Stefan Reiterer

Toward the Next Generation of Recommender Systems: Applications and Research Challenges

Recommender systems are assisting users in the process of identifying items that fulfill their wishes and needs. These systems are successfully applied in different e-commerce settings, for example, to the recommendation of news, movies, music, books, and digital cameras. The major goal of this book chapter is to discuss new and upcoming applications of recommendation technologies and to provide an outlook on major characteristics of future technological developments. Based on a literature analysis, we discuss new and upcoming applications in domains such as software engineering, data and knowledge engineering, configurable items, and persuasive technologies. Thereafter we sketch major properties of the next generation of recommendation technologies.

Basic Approaches in Recommendation Systems

Recommendation systems support users in finding items of interest. In this chapter, we introduce the basic approaches of collaborative filtering, content-based filtering, and knowledge-based recommendation. We first discuss principles of the underlying algorithms based on a running example. Thereafter, we provide an overview of hybrid recommendation approaches which combine basic variants. We conclude this chapter with a discussion of newer algorithmic trends, especially critiquing-based and group recommendation.

Counteracting Serial Position Effects in the CHOICLA Group Decision Support Environment

Decisions are often suboptimal due to the fact that humans apply simple heuristics which cause different types of decision biases. CHOICLA is an environment that supports decision making for groups of users. It supports the determination of recommendations for groups and also includes mechanisms to counteract decision biases. In this paper we give an overview of the CHOICLA environment and report the results of a user study which analyzed two voting strategies with regard to their potential of counteracting serial position (primacy/recency) effects when evaluating decision alternatives.

Counteracting Anchoring Effects in Group Decision Making

Similar to single user decisions, group decisions can be affected by decision biases. In this paper we analyze anchoring effects as a specific type of decision bias in the context of group decision scenarios. On the basis of the results of a user study in the domain of software requirements prioritization we discuss results regarding the optimal time when preference information of other users should be disclosed to the current user. Furthermore, we show that explanations can increase the satisfaction of group members with various aspects of a group decision process (e.g., satisfaction with the decision and decision support quality).

INTELLIREQ: intelligent techniques for software requirements engineering

Requirements Engineering is considered as one of the most critical phases of a software development project. Low-quality requirements are a major reason for the failure of a project. Consequently, techniques are needed that help to improve the support of stakeholders in the development of requirements models as well as in the process of deciding about the corresponding release plans. In this paper we introduce the INTELLIREQ Requirements Engineering environment. This environment is based on different recommendation approaches that support stakeholders in requirements-related activities such as definition, quality assurance, reuse, and release planning. We provide an overview of recommendation approaches integrated in INTELLIREQ and report results of empirical studies that show in which way recommenders can improve the quality of Requirements Engineering processes.

WE-DECIDE: a decision support environment for groups of users

Group recommendation technologies are becoming increasingly popular for supporting group decision processes in various domains such as interactive television, music, and tourist destinations. Existing group recommendation environments are focusing on specific domains and do not include the possibility of supporting different kinds of decision scenarios. The We-Decide group decision support environment advances the state of the art by supporting different decision scenarios in a domain-independent fashion. In this paper we give an overview of the We-Decide environment and report the results of a first user study which focused on system usability and potentials for further applications.

Conflict Detection and Diagnosis in Configuration

The widespread industrial application of configuration technologies triggers an increasing demand for intelligent techniques that efficiently support anomaly management operations for configuration knowledge bases. Examples of such operations are the testing and debugging of faulty knowledge bases (see Chapter 11) and the detection of redundancies in configuration knowledge bases (see Chapter 12). The goal of this chapter is to discuss techniques and algorithms that form the technological basis for the aforementioned anomaly management operations.

Human computation for constraint-based recommenders

PeopleViews is a Human Computation based environment for the construction of constraint-based recommenders. Constraint-based recommender systems support the handling of complex items where constraints (e.g., between user requirements and item properties) can be taken into account. When applying such systems, users are articulating their requirements and the recommender identifies solutions on the basis of the constraints in a recommendation knowledge base. In this paper, we provide an overview of the PeopleViews environment and show how recommendation knowledge can be collected from users of the environment on the basis of micro-tasks. We also show how PeopleViews exploits this knowledge for automatically generating recommendation knowledge bases. In this context, we compare the prediction quality of the recommendation approaches integrated in PeopleViews using a DSLR camera dataset.

Intelligent Techniques for Configuration Knowledge Evolution

Automated testing and debugging of knowledge bases (such as configuration knowledge bases and feature models) is an important contribution to manage knowledge evolution efficiently. However, existing approaches rely on the assumption of consistent test suites which are always kept up-to-date within the scope of different knowledge base maintenance cycles. In this paper we introduce diagnosis techniques that actively guide stakeholders (knowledge engineers and domain experts) in the process of testing and debugging knowledge bases. These techniques take into account faulty test cases and constraints and recommend diagnoses which are the source of a given inconsistency.

Anytime diagnosis for reconfiguration

Many domains require scalable algorithms that help to determine diagnoses efficiently and often within predefined time limits. Anytime diagnosis is able to determine solutions in such a way and thus is especially useful in real-time scenarios such as production scheduling, robot control, and communication networks management where diagnosis and corresponding reconfiguration capabilities play a major role. Anytime diagnosis in many cases comes along with a trade-off between diagnosis quality and the efficiency of diagnostic reasoning. In this paper we introduce and analyze FlexDiag which is an anytime direct diagnosis approach. We evaluate the algorithm with regard to performance and diagnosis quality using a configuration benchmark from the domain of feature models and an industrial configuration knowledge base from the automotive domain. Results show that FlexDiag helps to significantly increase the performance of direct diagnosis search with corresponding quality tradeoffs in terms of minimality and accuracy.