CRUC: Cold-start Recommendations Using Collaborative Filtering in Internet of Things

The Internet of Things (IoT) aims at interconnecting everyday objects (including both things and users) and then using this connection information to provide customized user services. However, IoT does not work in its initial stages without adequate acquisition of user preferences. This is caused by cold-start problem that is a situation where only few users are interconnected. To this end, we propose CRUC scheme - Cold-start Recommendations Using Collaborative Filtering in IoT, involving formulation, filtering and prediction steps. Extensive experiments over real cases and simulation have been performed to evaluate the performance of CRUC scheme. Experimental results show that CRUC efficiently solves the cold-start problem in IoT.Comment: Elsevier ESEP 2011: 9-10 December 2011, Singapore, Elsevier Energy Procedia, http://www.elsevier.com/locate/procedia/, 201

Analisis dan Implementasi Collaborative Filtering menggunakan Strategi Smoothing dan Fusing pada Recommender System

Collaborative Filtering (CF) adalah salah satu pendekatan yang populer untuk membangun Recommender System dengan memanfaatkan informasi dan preferensi dari user lain untuk memberikan rekomendasi item. Salah satu permasalahan mendasar dalam CF adalah data rating yang sangat sedikit (data sparsity) yang mampu mempengaruhi hasil rekomendasi. Secara umum terdapat dua tipe algoritma pada CF, yaitu memory-based dan model-based yang memiliki kelebihan dan kekurangan masing-masing. Dalam tugas akhir ini, digunakan strategi smoothing dan fusing yang merupakan pendekatan hybrid dari memory-based dan model-based untuk menangani permasalahan data sparsity. Berdasarkan hasil pengujian, strategi smoothing dan fusing mampu menurunkan error sistem yang diukur menggunakan MAE dari 2,277 menjadi 0,746 atau menurun sebesar 50.624% dibandingkan tanpa menggunakan strategi smoothing dan fusing. Selain itu, akurasi sistem juga dipengaruhi oleh level sparsity dari data rating. Semakin sparse data rating yang dimiliki, maka akurasi yang dihasilkan semakin buruk

Bibliographic Review on Distributed Kalman Filtering

In recent years, a compelling need has arisen to understand the effects of distributed information structures on estimation and filtering. In this paper, a bibliographical review on distributed Kalman filtering (DKF) is provided.\ud The paper contains a classification of different approaches and methods involved to DKF. The applications of DKF are also discussed and explained separately. A comparison of different approaches is briefly carried out. Focuses on the contemporary research are also addressed with emphasis on the practical applications of the techniques. An exhaustive list of publications, linked directly or indirectly to DKF in the open literature, is compiled to provide an overall picture of different developing aspects of this area

Revisiting Graph based Collaborative Filtering: A Linear Residual Graph Convolutional Network Approach

Graph Convolutional Networks (GCNs) are state-of-the-art graph based representation learning models by iteratively stacking multiple layers of convolution aggregation operations and non-linear activation operations. Recently, in Collaborative Filtering (CF) based Recommender Systems (RS), by treating the user-item interaction behavior as a bipartite graph, some researchers model higher-layer collaborative signals with GCNs. These GCN based recommender models show superior performance compared to traditional works. However, these models suffer from training difficulty with non-linear activations for large user-item graphs. Besides, most GCN based models could not model deeper layers due to the over smoothing effect with the graph convolution operation. In this paper, we revisit GCN based CF models from two aspects. First, we empirically show that removing non-linearities would enhance recommendation performance, which is consistent with the theories in simple graph convolutional networks. Second, we propose a residual network structure that is specifically designed for CF with user-item interaction modeling, which alleviates the over smoothing problem in graph convolution aggregation operation with sparse user-item interaction data. The proposed model is a linear model and it is easy to train, scale to large datasets, and yield better efficiency and effectiveness on two real datasets. We publish the source code at https://github.com/newlei/LRGCCF.Comment: The updated version is publised in AAAI 202

Performance Study of the Robust Bayesian Regularization Technique for Remote Sensing Imaging in Geophysical Applications

In this paper, a performance study of a methodology for reconstruction of high-resolution remote sensing imagery is presented. This method is the robust version of the Bayesian regularization (BR) technique, which performs the image reconstruction as a solution of the ill-conditioned inverse spatial spectrum pattern (SSP) estimation problem with model uncertainties via unifying the Bayesian minimum risk (BMR) estimation strategy with the maximum entropy (ME) randomized a priori image model and other projection-type regularization constraints imposed on the solution. The results of extended comparative simulation study of a family of image formation/enhancement algorithms that employ the RBR method for high-resolution reconstruction of the SSP is presented. Moreover, the computational complexity of different methods are analyzed and reported together with the scene imaging protocols. The advantages of the remote sensing imaging experiment (that employ the BR-based estimator) over the cases of poorer designed experiments (that employ the conventional matched spatial filtering as well as the least squares techniques) are verified trough the simulation study. Finally, the application of this estimator in geophysical applications of remote sensing imagery is described.Universidad de Guadalajar