Measures of Solution Accuracy in Case-Based Reasoning Systems

Cheetham, William; Price, Joseph A.

doi:10.1007/978-3-540-28631-8_9

Cited by 51 publications

(38 citation statements)

References 9 publications

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“…However, the efficiency of the solving process depends on the discrimination capabilities, mainly, nearby to the boundaries of clusters. Cheetham and Price [4] have pointed the importance of providing a confidence indication with any proposed solution. Some CBR approaches handle the solution quality by using as similarity criteria, the amount of cases that indicate confidence.…”

Section: Confidence Assessmentmentioning

confidence: 99%

“…Other contributions on confidence assessment highlight whether a feature has a negative or positive correlation with respect to the classification. Consequently, the development of CBR systems has increased the necessity of supporting the analysis of the case-base structure while providing solutions with a required accuracy and stability [4]. According to these authors, we are also concerned on proving the classification stability of a set of estimators.…”

Section: Confidence Assessmentmentioning

confidence: 99%

“…Cheetham and Price [4] describe different measures of confidence that can be applicable for k-NN classifiers. However, their confidence estimators contain similar, redundant information about nearness, which does not allow dichotomizing near classes and decision surfaces for complex domains.…”

Section: Classification Confidencementioning

confidence: 99%

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Section: Confidence Assessmentmentioning

confidence: 99%

Section: Confidence Assessmentmentioning

confidence: 99%

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Confidence assessment on eyelid and eyebrow expression recognition

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et al. 2008

2008 8th IEEE International Conference on Automatic Face &Amp; Gesture Recognition

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“…The product-level sentiment approach used Eq. (7) Similar approaches have been successfully used by the CBR community [21]. The results of the accuracy-based CBR are presented in Table 6.…”

Section: Evaluation Analysis and Discussionmentioning

confidence: 99%

A New Sentiment Case-Based Recommender

Aldayel

Ykhlef

2017

IEICE Trans. Inf. & Syst.

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SUMMARYRecommender systems have attracted attention in both the academic and the business areas. They aim to give users more intelligent methods for navigating and identifying complex information spaces, especially in e-commerce domain. However, these systems still have to overcome certain limitations that reduce their performance, such as overspecialization of recommendations, cold-start, and difficulties when items with unequal probability distribution exist. A novel approach addresses the above issues through a case-based recommendation methodology which is a form of content-based recommendation that is well suited to many product recommendation domains, owing to the clear organization of users' needs and preferences. Unfortunately, the experience-based roots of case-based reasoning are not clearly reflected in case-based recommenders. In other words, the concept that product cases, which are usually fixed feature-based tuples, are experiential is not adopted well in case-based recommenders. To solve this problem as well as the recommenders' rating sparsity issue, one can use product reviews which are generated from users' experience with the product a basis of product information. Our approach adapts the use of sentiment scores along with feature similarity throughout the recommendation unlike traditional case-based recommender systems, which tend to depend entirely on pure similarity-based approaches. This paper models product cases with the products' features and sentiment scores at the feature level and product level. Thus, combining user experience and similarity measures improves the recommender performance and gives users more flexibility to choose whether they prefer products more similar to their query or better qualified products. We present the results using different evaluation methods for different case structures, different numbers of similar cases retrieved and multilevel sentiment-approaches. The recommender performance was highly improved with the use of feature-level sentiment approach, which recommends product cases that are similar to the query but favored for customers. key words: recommenders, case-based reasoning (CBR), case-based recommenders, opinion mining

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“…The question of how to determine an appropriate confidence value for a recommendation is an area of active research. See [13] for recent work on the assessing the confidence of a case-based recommender system.…”

Section: Switchingmentioning

confidence: 99%

Hybrid Web Recommender Systems

Burke

Lecture Notes in Computer Science

913

525

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Abstract. Adaptive web sites may offer automated recommendations generated through any number of well-studied techniques including collaborative, content-based and knowledge-based recommendation. Each of these techniques has its own strengths and weaknesses. In search of better performance, researchers have combined recommendation techniques to build hybrid recommender systems. This chapter surveys the space of two-part hybrid recommender systems, comparing four different recommendation techniques and seven different hybridization strategies. Implementations of 41 hybrids including some novel combinations are examined and compared. The study finds that cascade and augmented hybrids work well, especially when combining two components of differing strengths. IntroductionRecommender systems are personalized information agents that provide recommendations: suggestions for items likely to be of use to a user [18,41,42]. In an ecommerce context, these might be items to purchase; in a digital library context, they might be texts or other media relevant to the user's interests. 1 A recommender system can be distinguished from an information retrieval system by the semantics of its user interaction. A result from a recommender system is understood as a recommendation, an option worthy of consideration; a result from an information retrieval system is interpreted as a match to the user's query. Recommender systems are also distinguished in terms of personalization and agency. A recommender system customizes its responses to a particular user. Rather than simply responding to queries, a recommender system is intended to serve as an information agent. 2 1 In this chapter, I use the e-commerce term "products" to refer to the items being recommended, with the understanding that other information-seeking contexts are also pertinent. 2 Techniques such as relevance feedback enable an information retrieval engine to refine its representation of the user's query, and therefore can be seen as a simple form of recommendation. The search engine Google (http://www.google.com) blurs this distinction further, using "authoritativeness" criteria in addition to strict matching [6]. R. BurkeA variety of techniques have been proposed as the basis for recommender systems: collaborative, content-based, knowledge-based, and demographic techniques are surveyed below. Each of these techniques has known shortcomings, such as the wellknown cold-start problem for collaborative and content-based systems (what to do with new users with few ratings) and the knowledge engineering bottleneck in knowledge-based approaches. A hybrid recommender system is one that combines multiple techniques together to achieve some synergy between them. For example, a collaborative system and a knowledge-based system might be combined so that the knowledgebased component can compensate for the cold-start problem, providing recommendations to new users whose profiles are too small to give the collaborative technique any traction, and the collaborative component can work it...

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Measures of Solution Accuracy in Case-Based Reasoning Systems

Cited by 51 publications

References 9 publications

Confidence assessment on eyelid and eyebrow expression recognition

Confidence assessment on eyelid and eyebrow expression recognition

A New Sentiment Case-Based Recommender

Hybrid Web Recommender Systems

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