprises a segmentation, assignments of related motifs and notes, and detailed information on the differences of assigned motifs and notes. The ISSM is based on generating and rating interpretations to find the most adequate one. For this rating a neuro-fuzzy-system is used, which combines knowledge with learning from data. The ISSM is an extension of a system for rhythm analysis. This paper covers the model structure and the features relevant for melodic and motivic analysis. Melodic segmentation and similarity ratings are described and results of a small experiment which show that the ISSM can learn structural interpretations from data and that integrating similarity improves segmentation performance of the model

A Faster Algorithm to Build New Users Similarity List in Neighbourhood-based Collaborative Filtering

Neighbourhood-based Collaborative Filtering (CF) has been applied in the industry for several decades, because of the easy implementation and high recommendation accuracy. As the core of neighbourhood-based CF, the task of dynamically maintaining users' similarity list is challenged by cold-start problem and scalability problem. Recently, several methods are presented on solving the two problems. However, these methods applied an $O(n^2)$ algorithm to compute the similarity list in a special case, where the new users, with enough recommendation data, have the same rating list. To address the problem of large computational cost caused by the special case, we design a faster ($O(\frac{1}{125}n^2)$) algorithm, TwinSearch Algorithm, to avoid computing and sorting the similarity list for the new users repeatedly to save the computational resources. Both theoretical and experimental results show that the TwinSearch Algorithm achieves better running time than the traditional method

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