Sistem Rekomendasi dengan Teknik Faktorisasi Matriks dan Temporal Dynamics Berbasis Collaborative Filtering

ABSTRAKSI: Recommender system merupakan sebuah aplikasi yang memberikan rekomendasi kepada user berupa prediksi rating terhadap sebuah item berdasarkan karakteristik user dalam memberikan informasi.Tugas akhir ini mengimplementasikan dan menganalisis metode Faktorisasi Matriks pada item yang berbasis Temporal Dyamics pada sistem rekomendasi. Tugas akhir ini menganalisis pengaruh jumlah faktor fitur yang tersembunyi dan faktor waktu terhadap akurasi prediksi rating yang dihasilkan oleh recommender system setelah diimplementasikan metode Faktorisasi Matriks dan Temporal Dynamics berbasis Collaborative Filtering. Parameter yang digunakan dalam analisis adalah parameter k, penggunaan atribut time dan parameter To pada metode time weight collaborative filtering (penerapan Temporal Dynamics).Pada metode Faktorisasi Matriks, prediksi dilakukan dengan menggunakan dekomposisi matriks yang meng-generate matriks awal menjadi dua buah matriks yang kemudian saling dikalikan. Hasil perkalian matriks tersebut diolah dengan parameter faktor k, kemudian menghasilkan matriks baru sebagai hasil learning dengan nilai yang mendekati nilai matriks aslinya.Metode Collaborative Filtering yang mengadaptasi Temporal Dynamics menggunakan parameter time (usia item) untuk membantu menentukan prediksi rating. Dengan menggunakan metode Faktorisasi Matriks, rata-rata MAE dapat mencapai 0.64 dan menggunakan parameter nilai feature k yang paling optimal adalah 10. Sedangkan bila menggunakan Collaborative Filtering dengan Temporal Dynamics dengan parameter time, MAE dapat dihasilkan hingga mencapai 0.88. Ukuran data mempunyai pengaruh terhadap kinerja sistem dan akurasi prediksi. Semakin besar data, kompleksitas yang dibutuhkan sistem semakin tinggi.Kata Kunci : recommender system, metode Faktorisasi Matriks, Temporal DynamicsABSTRACT: Recommender system is an application that provides recommendations to the user a prediction rating of an item based on user characteristics in providing information.The final task is to implement and analyze the matrix factorization method based on items Temporal Dynamics on recommendation systems. The final task is to analyze the influence of the number of features that are hidden factors and time factors on the prediction accuracy of the ratings produced by the Recommender system once implemented method of matrix factorization and Temporal Dynamics-based Collaborative Filtering. The parameters used in the analysis is the parameter k, the use of time and parameters To attribute the weight-time method of collaborative filtering (implementation of Temporal Dynamics).In the matrix factorization method, prediction is done using a matrix decomposition to generate the initial matrix into two matrices are then multiplied together. The results of matrix multiplication is processed by the parameters k factors, then generate a new matrix as a result of learning with a value near the value of the original matrix.Collaborative filtering method that adapts Temporal Dynamics using the parameters of time (age of user) to help determine the predictive rating. Premises using matrix factorization method, the average MAE can reach 0.64 and using the parameter values k feature the most optimum is 10. Whereas when using Collaborative Filtering with Temporal Dynamics with time parameters, MAE can be generated up to 0.88. The size of the data have an influence on system performance and prediction accuracy. The larger the data, the complexity of the system becomes higher.Keyword: Recommender systems, matrix factorization method, Temporal Dynamic

Interacting Attention-gated Recurrent Networks for Recommendation

Capturing the temporal dynamics of user preferences over items is important for recommendation. Existing methods mainly assume that all time steps in user-item interaction history are equally relevant to recommendation, which however does not apply in real-world scenarios where user-item interactions can often happen accidentally. More importantly, they learn user and item dynamics separately, thus failing to capture their joint effects on user-item interactions. To better model user and item dynamics, we present the Interacting Attention-gated Recurrent Network (IARN) which adopts the attention model to measure the relevance of each time step. In particular, we propose a novel attention scheme to learn the attention scores of user and item history in an interacting way, thus to account for the dependencies between user and item dynamics in shaping user-item interactions. By doing so, IARN can selectively memorize different time steps of a user's history when predicting her preferences over different items. Our model can therefore provide meaningful interpretations for recommendation results, which could be further enhanced by auxiliary features. Extensive validation on real-world datasets shows that IARN consistently outperforms state-of-the-art methods.Comment: Accepted by ACM International Conference on Information and Knowledge Management (CIKM), 201

Large-Scale User Modeling with Recurrent Neural Networks for Music Discovery on Multiple Time Scales

The amount of content on online music streaming platforms is immense, and most users only access a tiny fraction of this content. Recommender systems are the application of choice to open up the collection to these users. Collaborative filtering has the disadvantage that it relies on explicit ratings, which are often unavailable, and generally disregards the temporal nature of music consumption. On the other hand, item co-occurrence algorithms, such as the recently introduced word2vec-based recommenders, are typically left without an effective user representation. In this paper, we present a new approach to model users through recurrent neural networks by sequentially processing consumed items, represented by any type of embeddings and other context features. This way we obtain semantically rich user representations, which capture a user's musical taste over time. Our experimental analysis on large-scale user data shows that our model can be used to predict future songs a user will likely listen to, both in the short and long term.Comment: Author pre-print version, 20 pages, 6 figures, 4 table