Supporting User Query Relaxation in a Recommender System

Mirzadeh, Nader; Ricci, Francesco; Bansal, Mukesh

doi:10.1007/978-3-540-30077-9_4

Cited by 28 publications

(21 citation statements)

References 11 publications

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“…Classical approaches to preference relaxation (Mirzadeh & Ricci, 2007;Mirzadeh, Ricci, & Bansal, 2004), referred to here as Standard Preference Relaxation (see Section 3.1) may increase consumers' decision-making performance through positive effects on decision quality. Nevertheless, the major disadvantage of the Standard Preference Relaxation approach is an increase in decision effort (see section 4).…”

Section: Introductionmentioning

confidence: 99%

Intelligent product search with soft-boundary preference relaxation

Dąbrowski

Acton

Heijden

2012

Expert Systems with Applications

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Abstract. This paper proposes a novel method for preference relaxation in online product search, which enables consumers to make quality choices without suffering from the commonly experienced information overload. In online shopping scenarios that involve multi-attribute choice tasks, it can be difficult for consumers to process the vast amounts of information available and to make satisfactory buying decisions. In such situations consumers are likely to eliminate potentially good choices early on, using hard-constraint filtering tools. Our approach uses edge sets to identify the alternatives on the soft boundary and the principle of alternative domination to suppress the alternatives on this boundary that are irrelevant. We demonstrate how our approach outperforms existing methods for product search in a set of simulations using two sets of 2650 car advertisements and 1813 digital cameras gathered from a popular online store.

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Section: Introductionmentioning

confidence: 99%

Intelligent product search with soft-boundary preference relaxation

Dąbrowski

Acton

Heijden

2012

Expert Systems with Applications

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“…In the example above, a consumer using such a product search tool, and who provided preferences on price ($7000 to $8000) and mileage (25000mi to 75000mi) would be presented with a result set with only those offers that fully satisfy all the stated criteria, that is, are both within the price and mileage range. This approach is often referred to as product filtration using hard-constraints or logical filtering (Mirzadeh & Ricci, 2007) and has many limitations acknowledged in the literature (Dabrowski & Acton, 2010b;Felfernig, Mairitsch, Mandl, Schubert, & Teppan, 2009;Mirzadeh, Ricci, & Bansal, 2004), addressed with recommendation method, for example through preference relaxation.…”

Section: Preference Relaxation Methodsmentioning

confidence: 99%

The performance of recommender systems in online shopping: A user-centric study

Dąbrowski

Acton

2013

Expert Systems with Applications

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Abstract. This research investigates the effects of preference relaxation on decisionmaking performance of users in online preference-based product search contexts. We compare four recommender systems based on different preference relaxation methods in extensive user experiments with 111 subjects that use two real-world datasets: 1818 digital cameras and 45278 used car advertisements gathered from popular e-commerce websites. Our results provide new insights into the positive impact of the soft-boundary preference relaxation methods on decision-making quality and effort. The paper extends previous studies on this topic and demonstrates that decision aids based on preference relaxation techniques can effectively enhance preference-based product search in online product catalogues and help alleviate common disadvantages of form-based filtering mechanisms.

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“…See (Mirzadeh et al, 2004;Jannach, 2006a;Jannach, 2006b) for discussions of relaxation strategies for interactive recommender systems.…”

Section: Recommendation Algorithm: Which Reasoning Paradigm Does the mentioning

confidence: 99%

Case-studies on exploiting explicit customer requirements in recommender systems

Zanker

Jessenitschnig

2008

User Model User-Adap Inter

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Abstract. Recommender Systems (RS) suggest useful and interesting items to users in order to increase user satisfaction and online conversion rates. They typically exploit explicit or implicit user feedback such as ratings, buying records or clickstream data and apply statistical methods to derive recommendations. This paper focuses on explicitly formulated customer requirements as the sole type of user feedback. Its contribution lies in comparing different techniques such as knowledge-and utility-based methods, collaborative filtering, association rule mining as well as hybrid variants when user models consist solely of explicit customer requirements. We examine how this type of user feedback can be exploited for personalization in e-commerce scenarios. Furthermore, examples of actual online shops are developed where such contextual user information is available, demonstrating how more efficient RS configurations can be implemented. Results indicate that, especially for new users, explicit customer requirements are a useful source of feedback for personalization and hybrid configurations of collaborative and knowledge-based techniques achieve best results.

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Supporting User Query Relaxation in a Recommender System

Cited by 28 publications

References 11 publications

Intelligent product search with soft-boundary preference relaxation

Intelligent product search with soft-boundary preference relaxation

The performance of recommender systems in online shopping: A user-centric study

Case-studies on exploiting explicit customer requirements in recommender systems

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