Hybrid based Collaborative Filtering with Temporal Dynamics

Hybrid-based collaborative filters use some part or entire database relating to user preferences for making recommendations for new products and new users. In our time, it is of utmost importance to make recommendations in line with interests and demands of users by making their interest alive. However, although Hybrid-based collaborative filters are used in this area, changing of preferences of users in a time, emergence of new products and new users overshadow success of such systems. Traditional hybrid-based collaborative filtering (CF) technique become insufficient for responding interests and demands changing in a time. For this reason, temporal changes in recommendation systems become an important concept. Together with the study conducted, an appropriate and new method has been developed in line with changing pleasure and demands depending on time. In the recommended system, unlike traditional hybrid technique based CF technique, point given to the products depending on dates scored by users has been attempted to be estimated. In this study, process has been made over netflix data for measuring success of both traditional hybrid based CF technique and the recommended system. Quite successful and rewarding results have been obtained in the issue of accuracy of predicted points. Keywords- Recommendation System;, Data Mining; Temporal Dynamics

Collaborative Filtering with Temporal Dynamics with Using Singular Value Decomposition

Nowadays, Collaborative Filtering (CF) is a widely used recommendation system. However, traditional CF techniques are harder to make fast and accurate suggestions due to changes in user preferences over time, the emergence of new products and the availability of too many users and too many products in the system. Therefore, it becomes more important to make suggestions that are both fast and take the changes in time into consideration. In the presented study, a new method for providing suggestions customized according to the users\u27 preference and taste as they change over time was developed. By combining the time-dependent changes through the SVD (Singular Value Decomposition), a faster suggestion system was developed. Thus, an attempt was made to enhance product prediction success. In the present study all techniques on Netflix data and the results were compared. The results obtained on the accuracy of the predicted ratings were found out to be promising

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

The Method of Constructing Recommendations Online on the Temporal Dynamics of User Interests Using Multilayer Graph

The problem of the online construction of a rating list of objects in the recommender system is considered. A method for constructing recommendations online using the presentation of input data in the form of a multi-layer graph based on changes in user interests over time is proposed. The method is used for constructing recommendations in a situation with implicit feedback from the user. Input data are represented by a sequence of user choice records with a time stamp for each choice. The method includes the phases of pre-filtering of data and building recommendations by collaborative filtering of selected data. At pre-filtering of the input data, the subset of data is split into a sequence of fixed-length non-overlapping time intervals. Users with similar interests and records with objects of interest to these users are selected on a finite continuous subset of time intervals. In the second phase, the pre-filtered subset of data is used, which allows reducing the computational costs of generating recommendations. The method allows increasing the efficiency of building a rating list offered to the target user by taking into account changes in the interests of the user over time

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