Diverse Product Recommendations Using an Expressive Language for Case Retrieval

Bridge, Derek; Ferguson, Alex

doi:10.1007/3-540-46119-1_5

Cited by 32 publications

(19 citation statements)

References 5 publications

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“…Tversky (1996) demonstrated that increased awareness of product options causes consumers to adjust their initial preferences based on available choices, in contrast to maximizing fit to pre-computed preferences. Recommender Systems research supports this claim through design of methods that suggest diverse sets of recommended products to consumers (Bridge & Ferguson, 2002). Indeed, Bodapati (2008) showed that product awareness is a necessary condition for actual purchase.…”

Section: Size Of a Results Setmentioning

confidence: 97%

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: Size Of a Results Setmentioning

confidence: 97%

Intelligent product search with soft-boundary preference relaxation

Dąbrowski

Acton

Heijden

2012

Expert Systems with Applications

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“…Hybrid algorithms combine the results from existing recommendation algorithms that bear different characteristics or perspectives, in order to calculate RecLists [12], [30], [46], [83], [92], [94]. Among the surveyed studies, we found a number of combined algorithms; of particular interest was a novelty prediction model inferred from a novelty matrix, where the perceived item novelty is explicitly given by users as one of its elements [30].…”

Section: Hybrid Algorithmsmentioning

confidence: 99%

A Survey on Recommendation Methods Beyond Accuracy

Han

Yamana

2017

IEICE Trans. Inf. & Syst.

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SUMMARYIn recommending to another individual an item that one loves, accuracy is important, however in most cases, focusing only on accuracy generates less satisfactory recommendations. Studies have repeatedly pointed out that aspects that go beyond accuracy-such as the diversity and novelty of the recommended items-are as important as accuracy in making a satisfactory recommendation. Despite their importance, there is no global consensus about definitions and evaluations regarding beyondaccuracy aspects, as such aspects closely relate to the subjective sensibility of user satisfaction. In addition, devising algorithms for this purpose is difficult, because algorithms concurrently pursue the aspects in trade-off relation (i.e., accuracy vs. novelty). In the aforementioned situation, for researchers initiating a study in this domain, it is important to obtain a systematically integrated view of the domain. This paper reports the results of a survey of about 70 studies published over the last 15 years, each of which addresses recommendations that consider beyond-accuracy aspects. From this survey, we identify diversity, novelty, and coverage as important aspects in achieving serendipity and popularity unbiasedness-factors that are important to user satisfaction and business profits, respectively. The five major groups of algorithms that tackle the beyond-accuracy aspects are multi-objective, modified collaborative filtering (CF), clustering, graph, and hybrid; we then classify and describe algorithms as per this typology. The off-line evaluation metrics and user studies carried out by the studies are also described. Based on the survey results, we assert that there is a lot of room for research in the domain. Especially, personalization and generalization are considered important issues that should be addressed in future research (e.g., automatic per-user-trade-off among the aspects, and properly establishing beyond-accuracy aspects for various types of applications or algorithms).

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“…The query language supports queries that naturally combine preferred values with other preference information such as maximum values, minimum values, and values that the user would prefer not to consider. As in compromise-driven retrieval (McSherry, 2003b), there is no need for an explicit measure of recommendation diversity because the set of retrieved cases is inherently diverse (Bridge & Ferguson, 2002b).…”

Section: Order-based Retrievalmentioning

confidence: 99%

Retrieval, reuse, revision and retention in case-based reasoning

Mántaras

McSherry

Bridge

et al. 2005

The Knowledge Engineering Review

Self Cite

442

185

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Case-based reasoning (CBR) is an approach to problem solving that emphasizes the role of prior experience during future problem solving (i.e., new problems are solved by reusing and if necessary adapting the solutions to similar problems that were solved in the past). It has enjoyed considerable success in a wide variety of problem solving tasks and domains. Following a brief overview of the traditional problem-solving cycle in CBR, we examine the cognitive science foundations of CBR and its relationship to analogical reasoning. We then review a representative selection of CBR research in the past few decades on aspects of retrieval, reuse, revision, and retention. R. LÓPEZ DE MÁNTARAS ET AL.

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Diverse Product Recommendations Using an Expressive Language for Case Retrieval

Cited by 32 publications

References 5 publications

Intelligent product search with soft-boundary preference relaxation

Intelligent product search with soft-boundary preference relaxation

A Survey on Recommendation Methods Beyond Accuracy

Retrieval, reuse, revision and retention in case-based reasoning

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