Partial constraint satisfaction

Freuder, Eugene C.; Wallace, Richard J.

doi:10.1016/0004-3702(92)90004-h

Cited by 416 publications

(149 citation statements)

References 14 publications

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“…The penalty values are the difference between the attained CVs and the target CVs for each administrative or modeled variable. This approach can be considered a weighted or approximate constraint satisfaction problem in operations research (Freuder and Wallace 1992). Traditional hard constraints can be considered by setting the penalty weight to infinity.…”

Section: Optimal Stratified Design Algorithmmentioning

confidence: 99%

Optimal Stratification and Allocation for the June Agricultural Survey

Lisic

Sang

Zhu

et al. 2018

Journal of Official Statistics

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A computational approach to optimal multivariate designs with respect to stratification and allocation is investigated under the assumptions of fixed total allocation, known number of strata, and the availability of administrative data correlated with thevariables of interest under coefficient-of-variation constraints. This approach uses a penalized objective function that is optimized by simulated annealing through exchanging sampling units and sample allocations among strata. Computational speed is improved through the use of a computationally efficient machine learning method such as K-means to create an initial stratification close to the optimal stratification. The numeric stability of the algorithm has been investigated and parallel processing has been employed where appropriate. Results are presented for both simulated data and USDA's June Agricultural Survey. An R package has also been made available for evaluation.

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Section: Optimal Stratified Design Algorithmmentioning

confidence: 99%

Optimal Stratification and Allocation for the June Agricultural Survey

Lisic

Sang

Zhu

et al. 2018

Journal of Official Statistics

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show abstract

“…Of course, optimality of the result cannot be guaranteed then in general, but our experiments suggest that even rather complex problems can be solved in very short time or good relaxations can be found, e.g., when using a domain-independent, priority-based search heuristic. 5 conflicts in depth-first manner. Label edges and nodes similar to the algorithm described previously.…”

Section: Definition (Optimal Relaxation): Given a Recommendation Probmentioning

confidence: 99%

Conflict-Directed Relaxation of Constraints in Content-Based Recommender Systems

Jannach

Liegl

2006

Advances in Applied Artificial Intelligence

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Abstract. Content-based recommenders are systems that exploit detailed knowledge about the items in the catalog for generating adequate product proposals. In that context, query relaxation is one of the basic approaches for dealing with situations, where none of the products in the catalogue exactly matches the customer requirements. The major challenges when applying query relaxation are that the relaxation should be minimal (or optimal for the customer), that there exists a potentially vast search space, and that we have to deal with hard time constraints in interactive recommender applications. In this paper, we show how the task of finding adequate or customer optimal relaxations for a given recommendation problem can be efficiently achieved by applying techniques from the field of model-based diagnosis, i.e., with the help of extended algorithms for computing conflicts and hitting sets. In addition, we propose a best-effort search algorithm based on branch-and-bound for dealing with hard problems and also describe how an optimal relaxation can be immediately obtained when partial queries can be (pre-)evaluated. Finally, we discuss the results of an evaluation of the described techniques, which we made by extending an existing knowledge-based recommender system and which we based on different real-world problem settings.

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“…The distance can be defined as the number of constraints violated by a valuation [18]. Our strategy to solve the PCSP is explained using an exemplar user profile (in Table 3) and dataset (in Table 4).…”

Section: Solving the Constraint Satisfaction Problem For Information mentioning

confidence: 99%

Intelligent Information Personalization Leveraging Constraint Satisfaction and Association Rule Methods

Zeng

2006

Advances in Artificial Intelligence

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Abstract. Recommender systems, using information personalization methods, provide information that is relevant to a user-model. Current information personalization methods do not take into account whether multiple documents when recommended together present a factually consistent outlook. In the realm of content-based filtering, in this paper, we investigate establishing the factual consistency between the set of documents deemed relevant to a user. We approach information personalization as a constraint satisfaction problem, where we attempt to satisfy two constraints-i.e. user-model constraints to determine the relevance of a document to a user and consistency constraints to establish factual consistency of the overall personalized information. Our information personalization framework involves: (a) an automatic constraint acquisition method, based on association rule mining, to derive consistency constraints from a corpus of documents; and (b) a hybrid of constraint satisfaction and optimization methods to derive an optimal solution comprising both relevant and factually consistent documents. We apply our information personalization framework to filter news items using the Reuters-21578 dataset.

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Partial constraint satisfaction

Cited by 416 publications

References 14 publications

Optimal Stratification and Allocation for the June Agricultural Survey

Optimal Stratification and Allocation for the June Agricultural Survey

Conflict-Directed Relaxation of Constraints in Content-Based Recommender Systems

Intelligent Information Personalization Leveraging Constraint Satisfaction and Association Rule Methods

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