Product Recommendation with Interactive Query Management and Twofold Similarity

Ricci, Francesco; Venturini, Adriano; Cavada, Dario; Mirzadeh, Nader; Blaas, Dennis; Nones, Marisa

doi:10.1007/3-540-45006-8_37

Cited by 53 publications

(36 citation statements)

References 10 publications

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“…We developed a prototype, based on Trip@dvice [2], and empirically evaluated that with real users. In fact two variants of the same system (NutKing 4 ) were built.…”

Section: Discussionmentioning

confidence: 99%

“…In Trip@dvice [2], as well as in other proposals [9], it is argued that the system should not autonomously determine the best attainable approximate match, as in a similarity-based retrieval, but should actively support the user and let him understand what is the best relaxation/compromise. Hence in Trip@dvice when a query fails to return any item a dedicated interactive query management component (IQM in Figure 1) suggests some refinement(s) in terms of query constraints to discard or change.…”

Section: Interactive Query Managementmentioning

confidence: 99%

“…For instance, it is very popular the books recommender system included in amazon.com or the movie recommender system MovieLens [1]. In a previous paper we have introduced Trip@dvice, a travel planning recommendation methodology that integrates interactive query management and case-based reasoning (CBR) [2]. In this methodology, product research is supported by an interactive query management subsystems that cooperates with the user and provides information about the cause of query failure and its possible remedies.…”

Section: Introductionmentioning

confidence: 99%

“…Then we present the empirical evaluation of IQM, that was conducted by exploiting log data derived from the evaluation a recommender system based on Trip@dvice [2]. The results show the method is powerful enough to find at lease one successful relaxed query most of the time.…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Supporting User Query Relaxation in a Recommender System

Mirzadeh¹,

Ricci²,

Bansal³

2004

E-Commerce and Web Technologies

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Abstract. This paper presents a new technology for supporting flexible query management in recommender systems. It is aimed at guiding a user in refining her query when it fails to return any item. It allows the user to understand the culprit of the failure and to decide what is the best compromise to chose. The method uses the notion of hierarchical abstraction among a set of features, and tries to relax first the constraint on the feature with lowest abstraction, hence with the lightest revision of the original user needs. We have introduced this methodology in a travel recommender system as a query refinement tool used to pass the returned items by the query to a case-based ranking algorithm, before showing the query results to the user. We discuss the results of the empirical evaluation which shows that the method, even if incomplete, is powerful enough to assist the users most of the time.

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“…We developed a prototype, based on Trip@dvice [2], and empirically evaluated that with real users. In fact two variants of the same system (NutKing 4 ) were built.…”

Section: Discussionmentioning

confidence: 99%

Section: Interactive Query Managementmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Supporting User Query Relaxation in a Recommender System

Mirzadeh¹,

Ricci²,

Bansal³

2004

E-Commerce and Web Technologies

Self Cite

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show abstract

“…Regarding searching in product catalogues the most similar approach is presented in [Ric03]. It is an extension of a mediator architecture that supports the relaxation or tightening of query constraints when no or too many results are retrieved from the catalogue.…”

Section: Related Workmentioning

confidence: 99%

On the Query Refinement in Searching a Bibliographic Database

Stojanović

2005

Wirtschaftsinformatik 2005

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In this paper we present an application of the logic-based query refinement in the searching for information in an information portal. The refinement approach is based on the discovery of causal relationships between queries regarding the inclusion relation between the answers of these queries. We define a formal model for the query-answering pairs and use methods from the inductive logic programming for the efficient calculation of a (lattice) order between them.In a case study we demonstrate the benefits of using our approach in the traditional information retrieval tasks. We focus on the combination of the free-text based querying and the logic-based query refinement.

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An overview on the exploitation of time in collaborative filtering

Vinagre

Jorge

Gama

2015

WIREs Data Min & Knowl

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Classic Collaborative Filtering (CF) algorithms rely on the assumption that data are static and we usually disregard the temporal effects in natural user-generated data. These temporal effects include user preference drifts and shifts, seasonal effects, inclusion of new users, and items entering the system-and old ones leaving-user and item activity rate fluctuations and other similar time-related phenomena. These phenomena continuously change the underlying relations between users and items that recommendation algorithms essentially try to capture. In the past few years, a new generation of CF algorithms has emerged, using the time dimension as a key factor to improve recommendation models. In this overview, we present a comprehensive analysis of these algorithms and identify important challenges to be faced in the near future.

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Product Recommendation with Interactive Query Management and Twofold Similarity

Cited by 53 publications

References 10 publications

Supporting User Query Relaxation in a Recommender System

Supporting User Query Relaxation in a Recommender System

On the Query Refinement in Searching a Bibliographic Database

An overview on the exploitation of time in collaborative filtering

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