Multi-Criteria Recommender Systems

Adomavicius, Gediminas; Kwon, YoungOk

doi:10.1007/978-1-4899-7637-6_25

Cited by 128 publications

(181 citation statements)

References 75 publications

Supporting

Mentioning

181

Contrasting

Order By: Relevance

“…in some cases. Therefore, the main goal of the system is ∀u ∈ Users to estimate the function f(u, i, r) for which r on i ∈ Items for u is not yet known, and to recommend i's according to the strength of their r [5].…”

Section: Multi-criteria Recommender Systems (Mcrss)mentioning

confidence: 99%

“…However, research has shown that these traditional recommendation techniques have their shortcomings, such as sparsity and cold start problem, overspecialization, and so on [3,4]. More generally, using a single rating to make predictions is considered by the recommender systems research community as one of their great limitations [5]. Because the acceptability of the item recommended to the user may depend on many utility-related attributes that the user might take into consideration when choosing the item.…”

Section: Introductionmentioning

confidence: 99%

“…Some users might prefer movies based on one or more attributes. The multi-criteria technique has been researched and proved to provide higher prediction accuracy than any of the single rating approaches [5]. However, one of the most exciting research directions in multi-criteria RSs is focusing on exploring statistical or machine learning techniques to model the criteria ratings for improving their prediction accuracy.…”

Section: Introductionmentioning

confidence: 99%

“…This area is relatively new, and there is much research that has not yet been done extensively. One of the outstanding pieces of research comes in to answer the challenge thrown to the RSs research community by Adomavicius et al [5] to use more sophisticated machine learning algorithms such as artificial neural networks and aggregation function approaches to analyze the performance of multi-criteria RSs. The same challenge was repeated four years later [5], and to date, no previous research has investigated the performance of multi-criteria RSs using artificial neural networks.…”

Section: Introductionmentioning

confidence: 99%

“…One of the outstanding pieces of research comes in to answer the challenge thrown to the RSs research community by Adomavicius et al [5] to use more sophisticated machine learning algorithms such as artificial neural networks and aggregation function approaches to analyze the performance of multi-criteria RSs. The same challenge was repeated four years later [5], and to date, no previous research has investigated the performance of multi-criteria RSs using artificial neural networks. Among the initial attempts that explored some of the powerful machine learning techniques is the work Jannach et al [6], who used support vector regression (SVR) to model the criteria rating.…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

A Neural Networks Approach for Improving the Accuracy of Multi-Criteria Recommender Systems

Hassan

Hamada

2017

Applied Sciences

View full text Add to dashboard Cite

Accuracy improvement has been one of the most outstanding issues in the recommender systems research community. Recently, multi-criteria recommender systems that use multiple criteria ratings to estimate overall rating have been receiving considerable attention within the recommender systems research domain. This paper proposes a neural network model for improving the prediction accuracy of multi-criteria recommender systems. The neural network was trained using simulated annealing algorithms and integrated with two samples of single-rating recommender systems. The paper presents the experimental results for each of the two single-rating techniques together with their corresponding neural network-based models. To analyze the performance of the approach, we carried out a comparative analysis of the performance of each single rating-based technique and the proposed multi-criteria model. The experimental findings revealed that the proposed models have by far outperformed the existing techniques.

show abstract

Section: Multi-criteria Recommender Systems (Mcrss)mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

A Neural Networks Approach for Improving the Accuracy of Multi-Criteria Recommender Systems

Hassan

Hamada

2017

Applied Sciences

View full text Add to dashboard Cite

show abstract

Feedback beyond accuracy: Using eye‐tracking to detect comprehensibility and interest during reading

Sluis

Broek

2022

Asso for Info Science & Tech

View full text Add to dashboard Cite

Knowing what information a user wants is a paramount challenge to information science and technology. Implicit feedback is key to solving this challenge, as it allows information systems to learn about a user's needs and preferences. The available feedback, however, tends to be limited and its interpretation shows to be difficult. To tackle this challenge, we present a user study that explores whether tracking the eyes can unpack part of the complexity inherent to relevance and relevance decisions. The eye behavior of 30 participants reading 18 news articles was compared with their subjectively appraised comprehensibility and interest at a discourse level. Using linear regression models, the eye-tracking signal explained 49.93% (comprehensibility) and 30.41% (interest) of variance (p < .001). We conclude that eye behavior provides implicit feedback beyond accuracy that enables new forms of adaptation and interaction support for personalized information systems.

show abstract

An empirical exploration of the subjectivity problem of information qualities

Van der Sluis,

Faure,

Homnual

2024

Asso for Info Science & Tech

View full text Add to dashboard Cite

Information qualities such as usefulness, accuracy, and comprehensiveness are to some extent subjective. Information resources have different meanings to different people and at different moments. This apparent subjectivity hinders indexing based on qualities for retrieval and filtering purposes. We conceptualize this as the subjectivity problem and address it through two studies. Study One explores whether, on public fora, people consider qualities as claims they should agree upon. Study Two explores, through a vignettes study, which conditions foster this inter‐subjective validity of quality claims. We conclude that information qualities become agreeable given the right set of conditions. We discuss the need for transparency about information qualities and quality considerations in order to offer these conditions to end users.

show abstract

Multi-Criteria Recommender Systems

Cited by 128 publications

References 75 publications

A Neural Networks Approach for Improving the Accuracy of Multi-Criteria Recommender Systems

A Neural Networks Approach for Improving the Accuracy of Multi-Criteria Recommender Systems

Feedback beyond accuracy: Using eye‐tracking to detect comprehensibility and interest during reading

An empirical exploration of the subjectivity problem of information qualities

Contact Info

Product

Resources

About