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Internet of Things (IoT) is an advancing technology that is a network of many smart devices connected together to provide services to various application domains such as smart offices, health monitoring, agriculture, etc. IoT-based recommendation technologies are becoming one of the key requirements that will recommend future IoT solutions. A review of existing recommendation technologies in the vibrant field of IoT is discussed in this paper. The main aim of this paper is to present a comprehensive analysis of existing literature on recommendation approaches. Several issues of applying recommendation systems to IoT are also discussed. Nearly 1000 research papers have been considered for analyses which are published by ACM, Springer, IEEE and Science Direct from 2011 to 2017. Finally, the recent research trends are spotted for future researchers intended to work in the recommendation-based IoT domain. Moreover, this paper also envisages the future of the Recommender System (RS) that opens up the newest research directions for young researchers.

State-of-the-art music recommender systems are based on collaborative filtering, which builds upon learning similarities between users and songs from the available listening data. These approaches inherently face the cold-start problem, as they cannot recommend novel songs with no listening history. Content-aware recommendation addresses this issue by incorporating content information about the songs on top of collaborative filtering. However, methods falling in this category rely on a shallow user/item interaction that originates from a matrix factorization framework. In this work, we introduce neural content-aware collaborative filtering, a unified framework which alleviates these limits, and extends the recently introduced neural collaborative filtering to its content-aware counterpart. This model leverages deep learning for both extracting content information from low-level acoustic features and for modeling the interaction between users and songs embeddings. The deep content feature extractor can either directly predict the item embedding, or serve as a regularization prior, yielding two variants (strict and relaxed) of our model. Experimental results show that the proposed method reaches state-of-the-art results for both warm- and cold-start music recommendation tasks. We notably observe that exploiting deep neural networks for learning refined user/item interactions outperforms approaches using a more simple interaction model in a content-aware framework.

In low-light image enhancement, prevailing Retinex-based methods often struggle with precise illumination estimation and brightness modulation. This can result in issues such as halo artifacts, blurred edges, and diminished details in bright regions, particularly under non-uniform illumination conditions. We propose an innovative approach that refines low-light images by leveraging an in-depth awareness of local content within the image. By introducing multi-scale effective guided filtering, our method surpasses the limitations of traditional isotropic filters, such as Gaussian filters, in handling non-uniform illumination. It dynamically adjusts regularization parameters in response to local image characteristics and significantly integrates edge perception across different scales. This balanced approach achieves a harmonious blend of smoothing and detail preservation, enabling more accurate illumination estimation. Additionally, we have designed an adaptive gamma correction function that dynamically adjusts the brightness value based on local pixel intensity, further balancing enhancement effects across different brightness levels in the image. Experimental results demonstrate the effectiveness of our proposed method for non-uniform illumination images across various scenarios. It exhibits superior quality and objective evaluation scores compared to existing methods. Our method effectively addresses potential issues that existing methods encounter when processing non-uniform illumination images, producing enhanced images with precise details and natural, vivid colors.