PENETRATE: Personalized news recommendation using ensemble hierarchical clustering

Recommending online news articles has become a promising research direction as the Internet provides fast access to real-time information from multiple sources around the world. Many online readers have their own reading preference on news articles; however, a group of users might be interested in similar fascinating topics. It would be helpful to take into consideration the individual and group reading behavior simultaneously when recommending news items to online users. In this paper, we propose PENETRATE, a novel PErsonalized NEws recommendaTion framework using ensemble hieRArchical clusTEring to provide attractive recommendation results. Specifically, given a set of online readers, our approach initially separates readers into different groups based on their reading histories, where each user might be designated to several groups. Once a collection of newly-published news items is provided, we can easily construct a news hierarchy for each user group. When recommending news articles to a given user, the hierarchies of multiple user groups that the user belongs to are merged into an optimal one. Finally a list of news articles are selected from this optimal hierarchy based on the user’s personalized information, as the recommendation result. Extensive empirical experiments on a set of news articles collected from various popular news websites demonstrate the efficacy of our proposed approach.     

Research and trends in the field of e-learning from 2001 to 2005: A content analysis of cognitive studies in selected journals

This paper provided a content analysis of studies in the field of cognition in e-learning that were published in five Social Sciences Citation Index (SSCI) journals (i.e. Computers and Education, British Journal of Educational Technology, Innovations in Education and Teaching International, Educational Technology Research & Development, and Journal of Computer Assisted Learning) from 2001 to 2005. Among the 1027 articles published in these journals from 2001 to 2005, 444 articles were identified as being related to the topic of cognition in e-learning. These articles were cross analyzed by published years, journal, research topic, and citation count. Furthermore, 16 highly-cited articles across different topics were chosen for further analysis according to their research settings, participants, research design types, and research methods. It was found from the analysis of the 444 articles that “Instructional Approaches,” “Learning Environment,” and “Metacognition” were the three most popular research topics, but the analysis of the citation counts suggested that the studies related to “Instructional Approaches,” “Information Processing” and “Motivation” might have a greater impact on subsequent research. Although the use of questionnaires might still be the main method of gathering research data in e-learning cognitive studies, a clear trend was observed that more and more studies were utilizing learners’ log files or online messages as data sources for analysis. The results of the analysis provided insights for educators and researchers into research trends and patterns of cognition in e-learning.     

Towards a journalist-based news recommendation system: The Wesomender approach

The present paper introduces a context-aware recommendation system for journalists to enable the identification of similar topics across different sources. More specifically a journalist-based recommendation system that can be automatically configured is presented to exploit news according to expert preferences. News contextual features are also taken into account due to the their special nature: time, current user interests, location or existing trends are combined with traditional recommendation techniques to provide an adaptive framework that deals with heterogeneous data providing an enhanced collaborative filtering system. Since the Wesomender approach is able to generate context-aware recommendations in the journalism field, a quantitative evaluation with the aim of comparing Wesomender results with the expectations of a team of experts is also performed to show that a context-aware adaptive recommendation engine can fulfil the needs of journalists daily work when retrieving timely and primary information is required.     

Wiki Scaffolding: Aligning wikis with the corporate strategy

Wikis are main exponents of collaborative development by user communities. This community may be created around the wiki itself (e.g., community of contributors in Wikipedia) or already exist (e.g., company employees in corporate wikis). In the latter case, the wiki is not created in a vacuum but as part of the information ecosystem of the hosting organization. As any other Information System resource, wiki success highly depends on the interplay of technology, work practice and the organization. Thus, wiki contributions should be framed along the concerns already in use in the hosting organization in terms of glossaries, schedules, policies, organigrams and the like. The question is then, how can corporate strategies permeate wiki construction while preserving wiki openness and accessibility? We advocate for the use of “Wiki Scaffoldings”, i.e., a wiki installation that is provided at the onset to mimic these corporate concerns: categories, users, templates, articles initialized with boilerplate text, are all introduced in the wiki before any contribution is made. To retain wikis' friendliness and engage layman participation, we propose scaffoldings to be described as mind maps. Mind maps are next “exported” as wiki installations. We show the feasibility of the approach introducing a Wiki Scaffolding Language (WSL). WSL is realized as a plugin for FreeMind, a popular tool for mind mapping. Finally, we validate the expressiveness of WSL in four case studies. WSL is available for download.     

User comments and public opinion: Findings from an eye-tracking experiment

Following a study that demonstrated that user comments have a strong impact on public opinion, Popular Science magazine decided to disable its user comments option. Prompted by this dramatic decision, this study used an eye-tracking experiment (N = 197) to study the popularity of user comments, and the effects of pre-existing opinions, readership patterns and the tone of user comments on the evaluation of news articles. Despite vast research utilizing eye tracking to study online behavior, very few previous studies engaged with online news consumption. This is the first eye tracking study to test for a correlation between reading user comments and evaluating a news story article. Although more than 40% read the user comments, the most significant and persistent predictor of readers' evaluations of the articles were their pre-existing opinions about the articles' theme, while readership had no effect on the articles' evaluation. Follow-up interviews demonstrate that readers commonly view user comments as a realm characterized by biases and commercialization.     

Personalized News Recommendation: A Review and an Experimental Investigation

Online news articles,as a new format of press releases,have sprung up on the Internet.With its convenience and recency,more and more people prefer to read news online instead of reading the paper-format press releases.However,a gigantic amount of news events might be released at a rate of hundreds,even thousands per hour.A challenging problem is how to efficiently select specific news articles from a large corpus of newly-published press releases to recommend to individual readers,where the selected news items should match the reader’s reading preference as much as possible.This issue refers to personalized news recommendation.Recently,personalized news recommendation has become a promising research direction as the Internet provides fast access to real-time information from multiple sources around the world.Existing personalized news recommendation systems strive to adapt their services to individual users by virtue of both user and news content information.A variety of techniques have been proposed to tackle personalized news recommendation,including content-based,collaborative filtering systems and hybrid versions of these two.In this paper,we provide a comprehensive investigation of existing personalized news recommenders.We discuss several essential issues underlying the problem of personalized news recommendation,and explore possible solutions for performance improvement.Further,we provide an empirical study on a collection of news articles obtained from various news websites,and evaluate the effect of different factors for personalized news recommendation.We hope our discussion and exploration would provide insights for researchers who are interested in personalized news recommendation.     

Volume discounting coordinates a supply chain effectively when demand is sensitive to both price and sales effort

In this paper, we use a simple and parsimonious model to investigate the performance of volume discounting schemes (hereafter “[VD]”) in a supply chain where the market demand is sensitive to both retail price “p” and sales effort “e” — hereafter called a “(p,e)-channel.” The problem is analyzed as a manufacturer-leading Stackelberg game. We first present, for the deterministic-system-parameter situation, contract-designing procedures under two contract formats; namely, a “regular” version of [VD] (hereafter “[RVD]”) and a “continuous” version of [VD] (hereafter “[CVD]”). Our solutions show that [RVD] cannot perfectly coordinate this (p,e)-sensitive channel; moreover, very often [RVD] leads to a lower channel efficiency than the simple price-only contract. In contrast, we show that [CVD] leads to perfect channel coordination — a significant result since most contract formats have been shown in the literature to be unable to coordinate a (p,e)-channel. Next, we consider the more realistic situations in which the manufacturer is uncertain about one of the system parameters — specifically, either the market size “a” or the effort cost “η”. Our results show that, if Manu is uncertain about a, [RVD] becomes useless but the manufacturer can still use [CVD] to benefit himself. When the manufacturer is uncertain about η, [CVD] remains useful (as expected); however, surprisingly, [RVD] can outperform [CVD] when both the mean value and the uncertainty of η are sufficient high. These results underline the necessity of evaluating a contract format under various forms of system-parameter uncertainties — often at the expense of analytical tractability.     

Attribute selection-based recommendation framework for short-head user group: An empirical study by MovieLens and IMDB

Most of recommender systems have serious difficulties on providing relevant services to the “short-head” users who have shown intermixed preferential patterns. In this paper, we assume that such users (which are referred to as long-tail users) can play an important role of information sources for improving the performance of recommendation. Attribute reduction-based mining method has been proposed to efficiently select the long-tail user groups. More importantly, the long-tail user groups as domain experts are employed to provide more trustworthy information. To evaluate the proposed framework, we have integrated MovieLens dataset with IMDB, and empirically shown that the long-tail user groups are useful for the recommendation process.     

Web news mining in an evolving framework

Online news has become one of the major channels for Internet users to get news. News websites are daily overwhelmed with plenty of news articles. Huge amounts of online news articles are generated and updated everyday, and the processing and analysis of this large corpus of data is an important challenge. This challenge needs to be tackled by using big data techniques which process large volume of data within limited run times. Also, since we are heading into a social-media data explosion, techniques such as text mining or social network analysis need to be seriously taken into consideration.In this work we focus on one of the most common daily activities: web news reading. News websites produce thousands of articles covering a wide spectrum of topics or categories which can be considered as a big data problem. In order to extract useful information, these news articles need to be processed by using big data techniques. In this context, we present an approach for classifying huge amounts of different news articles into various categories (topic areas) based on the text content of the articles. Since these categories are constantly updated with new articles, our approach is based on Evolving Fuzzy Systems (EFS). The EFS can update in real time the model that describes a category according to the changes in the content of the corresponding articles. The novelty of the proposed system relies in the treatment of the web news articles to be used by these systems and the implementation and adjustment of them for this task. Our proposal not only classifies news articles, but it also creates human interpretable models of the different categories. This approach has been successfully tested using real on-line news.     

Personalized rough-set-based recommendation by integrating multiple contents and collaborative information

In recent years, explosively-growing information makes the users confused in making decisions among various kinds of products such as music, movies, books, etc. As a result, it is a challenging issue to help the user identify what she/he prefers. To this end, so called recommender systems are proposed to discover the implicit interests in user’s mind based on the usage logs. However, the existing recommender systems suffer from the problems of cold-start, first-rater, sparsity and scalability. To alleviate such problems, we propose a novel recommender, namely FRSA (Fusion of Rough-Set and Average-category-rating) that integrates multiple contents and collaborative information to predict user’s preferences based on the fusion of Rough-Set and Average-category-rating. Through the integrated mining of multiple contents and collaborative information, our proposed recommendation method can successfully reduce the gap between the user’s preferences and the automated recommendations. The empirical evaluations reveal that the proposed method, FRSA, can associate the recommended items with user’s interests more effectively than other existing well-known ones in terms of accuracy.     

A collaborative user-centered framework for recommending items in Online Social Networks

Recommender Systems are more and more playing an important role in our life, representing useful tools helping users to find “what they need” from a very large number of candidates and supporting people in making decisions in various contexts: what items to buy, which movie to watch, or even who they can invite to their social network, etc. In this paper, we propose a novel collaborative user-centered recommendation approach in which several aspects related to users and available in Online Social Networks – i.e. preferences (usually in the shape of items’ metadata), opinions (textual comments to which it is possible to associate a sentiment), behavior (in the majority of cases logs of past items’ observations made by users), feedbacks (usually expressed in the form of ratings) – are considered and integrated together with items’ features and context information within a general framework that can support different applications using proper customizations (e.g., recommendation of news, photos, movies, travels, etc.). Experiments on system accuracy and user satisfaction in several domains shows how our approach provides very promising and interesting results.     

On discovery of soft associations with “most” fuzzy quantifier for item promotion applications

In item promotion applications, there is a strong need for tools that can help to unlock the hidden profit within each individual customer’s transaction history. Discovering association patterns based on the data mining technique is helpful for this purpose. However, the conventional association mining approach, while generating “strong” association rules, cannot detect potential profit-building opportunities that can be exposed by “soft” association rules, which recommend items with looser but significant enough associations. This paper proposes a novel mining method that automatically detects hidden profit-building opportunities through discovering soft associations among items from historical transactions. Specifically, this paper proposes a relaxation method of association mining with a new support measurement, called soft support, that can be used for mining soft association patterns expressed with the “most” fuzzy quantifier. In addition, a novel measure for validating the soft-associated rules is proposed based on the estimated possibility of a conditioned quantified fuzzy event. The new measure is shown to be effective by comparison with several existing measures. A new association mining algorithm based on modification of the FT-Tree algorithm is proposed to accommodate this new support measure. Finally, the mining algorithm is applied to several data sets to investigate its effectiveness in finding soft patterns and content recommendation.     

Posting,commenting, and tagging: Effects of sharing news stories on Facebook

Social networking sites such as Facebook provide new ways of sharing news stories that allow users to act as opinion leaders in their networks, encourage discussion, and potentially increase their involvement in current events. This study identifies the particular features of Facebook that facilitate the discussion of news and tests their effects on involvement and feelings of influence. Participants (N = 265) in a 3 (Broadcast level: news feed vs. wall post vs. direct message) × 3 (Elaboration: opinion vs. question vs. no comment) × 2 (Involving-friends: tag vs. no tag) between-subjects factorial experiment were randomly assigned to share a story from a news website on Facebook. Results show that user involvement in the news content depends on the social affordances of the site, particularly those that allow for audience customization and those that drive network feedback. Asking the network’s opinions and targeting specific friends led to greater involvement in the news content. Discussion through comments led to a greater sense of influence and greater involvement for those sharing the news story. These findings highlight the importance of encouraging individuals to act as sources of information in their networks to drive engagement in current events in the changing news landscape.     

Regranulation: A granular algorithm enabling communication between granular worlds

In this paper, we describe a granular algorithm for translating information between two granular worlds, represented as fuzzy rulebases. These granular worlds are defined on the same universe of discourse, but employ different granulations of this universe. In order to translate information from one granular world to the other, we must regranulate the information so that it matches the information granularity of the target world. This is accomplished through the use of a first-order interpolation algorithm, implemented using linguistic arithmetic, a set of elementary granular computing operations. We first demonstrate this algorithm by studying the common “fuzzy-PD” rulebase at several different granularities, and conclude that the “3 × 3” granulation may be too coarse for this objective. We then examine the question of what the “natural” granularity of a system might be; this is studied through a 10-fold cross-validation experiment involving three different granulations of the same underlying mapping. For the problem under consideration, we find that a 7 × 7 granulation appears to be the minimum necessary precision.     

News recommender system: a review of recent progress,challenges, and opportunities

Nowadays, more and more news readers read news online where they have access to millions of news articles from multiple sources. In order to help users find the right and relevant content, news recommender systems (NRS) are developed to relieve the information overload problem and suggest news items that might be of interest for the news readers. In this paper, we highlight the major challenges faced by the NRS and identify the possible solutions from the state-of-the-art. Our discussion is divided into two parts. In the first part, we present an overview of the recommendation solutions, datasets, evaluation criteria beyond accuracy and recommendation platforms being used in the NRS. We also talk about two popular classes of models that have been successfully used in recent years. In the second part, we focus on the deep neural networks as solutions to build the NRS. Different from previous surveys, we study the effects of news recommendations on user behaviors and try to suggest possible remedies to mitigate those effects. By providing the state-of-the-art knowledge, this survey can help researchers and professional practitioners have a better understanding of the recent developments in news recommendation algorithms. In addition, this survey sheds light on the potential new directions.

     

Cybernetics, system(s) theory, information theory and Fuzzy Sets and Systems in the 1950s and 1960s

About 60 years ago Norbert Wiener and Claude Elwood Shannon established the new scientific discipline of information theory. However, it is very probable that Shannon’s article A Mathematical Theory of Communication would not have become famous without the help of Warren Weaver, whose popular text on “The Mathematics of Communication” re-interpreted Shannon’s work for broader scientific audiences. Weaver’s “preface” and Shannon’s article were published together in the book The Mathematical Theory of Communication. Norbert Wiener’s Cybernetics was an even more popular event, when it appeared in print. However publications were influential on two scientific areas with concepts unmentioned or unelaborated within the texts themselves: Systems Theory and information theory. A “General System Theory” had already been created by Ludwig von Bertalanffy in the late 1920s for biological and philosophical research. This approach melded in North America in the 1950s with cybernetics, as well as a new system theoretical approach in engineering sciences in the 1950s. Bertalanffy’s “General System Theory” - or simply “systems theory” was used, became even more famous in humanities. In the 1960s attempts to yield both systems theory took root in the humanities, with mixed success.This paper will review the links across these fields showing the influences across cybernetics, system(s) theory and information theory throughout the 1950s and the theory of Fuzzy Sets and Systems. Then we focus to the non-technical but philosophical aspects of information theory. When Weaver emphasized not the technical but the semantic and influential problems of communication, his arguments were very similar to Charles W. Morris’ foundations of the Theory of Signs (1938) - Semiotics. We will show some interesting ideas of Weaver related to semiotic thinking and we will advocate a “fuzzy information theory” that has to be appropriate to cover this “semiotic concept of information”. Finally, the paper presents epistemological reflections in historical perspective on the concept of “information” as a “fluctuating object” that we take as a fuzzy concept.     

Code C# for chaos analysis of relativistic many-body systems

This work presents a new Microsoft Visual C# .NET code library, conceived as a general object oriented solution for chaos analysis of three-dimensional, relativistic many-body systems. In this context, we implemented the Lyapunov exponent and the “fragmentation level” (defined using the graph theory and the Shannon entropy). Inspired by existing studies on billiard nuclear models and clusters of galaxies, we tried to apply the virial theorem for a simplified many-body system composed by nucleons. A possible application of the “virial coefficient” to the stability analysis of chaotic systems is also discussed.

Program summary

Program title: Chaos Many-Body Engine v01Catalogue identifier: AEGH_v1_0Program summary URL:http://cpc.cs.qub.ac.uk/summaries/AEGH_v1_0.htmlProgram obtainable from: CPC Program Library, Queen's University, Belfast, N. IrelandLicensing provisions: Standard CPC licence, http://cpc.cs.qub.ac.uk/licence/licence.htmlNo. of lines in distributed program, including test data, etc.: 30 053No. of bytes in distributed program, including test data, etc.: 801 258Distribution format: tar.gzProgramming language: Visual C# .NET 2005Computer: PCOperating system: .Net Framework 2.0 running on MS WindowsHas the code been vectorized or parallelized?: Each many-body system is simulated on a separate execution threadRAM: 128 MegabytesClassification: 6.2, 6.5External routines: .Net Framework 2.0 LibraryNature of problem: Chaos analysis of three-dimensional, relativistic many-body systems.Solution method: Second order Runge-Kutta algorithm for simulating relativistic many-body systems. Object oriented solution, easy to reuse, extend and customize, in any development environment which accepts .Net assemblies or COM components. Implementation of: Lyapunov exponent, “fragmentation level”, “average system radius”, “virial coefficient”, and energy conservation precision test.Additional comments: Easy copy/paste based deployment method.Running time: Quadratic complexity.