A theory of diagnosis from first principles
Artificial Intelligence
Query relaxation for xml model
Query relaxation for xml model
FleXPath: flexible structure and full-text querying for XML
SIGMOD '04 Proceedings of the 2004 ACM SIGMOD international conference on Management of data
Compound Critiques for Conversational Recommender Systems
WI '04 Proceedings of the 2004 IEEE/WIC/ACM International Conference on Web Intelligence
QUICKXPLAIN: preferred explanations and relaxations for over-constrained problems
AAAI'04 Proceedings of the 19th national conference on Artifical intelligence
Conflict-directed relaxation of constraints in content-based recommender systems
IEA/AIE'06 Proceedings of the 19th international conference on Advances in Applied Artificial Intelligence: industrial, Engineering and Other Applications of Applied Intelligent Systems
Case-studies on exploiting explicit customer requirements in recommender systems
User Modeling and User-Adapted Interaction
Fast computation of query relaxations for knowledge-based recommenders
AI Communications
Efficient detection of minimal failing subqueries in a fuzzy querying context
ADBIS'11 Proceedings of the 15th international conference on Advances in databases and information systems
A probabilistic optimization framework for the empty-answer problem
Proceedings of the VLDB Endowment
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'Query relaxation' is one of the basic approaches to deal with unfulfillable or conflicting customer requirements in content-based recommender systems: When no product in the catalog exactly matches the customer requirements, the idea is to retrieve those products that fulfill as many of the requirements as possible by removing (relaxing) parts of the original query to the catalog. In general, searching for such an 'maximum succeeding subquery' is a non-trivial task because a) the theoretical search space exponentially grows with the number of the subqueries and b) the allowed response times are strictly limited in interactive recommender applications. In this paper, we describe new techniques for the fast computation of 'user-optimal' query relaxations: First, we show how the number of required database queries for determining an optimal relaxation can be limited to the number of given subqueries by evaluating the subqueries individually. Next, it is described how the problem of finding relaxations returning 'at-least-n' products can be efficiently solved by analyzing these partial query results in memory. Finally, we show how a general-purpose conflict detection algorithm can be applied for determining 'preferred' conflicts in interactive relaxation scenarios. The described algorithms have been implemented and evaluated in a knowledge-based recommender framework; the paper comprises a discussion of implementation details, experiences, and experimental results.