18,658 research outputs found

    A Channel Coding Perspective of Collaborative Filtering

    Full text link
    We consider the problem of collaborative filtering from a channel coding perspective. We model the underlying rating matrix as a finite alphabet matrix with block constant structure. The observations are obtained from this underlying matrix through a discrete memoryless channel with a noisy part representing noisy user behavior and an erasure part representing missing data. Moreover, the clusters over which the underlying matrix is constant are {\it unknown}. We establish a sharp threshold result for this model: if the largest cluster size is smaller than C1log⁡(mn)C_1 \log(mn) (where the rating matrix is of size m×nm \times n), then the underlying matrix cannot be recovered with any estimator, but if the smallest cluster size is larger than C2log⁡(mn)C_2 \log(mn), then we show a polynomial time estimator with diminishing probability of error. In the case of uniform cluster size, not only the order of the threshold, but also the constant is identified.Comment: 32 pages, 1 figure, Submitted to IEEE Transactions on Information Theor

    Message-Passing Inference on a Factor Graph for Collaborative Filtering

    Full text link
    This paper introduces a novel message-passing (MP) framework for the collaborative filtering (CF) problem associated with recommender systems. We model the movie-rating prediction problem popularized by the Netflix Prize, using a probabilistic factor graph model and study the model by deriving generalization error bounds in terms of the training error. Based on the model, we develop a new MP algorithm, termed IMP, for learning the model. To show superiority of the IMP algorithm, we compare it with the closely related expectation-maximization (EM) based algorithm and a number of other matrix completion algorithms. Our simulation results on Netflix data show that, while the methods perform similarly with large amounts of data, the IMP algorithm is superior for small amounts of data. This improves the cold-start problem of the CF systems in practice. Another advantage of the IMP algorithm is that it can be analyzed using the technique of density evolution (DE) that was originally developed for MP decoding of error-correcting codes

    Single integrated device for optical CDMA code processing in dual-code environment

    Get PDF
    We report on the design, fabrication and performance of a matching integrated optical CDMA encoder-decoder pair based on holographic Bragg reflector technology. Simultaneous encoding/decoding operation of two multiple wavelength-hopping time-spreading codes was successfully demonstrated and shown to support two error-free OCDMA links at OC-24. A double-pass scheme was employed in the devices to enable the use of longer code length

    Graph Signal Processing: Overview, Challenges and Applications

    Full text link
    Research in Graph Signal Processing (GSP) aims to develop tools for processing data defined on irregular graph domains. In this paper we first provide an overview of core ideas in GSP and their connection to conventional digital signal processing. We then summarize recent developments in developing basic GSP tools, including methods for sampling, filtering or graph learning. Next, we review progress in several application areas using GSP, including processing and analysis of sensor network data, biological data, and applications to image processing and machine learning. We finish by providing a brief historical perspective to highlight how concepts recently developed in GSP build on top of prior research in other areas.Comment: To appear, Proceedings of the IEE

    Peak to average power ratio based spatial spectrum sensing for cognitive radio systems

    Get PDF
    The recent convergence of wireless standards for incorporation of spatial dimension in wireless systems has made spatial spectrum sensing based on Peak to Average Power Ratio (PAPR) of the received signal, a promising approach. This added dimension is principally exploited for stream multiplexing, user multiplexing and spatial diversity. Considering such a wireless environment for primary users, we propose an algorithm for spectrum sensing by secondary users which are also equipped with multiple antennas. The proposed spatial spectrum sensing algorithm is based on the PAPR of the spatially received signals. Simulation results show the improved performance once the information regarding spatial diversity of the primary users is incorporated in the proposed algorithm. Moreover, through simulations a better performance is achieved by using different diversity schemes and different parameters like sensing time and scanning interval
    • 

    corecore