An evolutionary approach for combining results of recommender systems techniques based on collaborative filtering

Silva, Edjalma Queiroz da; Camilo-Junior, Celso G.; Pascoal, Luiz Mario L.; Rosa, Thierson Couto

doi:10.1016/j.eswa.2015.12.050

Cited by 71 publications

(23 citation statements)

References 22 publications

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“…This dataset exists in three different sizes (100 KB, 1 MB, and 10 MB ratings). In this study, 1 MB dataset is used for testing [24,39]. MovieLens dataset 1 M has 1,000,209 ratings on 3883 films by 6040 users.…”

Section: Implementation and Evaluation Of The Resultsmentioning

confidence: 99%

A Fast Recommender System for Cold User Using Categorized Items

Jazayeriy

Mohammadi

Shamshirband

2018

MCA

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Abstract:In recent years, recommender systems (RS) provide a considerable progress to users. RSs reduce the cost of a user's time in order to reach to desired results faster. The main issue of RSs is the presence of cold users which are less active and their preferences are more difficult to detect. The aim of this study is to provide a new way to improve recall and precision in recommender systems for cold users. According to the available categories of items, prioritization of the proposed items is improved and then presented to the cold user. The obtained results show that in addition to increased speed of processing, recall and precision have an acceptable improvement.

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Section: Implementation and Evaluation Of The Resultsmentioning

confidence: 99%

A Fast Recommender System for Cold User Using Categorized Items

Jazayeriy

Mohammadi

Shamshirband

2018

MCA

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“…There are several research studies to improve the performance of CF in cold-start problem, such as combining multiple CF techniques [27] or performing CF on a small cluster rather than the entire graph [28].…”

Section: Related Workmentioning

confidence: 99%

dTrust: A Simple Deep Learning Approach for Social Recommendation

Dang

Ignat

2017

2017 IEEE 3rd International Conference on Collaboration and Internet Computing (CIC)

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HAL is a multi-disciplinary open access archive for the deposit and dissemination of scientific research documents, whether they are published or not. The documents may come from teaching and research institutions in France or abroad, or from public or private research centers. L'archive ouverte pluridisciplinaire HAL, est destinée au dépôt et à la diffusion de documents scientifiques de niveau recherche, publiés ou non, émanant des établissements d'enseignement et de recherche français ou étrangers, des laboratoires publics ou privés. dTrust: a simple deep learning approach for social recommendationQuang-Vinh Dang, Claudia-Lavinia IgnatTo cite this version: Abstract-Rating prediction is a key task of e-commerce recommendation mechanisms. Recent studies in social recommendation enhance the performance of rating predictors by taking advantage of user relationships. However, these prediction approaches mostly rely on user personal information which is a privacy threat.In this paper, we present dTrust, a simple social recommendation approach that avoids using user personal information. It relies uniquely on the topology of an anonymized trust-useritem network that combines user trust relations with user rating scores. This topology is fed into a deep feed-forward neural network. Experiments on real-world data sets showed that dTrust outperforms state-of-the-art in terms of Root Mean Square Error (RMSE) and Mean Absolute Error (MAE) scores for both warmstart and cold-start problems.

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“…The [38] proposed an evolutionary approach for combining results of recommendation techniques in order to automate the choice of techniques and get fewer errors in recommendations. Experiments on Movie Lens data showed that the appropriate combination of the results of different recommendation techniques performed better than any one of collaborative filtering technique separately in the context addressed [39].…”

Section: Approaches Based On Weights Optimizingmentioning

confidence: 99%

Recommender Systems Based on Evolutionary Computing: A Survey

Sadeghi¹,

Asghari²

2017

JSEA

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Data mining techniques and information personalization have made significant growth in the past decade. Enormous volume of data is generated every day. Recommender systems can help users to find their specific information in the extensive volume of information. Several techniques have been presented for development of Recommender System (RS). One of these techniques is the Evolutionary Computing (EC), which can optimize and improve RS in the various applications. This study investigates the number of publications, focusing on some aspects such as the recommendation techniques, the evaluation methods and the datasets which are used.

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An evolutionary approach for combining results of recommender systems techniques based on collaborative filtering

Cited by 71 publications

References 22 publications

A Fast Recommender System for Cold User Using Categorized Items

A Fast Recommender System for Cold User Using Categorized Items

dTrust: A Simple Deep Learning Approach for Social Recommendation

Recommender Systems Based on Evolutionary Computing: A Survey

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