Report on the Information Retrieval Festival (IRFest2017)

The Information Retrieval Festival took place in April 2017 in Glasgow. The focus of the workshop was to bring together IR researchers from the various Scottish universities and beyond in order to facilitate more awareness, increased interaction and reflection on the status of the field and its future. The program included an industry session, research talks, demos and posters as well as two keynotes. The first keynote was delivered by Prof. Jaana Kekalenien, who provided a historical, critical reflection of realism in Interactive Information Retrieval Experimentation, while the second keynote was delivered by Prof. Maarten de Rijke, who argued for more Artificial Intelligence usage in IR solutions and deployments. The workshop was followed by a "Tour de Scotland" where delegates were taken from Glasgow to Aberdeen for the European Conference in Information Retrieval (ECIR 2017

Breakthroughs in Shared Measurement and Social Impact

A surprising new breakthrough is emerging in the social sector: A handful of innovative organizations have developed web-based systems for reporting the performance, measuring the outcomes, and coordinating the efforts of hundreds or even thousands of social enterprises within a field. These nascent efforts carry implications well beyond performance measurement, foreshadowing the possibility of profound changes in the vision and effectiveness of the entire nonprofit sector. This paper, based on six months of interviews and research by FSG Social Impact Advisors, examines twenty efforts to develop shared approaches to performance, outcome, or impact measurement across multiple organizations. The accompanying appendices include a short description of each system and four more in-depth case studies

Binary Particle Swarm Optimization based Biclustering of Web usage Data

Web mining is the nontrivial process to discover valid, novel, potentially useful knowledge from web data using the data mining techniques or methods. It may give information that is useful for improving the services offered by web portals and information access and retrieval tools. With the rapid development of biclustering, more researchers have applied the biclustering technique to different fields in recent years. When biclustering approach is applied to the web usage data it automatically captures the hidden browsing patterns from it in the form of biclusters. In this work, swarm intelligent technique is combined with biclustering approach to propose an algorithm called Binary Particle Swarm Optimization (BPSO) based Biclustering for Web Usage Data. The main objective of this algorithm is to retrieve the global optimal bicluster from the web usage data. These biclusters contain relationships between web users and web pages which are useful for the E-Commerce applications like web advertising and marketing. Experiments are conducted on real dataset to prove the efficiency of the proposed algorithms

Layered evaluation of interactive adaptive systems : framework and formative methods

Peer reviewedPostprin

What attracts vehicle consumers’ buying:A Saaty scale-based VIKOR (SSC-VIKOR) approach from after-sales textual perspective?

Purpose: The increasingly booming e-commerce development has stimulated vehicle consumers to express individual reviews through online forum. The purpose of this paper is to probe into the vehicle consumer consumption behavior and make recommendations for potential consumers from textual comments viewpoint. Design/methodology/approach: A big data analytic-based approach is designed to discover vehicle consumer consumption behavior from online perspective. To reduce subjectivity of expert-based approaches, a parallel Naïve Bayes approach is designed to analyze the sentiment analysis, and the Saaty scale-based (SSC) scoring rule is employed to obtain specific sentimental value of attribute class, contributing to the multi-grade sentiment classification. To achieve the intelligent recommendation for potential vehicle customers, a novel SSC-VIKOR approach is developed to prioritize vehicle brand candidates from a big data analytical viewpoint. Findings: The big data analytics argue that “cost-effectiveness” characteristic is the most important factor that vehicle consumers care, and the data mining results enable automakers to better understand consumer consumption behavior. Research limitations/implications: The case study illustrates the effectiveness of the integrated method, contributing to much more precise operations management on marketing strategy, quality improvement and intelligent recommendation. Originality/value: Researches of consumer consumption behavior are usually based on survey-based methods, and mostly previous studies about comments analysis focus on binary analysis. The hybrid SSC-VIKOR approach is developed to fill the gap from the big data perspective

Design and Implementation of a Web Usage Mining Model Based On Upgrowth and Preflxspan

Web Usage Mining (WUM) integrates the techniques of two popular research fields - Data Mining and the Internet. By analyzing the potential rules hidden in web logs, WUM helps personalize the delivery of web content and improve web design, customer satisfaction and user navigation through pre-fetching and caching. This paper introduces two prevalent data mining algorithms - FPgrowth and PrefixSpan into WUM and they are applied in a real business case. Maximum Forward Path (MFP) is also used in the web usage mining model during sequential pattern mining along with PrefixSpan so as to reduce the interference of false visit caused by browser cache and raise the accuracy of mining frequent traversal paths. Detailed analysis and application on the corresponding results are discussed

Web Mining Functions in an Academic Search Application

This paper deals with Web mining and the different categories of Web mining like content, structure and usage mining. The application of Web mining in an academic search application has been discussed. The paper concludes with open problems related to Web mining. The present work can be a useful input to Web users, Web Administrators in a university environment.Database, HITS, IR, NLP, Web mining

Intelligent Security for Phishing Online using Adaptive Neuro Fuzzy Systems

Anti-phishing detection solutions employed in industry use blacklist-based approaches to achieve low false-positive rates, but blacklist approaches utilizes website URLs only. This study analyses and combines phishing emails and phishing web-forms in a single framework, which allows feature extraction and feature model construction. The outcome should classify between phishing, suspicious, legitimate and detect emerging phishing attacks accurately. The intelligent phishing security for online approach is based on machine learning techniques, using Adaptive Neuro-Fuzzy Inference System and a combination sources from which features are extracted. An experiment was performed using two-fold cross validation method to measure the system’s accuracy. The intelligent phishing security approach achieved a higher accuracy. The finding indicates that the feature model from combined sources can detect phishing websites with a higher accuracy. This paper contributes to phishing field a combined feature which sources in a single framework. The implication is that phishing attacks evolve rapidly; therefore, regular updates and being ahead of phishing strategy is the way forward