An Analysis of Memory Based Collaborative Filtering Recommender Systems with Improvement Proposals

Memory Based Collaborative Filtering Recommender Systems have been around for the best part of the last twenty years. It is a mature technology, implemented in nu- merous commercial applications. However, a departure from Memory Based systems, in favour of Model Based systems happened during the last years. The Net ix.com competition of 2006, brought the Model Based paradigm to the spotlight, with plenty of research that followed. Still, these matrix factorization based algorithms are hard to compute, and cumbersome to update. Memory Based approaches, on the other hand, are simple, fast, and self explanatory. We posit that there are still uncomplicated approaches that can be applied to improve this family of Recommender Systems further. Four strategies aimed at improving the Accuracy of Memory Based Collaborative Filtering Recommender Systems have been proposed and extensively tested. The strategies put forward include an Average Item Voting approach to infer missing rat- ings, an Indirect Estimation algorithm which pre-estimates the missing ratings before computing the overall recommendation, a Class Type Grouping strategy to lter out items of a class di erent than the target one, and a Weighted Ensemble consisting of an average of an estimation computed with all samples, with one obtained via the Class Type Grouping approach. This work will show that there is still ample space to improve Memory Based Systems, and raise their Accuracy to the point where they can compete with state- of-the-art Model Based approaches such as Matrix Factorization or Singular Value Decomposition techniques, which require considerable processing power, and generate models that become obsolete as soon as users add new ratings into the system

The Role of the Mangement Sciences in Research on Personalization

We present a review of research studies that deal with personalization. We synthesize current knowledge about these areas, and identify issues that we envision will be of interest to researchers working in the management sciences. We take an interdisciplinary approach that spans the areas of economics, marketing, information technology, and operations. We present an overarching framework for personalization that allows us to identify key players in the personalization process, as well as, the key stages of personalization. The framework enables us to examine the strategic role of personalization in the interactions between a firm and other key players in the firm's value system. We review extant literature in the strategic behavior of firms, and discuss opportunities for analytical and empirical research in this regard. Next, we examine how a firm can learn a customer's preferences, which is one of the key components of the personalization process. We use a utility-based approach to formalize such preference functions, and to understand how these preference functions could be learnt based on a customer's interactions with a firm. We identify well-established techniques in management sciences that can be gainfully employed in future research on personalization.CRM, Persoanlization, Marketing, e-commerce,

Efficient Decision Support Systems

This series is directed to diverse managerial professionals who are leading the transformation of individual domains by using expert information and domain knowledge to drive decision support systems (DSSs). The series offers a broad range of subjects addressed in specific areas such as health care, business management, banking, agriculture, environmental improvement, natural resource and spatial management, aviation administration, and hybrid applications of information technology aimed to interdisciplinary issues. This book series is composed of three volumes: Volume 1 consists of general concepts and methodology of DSSs; Volume 2 consists of applications of DSSs in the biomedical domain; Volume 3 consists of hybrid applications of DSSs in multidisciplinary domains. The book is shaped upon decision support strategies in the new infrastructure that assists the readers in full use of the creative technology to manipulate input data and to transform information into useful decisions for decision makers

Semantic discovery and reuse of business process patterns

Patterns currently play an important role in modern information systems (IS) development and their use has mainly been restricted to the design and implementation phases of the development lifecycle. Given the increasing significance of business modelling in IS development, patterns have the potential of providing a viable solution for promoting reusability of recurrent generalized models in the very early stages of development. As a statement of research-in-progress this paper focuses on business process patterns and proposes an initial methodological framework for the discovery and reuse of business process patterns within the IS development lifecycle. The framework borrows ideas from the domain engineering literature and proposes the use of semantics to drive both the discovery of patterns as well as their reuse

Transmedia Strategies for Participatory Politics in Russia: Alexey Navalny’s Grassroots Campaign

Transmedia storytelling scholarship has been progressing rapidly over recent decades. Yet, a question that remains open is the lack of analysis of political transmedia campaigns. This political communication thesis contributes to filling that gap. Its goal is to develop a flexible and locally-specific approach to analysing transmedia political campaigning. To understand the context that affects the destinies of transmedia grassroots campaigns, the study turns to social movement and grassroots activism scholarships. In particular, it employs the idea of political opportunity structures to conceptualise those external opportunities and threats that affects transmedia campaigns in politics. To mitigate the negative aspects of a political climate, reduce the costs of political participation for active citizens and make their political change efforts more efficient, political organisers can mobilise valuable resources through transmedia campaigning, the thesis argues. Thus, the thesis incorporates the analysis of opportunity structures and mobilising resources to propose a new analytical approach to the study of political transmedia campaigns. Because this analytical approach reinterprets transmedia strategies through the lens of opportunity structures and resource mobilisation, I will refer to it in the thesis as the opportunity structures and mobilising resources (OSMR) analytical approach. The thesis tests it with the case study of Alexey Navalny\u27s 2013 mayoral campaign for Moscow. The case study outlines the opportunity structures of modern Russia and discusses the transmedia strategies Navalny’s campaign used to overcome some of their negative aspects. In doing so, the thesis enriches transmedia storytelling scholarship with insights from other disciplines and offers a flexible and locally specific approach to analysing political transmedia campaigns

State of the art 2015: a literature review of social media intelligence capabilities for counter-terrorism

Overview This paper is a review of how information and insight can be drawn from open social media sources. It focuses on the specific research techniques that have emerged, the capabilities they provide, the possible insights they offer, and the ethical and legal questions they raise. These techniques are considered relevant and valuable in so far as they can help to maintain public safety by preventing terrorism, preparing for it, protecting the public from it and pursuing its perpetrators. The report also considers how far this can be achieved against the backdrop of radically changing technology and public attitudes towards surveillance. This is an updated version of a 2013 report paper on the same subject, State of the Art. Since 2013, there have been significant changes in social media, how it is used by terrorist groups, and the methods being developed to make sense of it.  The paper is structured as follows: Part 1 is an overview of social media use, focused on how it is used by groups of interest to those involved in counter-terrorism. This includes new sections on trends of social media platforms; and a new section on Islamic State (IS). Part 2 provides an introduction to the key approaches of social media intelligence (henceforth ‘SOCMINT’) for counter-terrorism. Part 3 sets out a series of SOCMINT techniques. For each technique a series of capabilities and insights are considered, the validity and reliability of the method is considered, and how they might be applied to counter-terrorism work explored. Part 4 outlines a number of important legal, ethical and practical considerations when undertaking SOCMINT work

Making sense of strangers' expertise from digital artifacts

In organizations, individuals typically rely on their personal networks to obtain expertise when faced with ill-defined problems that require answers that are beyond the scope of their own knowledge. However, individuals cannot always get the needed expertise from their local colleagues. This issue is particularly acute for members in large geographically dispersed organizations since it is difficult to know ?who knows what? among numerous colleagues. The proliferation of social computing technologies such as blogs, online forums, social tags and bookmarks, and social network connection information have expanded the reach and ease at which knowledge workers may become aware of others? expertise. While all these technologies facilitate access to a stranger that can potentially provide needed expertise or advice, there has been little theoretical work on how individuals actually go about this process. I refer to the process of gathering complex, changing and potentially equivocal information, and comprehending it by connecting nuggets of information from many sources to answer vague, non-procedural questions as the process of ?sensemaking?. Through a study of 81 fulltime IBM employees in 21 countries, I look at how existing models and theories of sensemaking and information search may be inadequate to describe the ?people sensemaking? process individuals go through when considering contacting strangers for expertise. Using signaling theory as an interpretive framework, I describe how certain ?signals? in various social software are hard to fake, and are thus more reliable indicators of expertise, approachability, and responsiveness. This research has the potential to inform models of sensemaking and information search when the search is for people, as opposed to documents

User decision improvement and trust building in product recommender systems

As online stores are offering an almost unlimited shelf space, users must increasingly rely on product search and recommender systems to find their most preferred products and decide which item is the truly best one to buy. However, much research work has emphasized on developing and improving the underlying algorithms whereas many of the user issues such as preference elicitation and trust formation received little attention. In this thesis, we aim at designing and evaluating various decision technologies, with emphases on how to improve users' decision accuracy with intelligent preference elicitation and revision tools, and how to build their competence-inspired subjective constructs via trustworthy recommender interfaces. Specifically, two primary technologies are proposed: one is called example critiquing agents aimed to stimulate users to conduct tradeoff navigation and freely specify feedback criteria to example products; another termed as preference-based organization interfaces designed to take two roles: explaining to users why and how the recommendations are computed and displayed, and suggesting critique suggestions to guide users to understand existing tradeoff potentials and to make concrete decision navigations from the top candidate for better choices. To evaluate the two technologies' true performance and benefits to real-users, an evaluation framework was first established, that includes important assessment standards such as the objective/subjective accuracy-effort measures and trust-related subjective aspects (e.g., competence perceptions and behavioral intentions). Based on the evaluation framework, a series of nine experiments has been conducted and most of them were participated by real-users. Three user studies focused on the example critiquing (EC) agent, which first identified the significant impact of tradeoff process with the help of EC on users' decision accuracy improvement, and then in depth explored the advantage of multi-item strategy (for critiquing coverage) against single-item display, and higher user-control level reflected by EC in supporting users to freely compose critiquing criteria for both simple and complex tradeoffs. Another three experiments studied the preference-based organization technique. Regarding its explanation role, a carefully conducted user survey and a significant-scale quantitative evaluation both demonstrated that it can be likely to increase users' competence perception and return intention, and reduce their cognitive effort in information searching, relative to the traditional "why" explanation method in ranked list views. In addition, a retrospective simulation revealed its superior algorithm accuracy in predicting critiques and product choices that real-users intended to make, in comparison with other typical critiquing generation approaches. Motivated by the empirically findings in terms of the two technologies' respective strengths, a hybrid system has been developed with the purpose of combining them into a single application. The final three experiments evaluated its two design versions and particularly validated the hybrid system's universal effectiveness among people from different types of cultural backgrounds: oriental culture and western culture. In the end, a set of design guidelines is derived from all of the experimental results. They should be helpful for the development of a preference-based recommender system, making it capable of practically benefiting its users in improving decision accuracy, expending effort they are willing to invest, and even promoting trust in the system with resulting behavioral intentions to purchase chosen products and return to the system for repeated uses