Discovery of Actionable Patterns in Databases: The Action Hierarchy Approach

An approach to defining actionability as a measure of interestingness of patterns is proposed. This approach is based on the concept of an action hierarchy which is defined as a tree of actions with patterns and pattern templates (data mining queries) assigned to its nodes. A method for discovering actionable patterns is presented and various techniques for optimizing the discovery process are proposed.Information Systems Working Papers Serie

RQL: A Query Language For Recommender Systems

Initially popularized by Amazon.com, recommendation technologies have become widespread over the past several years, both in the industry and academia. The traditional two-dimensional approach to recommender systems, involving the dimensions of Users and Items, has been subsequently extended to the multidimensional approach supporting additional contextual dimensions and OLAP-type aggregation capabilities. Furthermore, the class of all possible recommendations available to the users in traditional recommender systems is typically determined by the vendor and is quite limited. In this paper we address this limitation by proposing a query language RQL that allows the users to formulate various types of recommendation requests on their own. RQL adapts OLAP queries to the domain of recommender systems and, therefore, is able to support both the traditional two-dimensional and the more complex multidimensional recommender systems. The paper also presents a recommendation algebra that allows mapping RQL queries into the algebraic expressions for the query processing purposes. Finally, the paper presents a method for executing RQL queries.Information Systems Working Papers Serie

Design and Evaluation of Feedback Schemes for Multiattribute Procurement Auctions

Multiattribute auctions, which allow bids on multiple dimensions of the product, are IT-enabled sourcing mechanisms that increase the efficiency of procurement for configurable goods and services compared to price-only auctions. Given the strategic nature of procurement auctions, the amount of information concerning the buyer’s preferences that is disclosed to the suppliers has implications on the profits of the buyer and suppliers and, consequently, on the long-term relationship between them. This study develops novel feedback schemes for multiattribute auctions that protect buyer’s preference information from the supplier and suppliers’ cost schedule from the buyer. We conduct a laboratory experiment to study bidder behavior and profit implications under three different feedback regimes. Our results indicate that bidders are able to extract more profit with more information regarding the state of the auction in terms of provisional allocation and prices. Furthermore, bidding behavior is substantially influenced by the nature and type of feedback

Toward Understanding the Dynamics of Bidder Behavior in Continuous Combinatorial Auctions: Agent-Based Simulation Approach

Combinatorial auctions represent sophisticated market mechanisms that are becoming increasingly important in various business applications due to their ability to improve economic efficiency and auction revenue, especially in settings where participants tend to exhibit more complex user preferences and valuations. While recent studies on such auctions have found heterogeneity in bidder behavior and its varying effect on auction outcomes, the area of bidder behavior and its impact on economic outcomes in combinatorial auctions is still largely underexplored. One of the main reasons is that it is nearly impossible to control for the type of bidder behavior in real world or experimental auction setups. We propose an agent-based modeling approach to replicate human bidder behavior in continuous combinatorial auctions and leverage our agents to simulate a wide variety of competition types, including experimentally unobserved ones that could not otherwise be studied. The capabilities of the proposed approach enable more comprehensive studies (via richer controlled experiments) of bidding behavior in the complex and highly dynamic decision environment of continuous combinatorial auctions

Toward the Next Generation of Recommender Systems: A Survey of the State-of-the-Art and Possible Extensions

This paper presents an overview of the field of recommender systems and describes the current generation of recommendation methods that are usually classified into the following three main categories: content-based, collaborative, and hybrid recommendation approaches. This paper also describes various limitations of current recommendation methods and discusses possible extensions that can improve recommendation capabilities and make recommender systems applicable to an even broader range of applications. These extensions include, among others, an improvement of understanding of users and items, incorporation of the contextual information into the recommendation process, support for multcriteria ratings, and a provision of more flexible and less intrusive types of recommendations

Maximizing Aggregate Recommendation Diversity: A GraphTheoretic Approach

Recommender systems are being used to help users find relevant items from a large set of alternatives in many online applications. Most existing recommendation techniques have focused on improving recommendation accuracy; however, diversity of recommendations has also been increasingly recognized in research literature as an important aspect of recommendation quality. This paper proposes a graph-theoretic approach for maximizing aggregate recommendation diversity based on maximum flow or maximum bipartite matching computations. The proposed approach is evaluated using real-world movie rating datasets and demonstrates substantial improvements in both diversity and accuracy, as compared to the recommendation re-ranking approaches, which have been introduced in prior literature for the purpose of diversity improvement

Heterogeneous Electric Vehicle Charging Coordination: A Variable Charging Speed Approach

We present a coordination mechanism that reduces peak demand coming from EV charging, supports grid stability and environmental sustainability. The proposed mechanism accounts for individual commuting preferences, as well as desired states of charge by certain deadlines, which can serve as a proxy for range anxiety. It can shape EV charging toward a desired profile, without violating individual preferences. Our mechanism mitigates herding, which is typical in populations where all agents receive the same price signals and make similar charging decisions. Furthermore, it assumes no prior knowledge about EV customers and therefore learns preferences and reactions to prices dynamically. We show through simulations that our mechanism induces a less volatile demand and lower peaks compared to currently used benchmarks

Design and Effects of Information Feedback in Continuous Combinatorial Auctions

Advancements in information technologies offer opportunities for designing and deploying innovative market mechanisms. For example, combinatorial auctions, in which bidders can bid on combinations of goods, can increase the economic efficiency of a trade when goods have complementarities. However, lack of real-time bidder support tools has been a major obstacle preventing this mechanism from reaching its full potential. This study uses novel feedback mechanisms to aid bidders in formulating bids in real-time to facilitate participation in continuous combinatorial auctions. Laboratory experiments examine the effectiveness of our feedback mechanisms; the study is the first to examine how bidders behave in such information-rich environments. Our results indicate that feedback results in higher efficiency and higher seller’s revenue compared to the baseline case where bidders are not provided feedback. Furthermore, contrary to conventional wisdom, even in complex economic environments, individuals effectively integrate rich information in their decision making