Matrix Factorization Techniques for Recommender Systems

User modeling and user adapted interaction

Cognitive radios are expected to play a major role towards meeting the exploding traffic demand over wireless systems. A cognitive radio node senses the environment, analyzes the outdoor parameters, and then makes decisions for dynamic time-frequency-space resource allocation and management to improve the utilization of the radio spectrum. For efficient real-time process, the cognitive radio is usually combined with artificial intelligence and machine-learning techniques so that an adaptive and intelligent allocation is achieved. This paper firstly presents the cognitive radio networks, resources, objectives, constraints, and challenges. Then, it introduces artificial intelligence and machine-learning techniques and emphasizes the role of learning in cognitive radios. Then, a survey on the state-of-the-art of machine-learning techniques in cognitive radios is presented. The literature survey is organized based on different artificial intelligence techniques such as fuzzy logic, genetic algorithms, neural networks, game theory, reinforcement learning, support vector machine, case-based reasoning, entropy, Bayesian, Markov model, multi-agent systems, and artificial bee colony algorithm. This paper also discusses the cognitive radio implementation and the learning challenges foreseen in cognitive radio applications.

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Recent advances on artificial intelligence and learning techniques in cognitive radio networks

Recommendation system (RS) is a technology that provides accurate recommendations to users However, it is not comprehensive to only consider the accuracy of the recommendation because users have different requirements To improve the comprehensive performance, this paper presents a hybrid recommendation model based on many-objective optimization, which can simultaneously optimize the accuracy, diversity, novelty and coverage of recommendation This model enhances the robustness of recommendations by mixing three different basic recommendation technologies Additionally, we solve it with many-objective evolutionary algorithm (MaOEA) and test it extensively Experimental results demonstrate the effectiveness of the presented model, which can provide the recommendations with more and novel items on the basis of accurate and diverse

A hybrid recommendation system with many-objective evolutionary algorithm

A many-objective optimization recommendation algorithm based on knowledge mining

systems or recommendation systems are a subset of information filtering system that used to anticipate the 'evaluation' or 'preference' that user would feed to an item. In recent years E-commerce applications are widely using Recommender system. Generally the most popular E- commerce sites are probably music, news, books, research articles, and products. Recommender systems are also available for business experts, jokes, restaurants, financial services, life insurance and twitter followers. Recommender systems have formulated in parallel with the web. Initially Recommender systems were based on demographic, content-based filtering and collaborative filtering. Currently, these systems are incorporating social information for enhancing a quality of recommendation process. For betterment of recommendation process in the future, Recommender systems will use personal, implicit and local information from the Internet. This paper provides an overview of recommender systems that include collaborative filtering, content-based filtering and hybrid approach of recommender system.

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Survey on Collaborative Filtering, Content-based Filtering and Hybrid Recommendation System

Opportunistic spectrum access using cognitive radio technology enables exploring vacant licensed spectrum bands and thereby improving the spectrum utilization. However, it will have a significant impact on upper layer performance like routing as the reliable knowledge of topology and channel statistics are not available, especially in Mobile Cognitive Radio Ad hoc Network (MCRAN). To address specific requirements of MCRAN, this paper is proposing online opportunistic routing algorithm using multi-agent reinforcement learning. The proposed routing scheme jointly addresses, link and relay selection based on transmission success probabilities. This sophisticated learning mechanism successfully explores opportunities in partially observable and non-stationary environment of MCRAN. Simulation results show the effectiveness of this algorithm.

Multi-Agent Reinforcement Learning Based Opportunistic Routing and Channel Assignment for Mobile Cognitive Radio Ad Hoc Network

Recent exploration in Cognitive Radio Network proved itself as emerging paradigm to attempt the underutilization of wireless spectrum. Routing is challenging problem due to intermittent spectrum availability and incomplete knowledge of environment. This paper proposes reinforcement learning based combined framework of channel selection and routing for multi-hop cognitive radio network. Reinforcement learning is generic method for resource utilization in a partially observable and non-stationary environment. In this paper, channel selection and routing is modeled as Markao Decision Process to design the methodology of learning the best resource allocation policies adopted in the process state, based on the feedback received from the environment. First the design of the reward, transition and value function is described which helps in evolving the policy for selecting channel which results in increased spectrum utilization. The routing strategy is described which is exploring different state-action pair to come up with various routing solution which are ranked according to their reinforcement signal. Overhead of rerouting is also minimized by providing backup routes. Agent experiences in the form of reinforcement signal can be used by each cognitive node to further refine the routing strategies.

Dynamic channel selection and routing through reinforcement learning in Cognitive Radio Networks

In an application such as literature recommendation, we require a comprehensive recommender model that can generate relevant recommendations similar to the literature provided in the input query. In this paper, we have proposed a novel content-based recommender system based on Latent Dirichlet Allocation (LDA) and Jensen-Shannon distance, which can be used specifically for the task of literature recommendations. We have compared this model with the standard cosine-similarity based approach for its use to generate scientific publication recommendations, in which recommend suitable journals/conferences to publish a research work based on the abstract of the user's manuscript as an input. We evaluated the results of both the proposed model and standard cosine-similarity based approach over unseen documents and achieved a precision score of 62.58% while the standard cosine-similarity based approach achieved a precision of only around 48%.

A novel content-based recommendation approach based on LDA topic modeling for literature recommendation

Today's wireless networks are characterized by a fixed spectrum assignment policy. However, a large portion of the assigned spectrum is used sporadically and geographical variations in the utilization of assigned spectrum ranges from 15% to 85% with a high variance in time. The limited available spectrum and the inefficiency in the spectrum usage necessitate a new communication paradigm to exploit the existing wireless spectrum opportunistically. In contrast to current systems where the spectrum allocation is static, future cognitive radio devices will be able to seek and use in a dynamic way the frequencies for network access; this will be done by autonomous detection of vacant bands in the radio spectrum. This new networking paradigm is referred to as NeXt Generation Networks or Dynamic Spectrum Access (DSA) or cognitive radio networks. In this paper the novel functionalities and current research challenges of the cognitive networks are explained in detail. More specifically, a brief overview of the cognitive radio technology is provided and the cognitive network architecture is introduced. Moreover, the cognitive network functions such as spectrum management, spectrum mobility and spectrum sharing are explained in detail. Open research issues in these areas are also outlined.

Sunita Barve

Papers

Survey on Collaborative Filtering, Content-based Filtering and Hybrid Recommendation System

Multi-Agent Reinforcement Learning Based Opportunistic Routing and Channel Assignment for Mobile Cognitive Radio Ad Hoc Network

Dynamic channel selection and routing through reinforcement learning in Cognitive Radio Networks

A novel content-based recommendation approach based on LDA topic modeling for literature recommendation

A cognitive approach to spectrum sensing in virtual unlicensed wireless network