Knowledge-based Configuration J.UCS Special Issue

Non peer reviewe

An introduction to personalization and mass customization

Mass customization as a state-of-the-art production paradigm aims to produce individualized, highly variant products and services with nearly mass production costs. A major side-effect for companies providing complex products and services is that customers quite often get confused by the high variety and do not make a purchase. Personalization technologies can help to alleviate the challenges of mass customization. These technologies support customers in specifying products and services that fit their wishes and needs in a fashion where decision and interaction efforts with sales support systems are significantly reduced. We provide a short overview of related research and the articles that are part of this special issue on Personalization and Mass Customization.Peer reviewe

Conjunctive Query Based Constraint Solving For Feature Model Configuration

Feature model configuration can be supported on the basis of various types of reasoning approaches. Examples thereof are SAT solving, constraint solving, and answer set programming (ASP). Using these approaches requires technical expertise of how to define and solve the underlying configuration problem. In this paper, we show how to apply conjunctive queries typically supported by today's relational database systems to solve constraint satisfaction problems (CSP) and -- more specifically -- feature model configuration tasks. This approach allows the application of a wide-spread database technology to solve configuration tasks and also allows for new algorithmic approaches when it comes to the identification and resolution of inconsistencies.Comment: to be presented at The 12th Conference on Information Technology and Its Application, CITA 2023, July 28-29, 2023, Da Nang, Vietnam, and to be published in the volume of the Lecture Notes in Network and Systems series (Springer

Solving Multi-Configuration Problems: A Performance Analysis with Choco Solver

In many scenarios, configurators support the configuration of a solution that satisfies the preferences of a single user. The concept of \emph{multi-configuration} is based on the idea of configuring a set of configurations. Such a functionality is relevant in scenarios such as the configuration of personalized exams, the configuration of project teams, and the configuration of different trips for individual members of a tourist group (e.g., when visiting a specific city). In this paper, we exemplify the application of multi-configuration for generating individualized exams. We also provide a constraint solver performance analysis which helps to gain some insights into corresponding performance issues.Comment: The paper was presented at ConfWS'23: 25th International Workshop on Configuration, September 6-7, 2023, M\'alaga, Spain and is published in the conference proceedings: https://ceur-ws.org/Vol-3509

Towards Question-based Recommender Systems

Conversational and question-based recommender systems have gained increasing attention in recent years, with users enabled to converse with the system and better control recommendations. Nevertheless, research in the field is still limited, compared to traditional recommender systems. In this work, we propose a novel Question-based recommendation method, Qrec, to assist users to find items interactively, by answering automatically constructed and algorithmically chosen questions. Previous conversational recommender systems ask users to express their preferences over items or item facets. Our model, instead, asks users to express their preferences over descriptive item features. The model is first trained offline by a novel matrix factorization algorithm, and then iteratively updates the user and item latent factors online by a closed-form solution based on the user answers. Meanwhile, our model infers the underlying user belief and preferences over items to learn an optimal question-asking strategy by using Generalized Binary Search, so as to ask a sequence of questions to the user. Our experimental results demonstrate that our proposed matrix factorization model outperforms the traditional Probabilistic Matrix Factorization model. Further, our proposed Qrec model can greatly improve the performance of state-of-the-art baselines, and it is also effective in the case of cold-start user and item recommendations.Comment: accepted by SIGIR 202

Goal-conflict detection based on temporal satisfiability checking

Goal-oriented requirements engineering approaches propose capturing how a system should behave through the speci ca- tion of high-level goals, from which requirements can then be systematically derived. Goals may however admit subtle situations that make them diverge, i.e., not be satis able as a whole under speci c circumstances feasible within the domain, called boundary conditions . While previous work al- lows one to identify boundary conditions for con icting goals written in LTL, it does so through a pattern-based approach, that supports a limited set of patterns, and only produces pre-determined formulations of boundary conditions. We present a novel automated approach to compute bound- ary conditions for general classes of con icting goals expressed in LTL, using a tableaux-based LTL satis ability procedure. A tableau for an LTL formula is a nite representation of all its satisfying models, which we process to produce boundary conditions that violate the formula, indicating divergence situations. We show that our technique can automatically produce boundary conditions that are more general than those obtainable through existing previous pattern-based approaches, and can also generate boundary conditions for goals that are not captured by these patterns