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

Social Information Processing in Social News Aggregation

The rise of the social media sites, such as blogs, wikis, Digg and Flickr among others, underscores the transformation of the Web to a participatory medium in which users are collaboratively creating, evaluating and distributing information. The innovations introduced by social media has lead to a new paradigm for interacting with information, what we call 'social information processing'. In this paper, we study how social news aggregator Digg exploits social information processing to solve the problems of document recommendation and rating. First, we show, by tracking stories over time, that social networks play an important role in document recommendation. The second contribution of this paper consists of two mathematical models. The first model describes how collaborative rating and promotion of stories emerges from the independent decisions made by many users. The second model describes how a user's influence, the number of promoted stories and the user's social network, changes in time. We find qualitative agreement between predictions of the model and user data gathered from Digg.Comment: Extended version of the paper submitted to IEEE Internet Computing's special issue on Social Searc

Unravelling the dynamics of online ratings

Online product ratings are an immensely important source of information for consumers and accordingly a strong driver of commerce. Nonetheless, interpreting a particular rating in context can be very challenging. Ratings show significant variation over time, so understanding the reasons behind that variation is important for consumers, platform designers, and product creators. In this paper we contribute a set of tools and results that help shed light on the complexity of ratings dynamics. We consider multiple item types across multiple ratings platforms, and use a interpretable model to decompose ratings in a manner that facilitates comprehensibility. We show that the various kinds of dynamics observed in online ratings are largely understandable as a product of the nature of the ratings platform, the characteristics of the user population, known trends in ratings behavior, and the influence of recommendation systems. Taken together, these results provide a framework for both quantifying and interpreting the factors that drive the dynamics of online ratings.Published versio

Recommender Systems

The ongoing rapid expansion of the Internet greatly increases the necessity of effective recommender systems for filtering the abundant information. Extensive research for recommender systems is conducted by a broad range of communities including social and computer scientists, physicists, and interdisciplinary researchers. Despite substantial theoretical and practical achievements, unification and comparison of different approaches are lacking, which impedes further advances. In this article, we review recent developments in recommender systems and discuss the major challenges. We compare and evaluate available algorithms and examine their roles in the future developments. In addition to algorithms, physical aspects are described to illustrate macroscopic behavior of recommender systems. Potential impacts and future directions are discussed. We emphasize that recommendation has a great scientific depth and combines diverse research fields which makes it of interests for physicists as well as interdisciplinary researchers.Comment: 97 pages, 20 figures (To appear in Physics Reports

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

Fast Differentially Private Matrix Factorization

Differentially private collaborative filtering is a challenging task, both in terms of accuracy and speed. We present a simple algorithm that is provably differentially private, while offering good performance, using a novel connection of differential privacy to Bayesian posterior sampling via Stochastic Gradient Langevin Dynamics. Due to its simplicity the algorithm lends itself to efficient implementation. By careful systems design and by exploiting the power law behavior of the data to maximize CPU cache bandwidth we are able to generate 1024 dimensional models at a rate of 8.5 million recommendations per second on a single PC

From Amateurs to Connoisseurs: Modeling the Evolution of User Expertise through Online Reviews

Recommending products to consumers means not only understanding their tastes, but also understanding their level of experience. For example, it would be a mistake to recommend the iconic film Seven Samurai simply because a user enjoys other action movies; rather, we might conclude that they will eventually enjoy it -- once they are ready. The same is true for beers, wines, gourmet foods -- or any products where users have acquired tastes: the `best' products may not be the most `accessible'. Thus our goal in this paper is to recommend products that a user will enjoy now, while acknowledging that their tastes may have changed over time, and may change again in the future. We model how tastes change due to the very act of consuming more products -- in other words, as users become more experienced. We develop a latent factor recommendation system that explicitly accounts for each user's level of experience. We find that such a model not only leads to better recommendations, but also allows us to study the role of user experience and expertise on a novel dataset of fifteen million beer, wine, food, and movie reviews.Comment: 11 pages, 7 figure