Interactive regret minimization

Nanongkai, Danupon; Lall, Ashwin; Sarma, Anish Das; Makino, Kazuhisa

doi:10.1145/2213836.2213850

Cited by 58 publications

(61 citation statements)

References 30 publications

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“…Nanongkai et al [16] defined the interactive 1-regret minimization problem, where the user can interact with the system saying which tuples are more preferred. Peng and Wong [18] define the notion of happy points, which is a subset of the skyline points, to solve the 1-RMS problem.…”

Section: Top-k Rmsmentioning

confidence: 99%

Faster Approximation Algorithm for the k-Regret Minimizing Set and Related Problems

Kumar

Sintos

2018

2018 Proceedings of the Twentieth Workshop on Algorithm Engineering and Experiments (ALENEX)

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Efficient multi-criteria decision making often requires looking at a small set of representative objects from a large collection. A recently proposed method for finding representative objects is the k-regret minimizing set (k-RMS problem). Intuitively, given a large set of objects (points) in d dimensions, the goal is to choose a small representative subset, such that for every user preference, there is always an object in the subset whose preference score is not much worse than the score of the k-th most preferred object in the original set. We propose two new efficient approximation algorithms for the k-regret minimizing set problem with provable theoretical guarantees. Our algorithms improve on the space and time complexities of previous approximation algorithms for the k-RMS problem. In addition, we run extensive experiments on real and synthetic data sets showing that simple modifications of our theoretical algorithms run significantly faster than the previous implementations of the k-RMS problem. Finally, we present an efficient approximation algorithm with theoretical guarantees for an extension of the k-RMS problem, which is called the Top-k regret minimizing set problem.

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Section: Top-k Rmsmentioning

confidence: 99%

Faster Approximation Algorithm for the k-Regret Minimizing Set and Related Problems

Kumar

Sintos

2018

2018 Proceedings of the Twentieth Workshop on Algorithm Engineering and Experiments (ALENEX)

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“…The third type is k-regret queries [1], [2], [6], [9], [18], [21], [22], [25], recently proposed queries in the database community, which could address both the issue of top-k query processing (i.e., requiring an exact utility function of a given user) and the issue of skyline queries (i.e., returning an output set with an uncontrollable size). That is, a k-regret query does not require a user to give an exact utility function of a given user and returns the output set with a controllable size.…”

Section: Related Workmentioning

confidence: 99%

“…To tackle this problem, first we need a formulation for the satisfaction of a user when a set of points is shown to a user (as opposed to the user's satisfaction from a single point, which is measured by the user's utility function). Recently, The concept of regret ratio and the k-regret problem [1], [2], [6], [9], [18], [21], [22], [25] has been proposed to measure how well a set of points satisfies a user compared with when the user has seen the entire database. Specifically, when a user is presented with a set of points, the satisfaction of the user from the set of points is defined as the maximum value of the utility function of the user among all the points in the set.…”

Section: Introductionmentioning

confidence: 99%

Finding Average Regret Ratio Minimizing Set in Database

Zeighami

Wong

2019

2019 IEEE 35th International Conference on Data Engineering (ICDE)

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Selecting a certain number of data points (or records) from a database which "best" satisfy users' expectations is a very prevalent problem with many applications. One application is a hotel booking website showing a certain number of hotels on a single page. However, this problem is very challenging since the selected points should "collectively" satisfy the expectation of all users. Showing a certain number of data points to a single user could decrease the satisfaction of a user because the user may not be able to see his/her favorite point which could be found in the original database. In this paper, we would like to find a set of k points such that on average, the satisfaction (ratio) of a user is maximized. This problem takes into account the probability distribution of the users and considers the satisfaction (ratio) of all users, which is more reasonable in practice, compared with the existing studies that only consider the worst-case satisfaction (ratio) of the users, which may not reflect the whole population and is not useful in some applications. Motivated by this, in this paper, we propose algorithms for this problem. Finally, we conducted experiments to show the effectiveness and the efficiency of the algorithms.Definition 5 (Variance of Regret Ratio). Let F be a set of users with the probability density function η(.) and S be a subset of D. The variance of regret ratio of F for S, denoted by vrr(S), is defined to be f ∈F (rr(S, f ) − arr(S)) 2 η(f )df .Although the FAM problem focuses on minimizing average regret ratio, it is important for a set S to have low vrr(S) as well. In our empirical studies (Section V), we use estimates of vrr(S) to compare different selection sets.

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“…The CUBE algorithm is efficient, but the maximum regret ratio it obtains is quite large in practice. To obtain a smaller maximum regret ratio, in a different paper [31], Nanongkai et al propose an interactive algorithm where query users are involved in guiding the search for answers with smaller regret ratios. Peng and Wong [36] advance the k-regret query studies by utilizing geometric properties to improve the query efficiency.…”

Section: Related Workmentioning

confidence: 99%

K-Regret Queries Using Multiplicative Utility Functions

Zuo

Samet

et al. 2018

ACM Trans. Database Syst.

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The k-regret query aims to return a size-k subset S of a database D such that, for any query user that selects a data object from this size-k subset S rather than from database D, her regret ratio is minimized. The regret ratio here is modeled by the relative difference in the optimality between the locally optimal object in S and the globally optimal object in D. The optimality of a data object in turn is modeled by a utility function of the query user. Unlike traditional top-k queries, the k-regret query does not minimize the regret ratio for a specific utility function. Instead, it considers a family of infinite utility functions F , and aims to find a size-k subset that minimizes the maximum regret ratio of any utility function in F .Studies on k-regret queries have focused on the family of additive utility functions, which have limitations in modeling individuals' preferences and decision making processes, especially for a common observation called the diminishing marginal rate of substitution (DMRS). We introduce k-regret queries with multiplicative utility functions, which are more expressive in modeling the DMRS, to overcome those limitations. We propose a query algorithm with bounded regret ratios. To showcase the applicability of the algorithm, we apply it to a special family of multiplicative utility functions, the Cobb-Douglas family of utility functions, and a closely related family of utility functions, the Constant Elasticity of Substitution family of utility functions, both of which are frequently used utility functions in microeconomics. After a further study of the query properties, we propose a heuristic algorithm that produces even smaller regret ratios in practice. Extensive experiments on the proposed algorithms confirm that they consistently achieve small maximum regret ratios.CCS Concepts: • Information systems → Top-k retrieval in databases; • Theory of computation → Database query processing and optimization (theory); Data structures and algorithms for data management;Additional Key Words and Phrases: K-regret, multiplicative utility functions, skyline, maximum regret ratio ACM Reference format:

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Interactive regret minimization

Cited by 58 publications

References 30 publications

Faster Approximation Algorithm for the k-Regret Minimizing Set and Related Problems

Faster Approximation Algorithm for the k-Regret Minimizing Set and Related Problems

Finding Average Regret Ratio Minimizing Set in Database

K-Regret Queries Using Multiplicative Utility Functions

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