Evaluating Recommender Systems

Gunawardana, Asela; Shani, Guy

doi:10.1007/978-1-4899-7637-6_8

Cited by 212 publications

(177 citation statements)

References 61 publications

Supporting

Mentioning

167

Contrasting

Order By: Relevance

“…The second evaluation method is the user study: where a group of users will be asked to use the system in a particular controlled environment and give their feelings about the system. Thirdly, the systems can be evaluated when used by many real users over an extended period, normally without prior thinking of the experiment [32].…”

Section: Methodsmentioning

confidence: 99%

“…Cross-validation is one of the popular ways of selecting the test data, where the dataset will be split into some equal partitions, and every partition will be used in turn as the test data. We chose cross-validation so that more data in the ranking algorithm will be used, and the effect of variation in the training set will be taken into consideration [32].…”

Section: Methodsmentioning

confidence: 99%

See 1 more Smart Citation

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: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

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

“…It determines the efficient way of recommendation and can go beyond for accuracy, coverage, risk factor; adaptability etc. The accuracy is not enough for the need of recommended quality [9].…”

Section: Evaluating Recommender Systemsmentioning

confidence: 99%

Efficient Mining and Recommendation of Extensive Data Through Collaborative Filtering in E-Commerce: A Survey

Kumar¹

2018

IJET

View full text Add to dashboard Cite

E-Commerce is the most widely used technique nowadays. Buying and selling goods on the Internet has been most admired and frequently utilized. The humongous growth of the content available on the internet has made laborious for users to search and utilize information for classifying the products. Recommendation system regarded as the best way to help the customers in buying the related products. (GRS) group recommender system aims at enhancing the customer's benefits for buying the products. This paper summarizes the fuzzy tree matching, modeling user preference dynamics, web page recommendation, uncertainty analysis for keywords, recommender system application, temporal topic model for friend recommendation, autocratic decision-making system based on (GRS),modeling user recommender, evaluating recommender system and enhancing (GRS).

show abstract

“…e mise-en-scène dataset contains ve feature values extracted according to the procedure described in Section 2.2.2. In the o ine evaluation we computed recommendation quality with respect to: precision, diversity, novelty and coverage [18].…”

Section: Study A: O Line Experimentsmentioning

confidence: 99%

Exploring the Semantic Gap for Movie Recommendations

Elahi

Deldjoo

Moghaddam

et al. 2017

Proceedings of the Eleventh ACM Conference on Recommender Systems

View full text Add to dashboard Cite

In the last years, there has been much a ention given to the semantic gap problem in multimedia retrieval systems. Much e ort has been devoted to bridge this gap by building tools for the extraction of high-level, semantics-based features from multimedia content, as low-level features are not considered useful because they deal primarily with representing the perceived content rather than the semantics of it.In this paper, we explore a di erent point of view by leveraging the gap between low-level and high-level features. We experiment with a recent approach for movie recommendation that extract lowlevel Mise-en-Scène features from multimedia content and combine it with high-level features provided by the wisdom of the crowd.To this end, we rst performed an o ine performance assessment by implementing a pure content-based recommender system with three di erent versions of the same algorithm, respectively based on (i) conventional movie a ributes, (ii) mise-en-scène features, and (iii) a hybrid method that interleaves recommendations based on movie a ributes and mise-en-scène features. In a second study, we designed an empirical study involving 100 subjects and collected data regarding the quality perceived by the users. Results from both studies show that the introduction of mise-en-scène features in conjunction with traditional movie a ributes improves both o ine and online quality of recommendations.

show abstract

Evaluating Recommender Systems

Cited by 212 publications

References 61 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

Efficient Mining and Recommendation of Extensive Data Through Collaborative Filtering in E-Commerce: A Survey

Exploring the Semantic Gap for Movie Recommendations

Contact Info

Product

Resources

About