Jella Pfeiffer

A Low-Effort Recommendation System with High Accuracy

In recent studies on recommendation systems, the choice-based conjoint analysis has been suggested as a method for measuring consumer preferences. This approach achieves high recommendation accuracy and does not suffer from the start-up problem because it is also applicable for recommendations for new consumers or of new products. However, this method requires massive consumer input, which causes consumer reluctance. In a simulation study, we demonstrate the high accuracy, but also the high user’s effort for using a utility-based recommendation system using a choice-based conjoint analysis with hierarchical Bayes estimation. In order to reduce the conflict between consumer effort and recommendation accuracy, we develop a novel approach that only shows Pareto-efficient alternatives and ranks them according to the number of dominated attributes. We demonstrate that, in terms of the decision accuracy of the recommended products, the ranked Pareto-front approach performs better than a recommendation system that employs choice-based conjoint analysis. Furthermore, the consumer’s effort is kept low and comparable to that of simple systems that require little consumer input.

Reference point based multi-objective evolutionary algorithms for group decisions

While in the past decades research on multi-objective evolutionary algorithms (MOEA) has aimed at finding the whole set of Pareto optimal solutions, current approaches focus on only those parts of the Pareto front which satisfy the preferences of the decision maker (DM). Therefore, they integrate the DM early on in the optimization process instead of leaving him/her alone with the final choice of one solution among the whole Pareto optimal set. In this paper, we address an aspect which has been neglected so far in the research on integrating preferences: in most real-world problems, there is not only one DM, but a group of DMs trying to find one consensus decision all participants are willed to agree to. Therefore, our aim is to introduce methods which focus on the part of the Pareto front which satisfies the preferences of several DMs concurrently. We assume that the DMs have some vague notion of their preferences a priori the search in form of a reference point or goal. Thus, we present and compare several reference point based approaches for group decisions and evaluate them on three ZDT and two flow shop problems.

Analysis of greedy heuristics and weight-coded eas for multidimensional knapsack problems and multi-unit combinatorial auctions

The multidimensional knapsack problem (MDKP) is a generalized variant of the \( \mathcal{N}\mathcal{P} \)-complete knapsack problem (KP). The MDKP assumes one knapsack being packed with a number of items x j so that the total profit Σpj of the selected items is maximized. In contrast to the standard KP, each item has m different properties (dimensions) r ij (i = 1, ...,m; j = 1, ..., n) consuming c i of the knapsack:

Adapting Human-Computer-Interaction of Attentive Smart Glasses to the Trade-Off Conflict in Purchase Decisions: An Experiment in a Virtual Supermarket

maximize{\text{ }}\sum\limits_{j{\text{ = 1}}}^n {p_j x_j }

Towards Attentive In-Store Recommender Systems

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The Influence of Context-Based Complexity on Decision Processes

\begin{gathered} subject\ to \sum\limits_{j = 1}^n {r_{ij} x_j} \leqslant c_i ,i = 1,...,m \\ with\; x_j \in \{0,1\} ,j = 1,...,n, p_j ,c_i \in \mathbb{N}, r_{ij} \in \mathbb{N}_0 \end{gathered}