Interaction and Personalization of Criteria in Recommender Systems

Wolfe, Shawn R.; Zhang, Yi

doi:10.1007/978-3-642-13470-8_18

Cited by 10 publications

(6 citation statements)

References 14 publications

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“…Since independence of criteria usually was not given with real live decision problems [48], and since criteria interaction might be experienced differently among users [49], the possibility to model interaction between criteria was an important feature of "AniFair". An example on how the aggregation of interacting criteria could not be modeled by a weighted mean could be found in Čaklović [50].…”

Section: Discussionmentioning

confidence: 99%

“AniFair”: A GUI Based Software Tool for Multi-Criteria Decision Analysis—An Example of Assessing Animal Welfare

Salau¹,

Friedrich²,

Czycholl³

et al. 2020

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Multi-criteria decision analysis deals with decision problems in which multiple criteria need to be considered. The criteria might be measured on different scales so that comparability is difficult. One approach to help the user to organize the problem and to reflect on his or her assessment on the decision is Measuring Attractiveness by a Categorical Based Evaluation TecHnique (MACBETH). Here the user needs to provide qualitative judgment about differences of attractiveness regarding pairs of options. MACBETH was implemented in the M-MACBETH software using the additive aggregation model. The present article introduces the software tool "AniFair" which combines the MACBETH approach with the Choquet integral as an aggregation function, because the Choquet integral enables the modeling of interaction between criteria. With the Choquet integral, the user can define constraints on the relative importance of criteria (Shapley value) and the interaction between criteria. In contrast to M-MACBETH, with every instance of "AniFair" the user is made available at least two aggregation level. "AniFair" provides Graphical User Interfaces for the entering of information. The software tool is introduced via an example from the Welfare Quality Assessment protocol for pigs. With this, "AniFair" is applied to real data that were collected from thirteen farms in Northern Germany by an animal welfare expert. The "AniFair" results enabled a division of the farms into five groups of comparable performance concerning the welfare principle "Good feeding". Hereby, the results differed in how much the interaction between criteria contributed to the Choquet integral values. The shares varied from 5% to 55%. With this, the vulnerability of aggregation results towards relative importance of and interaction between criteria was stressed, as changes in the ranking due to the definition of constraints could be shown. All results were exported to human readable txt or csv files for further analyses, and advice could be given to the farmers on how to improve their welfare situation.

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Section: Discussionmentioning

confidence: 99%

“AniFair”: A GUI Based Software Tool for Multi-Criteria Decision Analysis—An Example of Assessing Animal Welfare

Salau¹,

Friedrich²,

Czycholl³

et al. 2020

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“…Besides accuracy, specificity has also gained deserved attention among researchers (He et al, 2016;Ramalho et al, 2014). Furthermore, timeliness has got a very important place on the list of evaluation metrics for recommendation systems in health care and other healthrelated systems (Caamares and Castells, 2018;Fong et al, 2011;O'Mahony and Smyth, 2010;Wolfe and Zhang, 2010;Xu et al, 2018;Zhang et al, 2018). Adaptability as an evaluation metric for recommendation systems in health care has also featured prominently in literature (Nathanson et al, 2007;Wiesner and Pfeifer, 2014;Wu et al, 2012;Yang et al, 2018).…”

Section: Methodsmentioning

confidence: 99%

“…Thus, the most important quality that is looked for in a recommendation system is its ability to provide personalized recommendations to its users. Its ability to infer the user's needs and satisfy it (Wolfe and Zhang, 2010). This is seen in the recommendation systems available in health care.…”

Section: Introductionmentioning

confidence: 99%

Improving the design of a recommendation system using evaluation criteria and metrics as a guide

Afolabi

Toivanen

2019

JSIT

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Purpose The roles recommendation systems play in health care have become crucial in achieving effective care and in meeting the needs of modern care giving. As a result, efforts have been geared toward using recommendation systems in the management of chronic diseases. Effectiveness of these systems is determined by evaluation following implementation and before deployment, using certain metrics and criteria. The purpose of this study is to ascertain whether consideration of criteria during the design of a recommendation system can increase acceptance and usefulness of the recommendation system. Design/methodology/approach Using survey-style requirements gathering method, the specific health and technology needs of people living with chronic diseases were gathered. The result was analyzed using quantitative method. Sets of harmonized criteria and metrics were used along with requirements gathered from stakeholders to establish relationship among the criteria and the requirements. A matching matrix was used to isolate requirements for prioritization. These requirements were used in the design of a mobile app. Findings Matching criteria against requirements highlights three possible matches, namely, exact, inferential and zero matches. In any of these matches, no requirement was discarded. This allows priority features of the system to be isolated and accorded high priority during the design. This study highlights the possibility of increasing the acceptance rate and usefulness of a recommendation system by using metrics and criteria as a guide during the design process of recommendation systems in health care. This approach was applied in the design of a mobile app called Recommendations Sharing Community for Aged and Chronically Ill People. The result has shown that with this method, it is possible to increase acceptance rate, robustness and usefulness of the product. Research limitations/implications Inability to know the evaluation criteria beforehand, inability to do functional analysis of requirements, lack of well-defined requirements and often poor cooperation from people living with chronic diseases during requirements gathering for fear of stigmatization, confidentiality and privacy breaches are possible limitations to this study. Practical implications The result has shown that with this method, it is possible to isolate more important features of the system and use them during the design process, thereby speeding up the design and increasing acceptance rate, robustness and usefulness of the system. It also helps to see in advance the likely features of the system that will enhance its usefulness and acceptance, thereby increasing the confidence of the developers in their ability to deliver a system that will meet users’ needs. As a result, developers know beforehand where to concentrate their efforts during system development to ascertain the possibility of increasing usefulness and acceptance rate of a recommendation system. In addition, it will also save time and cost. Originality/value This paper demonstrates originality by highlighting and testing the possibility of using evaluation criteria and metrics during the design of a recommender system with a view to increasing acceptance and enhancing usefulness. It also shows the possibility of using the metrics and criteria in system’s development process for an exercise other than evaluation.

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“…In practice, this problem is usually avoided by considering independent criteria (Cong et al., ; Göker & Myrhaug, ). Nevertheless, other works (Carterette, Kumar, Rao, & Zhu, ; Eickhoff et al., ; Saracevic, ; Wolfe & Zhang, ) have shown that relevance criteria usually interact. For instance, Carterette et al.…”

Section: Multidimensional Relevance Aggregation In Irmentioning

confidence: 97%

iAggregator: Multidimensional relevance aggregation based on a fuzzy operator

Moulahi

Tamine

Yahia

2014

Asso for Info Science & Tech

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Recently, an increasing number of information retrieval studies have triggered a resurgence of interest in redefining the algorithmic estimation of relevance, which implies a shift from topical to multidimensional relevance assessment. A key underlying aspect that emerged when addressing this concept is the aggregation of the relevance assessments related to each of the considered dimensions. The most commonly adopted forms of aggregation are based on classical weighted means and linear combination schemes to address this issue. Although some initiatives were recently proposed, none was concerned with considering the inherent dependencies and interactions existing among the relevance criteria, as is the case in many real-life applications. In this article, we present a new fuzzy-based operator, called iAggregator, for multidimensional relevance aggregation. Its main originality, beyond its ability to model interactions between different relevance criteria, lies in its generalization of many classical aggregation functions. To validate our proposal, we apply our operator within a tweet search task. Experiments using a standard benchmark, namely, Text REtrieval Conference Microblog, 1 emphasize the relevance of our contribution when compared with traditional aggregation schemes. In addition, it outperforms state-of-the-art aggregation operators such as the Scoring and the And prioritized operators as well as some representative learning-to-rank algorithms. IntroductionMulticriteria aggregation is an issue that has been thoroughly addressed in social choice (Arrow, 1974;Condorcet, 1785;Fishburn, 1972), engineering design (Keeney & Raiffa, 1993;Neumann & Morgenstern, 1953), and computer vision applications (Dubois & Prade, 2004;Torra, 2005), to cite but a few. The multicriteria aggregation arises when for a given task there are several alternatives that have to be ordered with respect to different criteria and we are faced with the problem of combining them to figure out a ranking over the set of alternatives. The need to aggregate several inputs into a single representative output allowed successful applications of aggregation functions to fields as diverse as information retrieval (IR) (Farah & Vanderpooten, 2007) multiple criteria decision analysis (Grabisch, Kojadinovic, & Meyer, 2008;Steuer, 1986), data fusion (Ah-Pine, 2008;Vogt & Cottrell, 1999), and database retrieval (Le Calvè & Savoy, 2000). In this article, we are more interested in the IR field. Because ranking and relevance are at the heart of IR systems (Hawking, Craswell, Bailey, & Griffiths, 2001), a great deal of research has triggered a resurgence of interest in revisiting the concept of relevance considering several criteria. In fact, many of the proposed state-of-the-art early IR models rank documents by computing single scores separately with respect to one single objective criterion, rather than considering other relevance dimensions encompassing contextual features with respect to users or documents (Borlund, 2003). This most commonly used crit...

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Interaction and Personalization of Criteria in Recommender Systems

Cited by 10 publications

References 14 publications

“AniFair”: A GUI Based Software Tool for Multi-Criteria Decision Analysis—An Example of Assessing Animal Welfare

“AniFair”: A GUI Based Software Tool for Multi-Criteria Decision Analysis—An Example of Assessing Animal Welfare

Improving the design of a recommendation system using evaluation criteria and metrics as a guide

iAggregator: Multidimensional relevance aggregation based on a fuzzy operator

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