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Collaborative Filtering Recommender Systems  [cached]
Mehrbakhsh Nilashi,Karamollah Bagherifard,Othman Ibrahim,Hamid Alizadeh
Research Journal of Applied Sciences, Engineering and Technology , 2013,
Abstract: Recommender Systems are software tools and techniques for suggesting items to users by considering their preferences in an automated fashion. The suggestions provided are aimed at support users in various decision-making processes. Technically, recommender system has their origins in different fields such as Information Retrieval (IR), text classification, machine learning and Decision Support Systems (DSS). Recommender systems are used to address the Information Overload (IO) problem by recommending potentially interesting or useful items to users. They have proven to be worthy tools for online users to deal with the IO and have become one of the most popular and powerful tools in E-commerce. Many existing recommender systems rely on the Collaborative Filtering (CF) and have been extensively used in E-commerce .They have proven to be very effective with powerful techniques in many famous E-commerce companies. This study presents an overview of the field of recommender systems with current generation of recommendation methods and examines comprehensively CF systems with its algorithms.
Applying Maxi-adjustment to Adaptive Information Filtering Agents  [PDF]
Raymond Lau,Arthur H. M. ter Hofstede,Peter D. Bruza
Computer Science , 2000,
Abstract: Learning and adaptation is a fundamental property of intelligent agents. In the context of adaptive information filtering, a filtering agent's beliefs about a user's information needs have to be revised regularly with reference to the user's most current information preferences. This learning and adaptation process is essential for maintaining the agent's filtering performance. The AGM belief revision paradigm provides a rigorous foundation for modelling rational and minimal changes to an agent's beliefs. In particular, the maxi-adjustment method, which follows the AGM rationale of belief change, offers a sound and robust computational mechanism to develop adaptive agents so that learning autonomy of these agents can be enhanced. This paper describes how the maxi-adjustment method is applied to develop the learning components of adaptive information filtering agents, and discusses possible difficulties of applying such a framework to these agents.
Dynamic Collaborative Filtering Recommender Model Based on Rolling Time Windows and its Algorithm

计算机科学 , 2013,
Abstract: For improving the performance of the traditional collaborative filtering recommender system, a dynamic user- item-time three-dimensional model based on rolling time windows was proposed, which considers the time seduence problem. I}hen a special collaborative filtering (CF) algorithm was explored to work with the model. 1}he interest scores at different times arc regarded differently according to the time sequence and the similarities between users arc com- posed of components at different times,which increases the timeliness of the algorithm. In addition, the similarities can also be calculated duickly by an incremental formula deduced in this paper so as to improve the scalability of the algo- rithm. At last, some reasonable experiments show that the model and algorithm presented in this paper outperform the traditional 2D collaborative filtering model and algorithm in terms of the hit rate.
FARS: Fuzzy Ant based Recommender System for Web Users
Shiva Nadi,Mohammad H. Saraee,Mohammad Davarpanah Jazi,Ayoub Bagheri
International Journal of Computer Science Issues , 2011,
Abstract: Recommender systems are useful tools which provide an adaptive web environment for web users. Nowadays, having a user friendly website is a big challenge in e-commerce technology. In this paper, applying the benefits of both collaborative and content based filtering techniques is proposed by presenting a fuzzy recommender system based on collaborative behavior of ants (FARS). FARS works in two phases: modeling and recommendation. First, user's behaviors are modeled offline and the results are used in second phase for online recommendation. Fuzzy techniques provide the possibility of capturing uncertainty among user interests and ant based algorithms provides us with optimal solutions. The performance of FARS is evaluated using log files of "Information and Communication Technology Center" of Isfahan municipality in Iran and compared with ant based recommender system (ARS). The results shown are promising and proved that integrating fuzzy Ant approach provides us with more functional and robust recommendations.
Proposing a New Metric for Collaborative Filtering  [PDF]
Arash Bahrehmand, Reza Rafeh
Journal of Software Engineering and Applications (JSEA) , 2011, DOI: 10.4236/jsea.2011.47047
Abstract: The aim of a recommender system is filtering the enormous quantity of information to obtain useful information based on the user’s interest. Collaborative filtering is a technique which improves the efficiency of recommendation systems by considering the similarity between users. The similarity is based on the given rating to data by similar users. However, user’s interest may change over time. In this paper we propose an adaptive metric which considers the time in measuring the similarity of users. The experimental results show that our approach is more accurate than the traditional collaborative filtering algorithm.
Adaptive Filtering Enhances Information Transmission in Visual Cortex  [PDF]
Tatyana O. Sharpee,Hiroki Sugihara,Andrei V. Kurgansky,Sergei P. Rebrik,Michael P. Stryker,Kenneth D. Miller
Quantitative Biology , 2006, DOI: 10.1038/nature04519
Abstract: Sensory neuroscience seeks to understand how the brain encodes natural environments. However, neural coding has largely been studied using simplified stimuli. In order to assess whether the brain's coding strategy depend on the stimulus ensemble, we apply a new information-theoretic method that allows unbiased calculation of neural filters (receptive fields) from responses to natural scenes or other complex signals with strong multipoint correlations. In the cat primary visual cortex we compare responses to natural inputs with those to noise inputs matched for luminance and contrast. We find that neural filters adaptively change with the input ensemble so as to increase the information carried by the neural response about the filtered stimulus. Adaptation affects the spatial frequency composition of the filter, enhancing sensitivity to under-represented frequencies in agreement with optimal encoding arguments. Adaptation occurs over 40 s to many minutes, longer than most previously reported forms of adaptation.
Heterogeneity, quality, and reputation in an adaptive recommendation model  [PDF]
Giulio Cimini,Matus Medo,Tao Zhou,Dong Wei,Yi-Cheng Zhang
Computer Science , 2010, DOI: 10.1140/epjb/e2010-10716-5
Abstract: Recommender systems help people cope with the problem of information overload. A recently proposed adaptive news recommender model [Medo et al., 2009] is based on epidemic-like spreading of news in a social network. By means of agent-based simulations we study a "good get richer" feature of the model and determine which attributes are necessary for a user to play a leading role in the network. We further investigate the filtering efficiency of the model as well as its robustness against malicious and spamming behaviour. We show that incorporating user reputation in the recommendation process can substantially improve the outcome.
Topic-focused Dynamic Information Filtering in Social Media  [PDF]
Yadong Zhu,Yanyan Lan,Jiafeng Guo,Xueqi Cheng
Computer Science , 2015,
Abstract: With the quick development of online social media such as twitter or sina weibo in china, many users usually track hot topics to satisfy their desired information need. For a hot topic, new opinions or ideas will be continuously produced in the form of online data stream. In this scenario, how to effectively filter and display information for a certain topic dynamically, will be a critical problem. We call the problem as Topic-focused Dynamic Information Filtering (denoted as TDIF for short) in social media. In this paper, we start open discussions on such application problems. We first analyze the properties of the TDIF problem, which usually contains several typical requirements: relevance, diversity, recency and confidence. Recency means that users want to follow the recent opinions or news. Additionally, the confidence of information must be taken into consideration. How to balance these factors properly in online data stream is very important and challenging. We propose a dynamic preservation strategy on the basis of an existing feature-based utility function, to solve the TDIF problem. Additionally, we propose new dynamic diversity measures, to get a more reasonable evaluation for such application problems. Extensive exploratory experiments have been conducted on TREC public twitter dataset, and the experimental results validate the effectiveness of our approach.
Adaptive filtering based on recurrent neural networks
Kihas Dejan,?urovi? ?eljko M.,Kova?evi? Branko D.
Journal of Automatic Control , 2003, DOI: 10.2298/jac0301013k
Abstract: Kalman filter is an optimal filtering solution in certain cases, however, it is more often than not, regarded as a non-robust filter. The slight mismatch in noise statistics or process model may lead to large performance deterioration and the loss of optimality. This research paper proposes an alternative method for robust adaptive filtering concerning lack of information of noise statistics. The method is based on the application of recurrent neural networks trained by a dynamic identity observer. The method is explained in details and tested in the case analysis of object tracking model. Performance evaluation is made for cases of the standard Kalman filter, a noise-adaptive Kalman filter, the adaptive filter with a recurrent neural network trained by a static identity observer, and the adaptive filter with recurrent neural network trained by a dynamic identity observer. The results for different noise statistics as well as noise statistics mismatches are compared and presented. It is shown that in cases with a lack of knowledge of the noise statistics it is beneficial to use the filtering method proposed in this research work.
Implementation of a Recommender System using Collaborative Filtering
Andrei-Cristian Prodan
Studia Universitatis Babes-Bolyai : Series Informatica , 2010,
Abstract: Nowadays, consumers have a lot of choices. Electronic retailers offer a great variety of products. Because of this, there is a need for Recommender Systems. These systems aim to solve the problem of matching consumers with the most appealing products for them. They do this by analyzing either the products information details (Content Based methods) or users social behavior (Collaborative Filtering). This paper describes the Collaborative Filtering technique in more detail. It then presents one of the best methods for CF: the Matrix Factorization technique. Next, it presents two algorithms used for matrix factorization. Then, the paper describes the implementation details of a framework created by us, called Rho, that uses Collaborative Filtering. In the end, we present some results obtained after experimenting with this framework.
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