Comparing Approaches to Preference Dominance for Conversational Recommenders

Trabelsi, Walid; Wilson, Nic; Bridge, Derek; Ricci, Francesco

doi:10.1109/ictai.2010.91

Cited by 13 publications

(8 citation statements)

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“…The results of our experiments on three different data sets have shown that the finite user profiles set assumption has a strong effect on the process of computing the best query revisions compared to the infinite models described and analyzed in [4,11].…”

Section: Discussionmentioning

confidence: 99%

“…Recommending personalized query revisions by inferring constraints on the user utility function by observing the user selection was first proposed in [4] and extended in [11]. This approach was proved to be effective: it was shown that good query suggestions were provided, and the final product recommendations were also optimal.…”

Section: Related Workmentioning

confidence: 99%

“…This approach was proved to be effective: it was shown that good query suggestions were provided, and the final product recommendations were also optimal. However, [4,11] left open some questions mostly related to the efficiency of computing the query suggestions and the size of the advice set. In fact, linear programming was extensively used to prove that a potential query suggestion was dominated and hence discarded.…”

Section: Related Workmentioning

confidence: 99%

“…In [4,11] it is introduced and evaluated a new conversational technique for helping the users to select items of Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. To copy otherwise, to republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee.…”

Section: Introductionmentioning

confidence: 99%

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Inferring user utility for query revision recommendation

Blanco

Ricci

2013

Proceedings of the 28th Annual ACM Symposium on Applied Computing

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A recommender system (RS) can infer constraints on the user utility function by observing the queries selected by a user among those it has suggested. Reasoning on these constraints it can avoid suggesting queries that retrieve products with an inferior utility, i.e., dominated queries. In this paper we propose a new efficient technique for the computation of dominated queries. It relies on the system's assumption that the number of possible profiles (utility functions) of the users it may interact with is finite. Under this assumption query suggestions can be efficiently computed and their number can be kept small. Moreover, we show that even if the system is not contemplating all the possible user profiles its performance is very close to the optimal one.

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Section: Discussionmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Inferring user utility for query revision recommendation

Blanco

Ricci

2013

Proceedings of the 28th Annual ACM Symposium on Applied Computing

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“…A basic problem is: given a set of outcomes, determine which are the undominated ones, i.e., which are not considered worse than another outcome. For example, in a recommender system, one can use preference deduction techniques to infer, from the previous user inputs, which products may be preferred over others, and hence which are the undominated ones [11].…”

Section: Introductionmentioning

confidence: 99%

Pruning Rules for Constrained Optimisation for Conditional Preferences

Wilson

Trabelsi

2011

Principles and Practice of Constraint Programming – CP 2011

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Access to the full text of the published version may require a subscription. Abstract. A depth-first search algorithm can be used to find optimal solutions of a Constraint Satisfaction Problem (CSP) with respect to a set of conditional preferences statements (e.g., a CP-net). This involves checking at each leaf node if the corresponding solution of the CSP is dominated by any of the optimal solutions found so far; if not, then we add this solution to the set of optimal solutions. This kind of algorithm can clearly be computationally expensive if the number of solutions is large. At a node N of the search tree, with associated assignment b to a subset of the variables B, it may happen that, for some previously found solution α, either (a) α dominates all extensions of b; or (b) α does not dominate any extension of b. The algorithm can be significantly improved if we can find sufficient conditions for (a) and (b) that can be efficiently checked. In case (a), we can backtrack since we need not continue the search below N ; in case (b), α does not need to be considered in any node below the current node N . We derive a sufficient condition for (b), and three sufficient conditions for (a). Our experimental testing indicates that this can make a major difference to the efficiency of constrained optimisation for conditional preference theories including CP-nets. Rights

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