# An Overview of Direct Diagnosis and Repair Techniques in the WeeVis Recommendation Environment

arXiv: https://arxiv.org/abs/2102.12327 · HTML: https://mlsanity.com/paper/2102.12327/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 2 of its 29 references that are in the map.
- First posted on arXiv 24 Feb 2021.

## Builds on (most cited first, 2 of 2)

- [An Efficient Diagnosis Algorithm for Inconsistent Constraint Sets](https://mlsanity.com/paper/2102.09005/) (2021)
- [Recommender Systems for Configuration Knowledge Engineering](https://mlsanity.com/paper/2102.08113/) (2021)

## Abstract (arXiv)

Constraint-based recommenders support users in the identification of items (products) fitting their wishes and needs. Example domains are financial services and electronic equipment. In this paper we show how divide-and-conquer based (direct) diagnosis algorithms (no conflict detection is needed) can be exploited in constraint-based recommendation scenarios. In this context, we provide an overview of the MediaWiki-based recommendation environment WeeVis.