Predictive User Modeling with Actionable Attributes

Different machine learning techniques have been proposed and used for modeling individual and group user needs, interests and preferences. In the traditional predictive modeling instances are described by observable variables, called attributes. The goal is to learn a model for predicting the target variable for unseen instances. For example, for marketing purposes a company consider profiling a new user based on her observed web browsing behavior, referral keywords or other relevant information. In many real world applications the values of some attributes are not only observable, but can be actively decided by a decision maker. Furthermore, in some of such applications the decision maker is interested not only to generate accurate predictions, but to maximize the probability of the desired outcome. For example, a direct marketing manager can choose which type of a special offer to send to a client (actionable attribute), hoping that the right choice will result in a positive response with a higher probability. We study how to learn to choose the value of an actionable attribute in order to maximize the probability of a desired outcome in predictive modeling. We emphasize that not all instances are equally sensitive to changes in actions. Accurate choice of an action is critical for those instances, which are on the borderline (e.g. users who do not have a strong opinion one way or the other). We formulate three supervised learning approaches for learning to select the value of an actionable attribute at an instance level. We also introduce a focused training procedure which puts more emphasis on the situations where varying the action is the most likely to take the effect. The proof of concept experimental validation on two real-world case studies in web analytics and e-learning domains highlights the potential of the proposed approaches

Robust and cost-effective approach for discovering action rules

The main goal of Knowledge Discovery in Databases is to find interesting and usable patterns, meaningful in their domain. Actionable Knowledge Discovery came to existence as a direct respond to the need of finding more usable patterns called actionable patterns. Traditional data mining and algorithms are often confined to deliver frequent patterns and come short for suggesting how to make these patterns actionable. In this scenario the users are expected to act. However, the users are not advised about what to do with delivered patterns in order to make them usable. In this paper, we present an automated approach to focus on not only creating rules but also making the discovered rules actionable. Up to now few works have been reported in this field which lacking incomprehensibility to the user, overlooking the cost and not providing rule generality. Here we attempt to present a method to resolving these issues. In this paper CEARDM method is proposed to discover cost-effective action rules from data. These rules offer some cost-effective changes to transferring low profitable instances to higher profitable ones. We also propose an idea for improving in CEARDM method

"How May I Help You?": Modeling Twitter Customer Service Conversations Using Fine-Grained Dialogue Acts

Given the increasing popularity of customer service dialogue on Twitter, analysis of conversation data is essential to understand trends in customer and agent behavior for the purpose of automating customer service interactions. In this work, we develop a novel taxonomy of fine-grained "dialogue acts" frequently observed in customer service, showcasing acts that are more suited to the domain than the more generic existing taxonomies. Using a sequential SVM-HMM model, we model conversation flow, predicting the dialogue act of a given turn in real-time. We characterize differences between customer and agent behavior in Twitter customer service conversations, and investigate the effect of testing our system on different customer service industries. Finally, we use a data-driven approach to predict important conversation outcomes: customer satisfaction, customer frustration, and overall problem resolution. We show that the type and location of certain dialogue acts in a conversation have a significant effect on the probability of desirable and undesirable outcomes, and present actionable rules based on our findings. The patterns and rules we derive can be used as guidelines for outcome-driven automated customer service platforms.Comment: 13 pages, 6 figures, IUI 201

The EU’s Trade Policy in the Doha Development Agenda – An Interim Assessment on Rules Negotiations

At Doha Ministerial Conference in 2001, WTO members agreed to launch new trade negotiations on a range of subjects and other work, including issues concerning the implementation of the present agreements. Various issues in the WTO Doha Development Agenda were dealt with in the form of ‘single undertaking’ which include the trade remedy rules, i.e., anti-dumping and subsidies rules. The EU, being the largest regional economy in the world, was no doubt a heavyweight in the Doha multilateral trade negotiations and so was its trade policy of great weight. To date, the EU had put forward a total of 10 submissions to clarify and improve the AD Agreement and the SCM Agreement at the end of 2006, and the submissions revealed the EU’s attitude toward the Rules negoation; not aggressive but prudent and cautious. While Doha Round seemed doomed and gloomy, the EU, on the other hand, launched its new trade policy, the ‘Global Europe’ framework in 2006 pursuant to the goals set up by the conclusions of Lisbon European Council. The new EU’s trade policy is comprised of a wider array of trade issues, aiming at maintaining its global competitiveness, and in light of the growing fragmentation and complexity of the process of production and supply chains as well as the growth of major new economic actors, particularly in Asia, there was a need for a revision of the EU Trade Defence Instruments (TDI) . A “Green Paper” on TDI was thus drafted and presented for public consultation by the Commission at the end of 2006, which is intended to make sure EU TDI fit in the trend of globalization as well as the European multinational corporations' competiveness in the new economic context. This paper intends to explore if the possible trade policy adjustment in the EU TDI will also facilitate to resolve the discrepancy between the EU and its counterparts in the Rules negotiations and provide a solid basis for the conclusion thereof. Section II of the article presents the ongoing DDA negotiations, inter alia, Rules negotiations. Section III will probe the negotiation objective and issues that EU concern by examining its submissions to the Negotiating Group on Rules as well as its implementation assessment. The EU’s new trade policy, in particular, that on the newly released “Green Paper” on the TDI will also be analyzed in section IV. This paper concludes that the EU policy on TDI is expected to be adjusted toward a framework favorable to other economic operators, such as users and consumers. Whether the public consultation for “Green Paper” is a process of consensus building is still an argument. It is likely that EU delegate will narrow down the gap between the EU and other exporting-oriented members in the Rules negotiations should the revised TDI be expanded to a large extent

Exploring the Potential of Developmental Work Research and Change Laboratory to Support Sustainability Transformations:A Case Study of Organic Agriculture in Zimbabwe

This paper explores the emergence of transgressive learning in CHAT-informed development work research in a networked organic agriculture case study in Zimbabwe, based on intervention research involving district organic associations tackling interconnected issues of climate change, water, food security and solidarity. The study established that We change laboratories can be used to support transgressive learning through: confronting unproductive local norms; collective reframing of problematic issues; stimulating expansive learning and sustainability transformations in minds, relationships and landscapes across time. The study also confirms the need for fourth generation CHAT to address the complex social-ecological problems of today