Item-Based Collaborative Filtering Recommendation Algorithm Combining Item Category with Interestingness Measure

Wei, Suyun; Ye, Ning; Zhang, Shuo; Huang, Xia; Zhu, Jian

doi:10.1109/csss.2012.507

Cited by 51 publications

(27 citation statements)

References 5 publications

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“…For this reason, proposed the use of the Item Based Collaborative Filtering (IBCF) methodology for the context of GS, invention attributed to Amazon No. US6266649B1 (2001); this methodology has become fundamental to improve the performance of electronic commerce, and has been used efficiently in sites such as Amazon, where it is used for the recommendation of books and other products for sale, as well as in various websites dedicated to products sale, to recommend users similar products (Wei et al, 2012).…”

Section: Introductionmentioning

confidence: 99%

“…Since, the genetic variability between plants of the same species is low in comparison to other taxonomic kingdoms like animalia, we can expect high correlation rates between individuals, allowing in theory a good performance of IBCF. The implementation of this methodology is based in the calculation of a matrix of relationships or similarity matrix between traits (or environments) and there are different ways to calculate this matrix (Wei et al, 2012), as the Pearson's correlation, cosine similarity among others.…”

Section: Introductionmentioning

confidence: 99%

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Benchmarking between item based collaborative filtering algorithm and genomic best linear unbiased prediction (GBLUP) model in terms of prediction accuracy for wheat and maize//Estudio comparativo en términos de capacidad predictiva para datos de trigo y maíz entre el algoritmo de filtrado colaborativo y el modelo genómico mejor predictor lineal insesgado (GBLUP)

Montesinos‐López¹,

Franco-Pérez²,

Luna-Vázquez³

et al. 2020

BIOTECNIA

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Aim/background: in view of the growing demand for food, new methodologies are needed to improve the genomic selection (GS) methodology to obtain more productive plant varieties and there is empirical evidence that GS it is revolutionizing plant breeding for food production around the world. Methods: since the prediction models play a key role in GS, for this reason Montesinos-López et al. (2018) proposed the item based collaborative filtering (IBCF) algorithm for Genomic prediction. For this reason, in this paper we compare the IBCF algorithm with the most popular genomic prediction model called the Genomic Best Linear Unbiased Prediction (GBLUP). Results: We found that the GBLUP is superior than the IBCF model, but the IBCF is competitive to the GBLUP model since produced very similar predictions, but with the large advantage that it is extremely efficient in terms of time for implementation. Conclusions: we found that the GBLUP is better than the IBCF algorithm but the IBCF is more than 400 times more efficient than the GBLUP model in terms of time for implementation. Limitations: The main limitation of the study is that it was performed in univariate terms and it is possible that the IBCF will perform better with multivariate data.RESUMENObjetivo / antecedentes: en vista de la creciente demanda de alimentos, se necesitan nuevas metodologías para mejorar la selección genómica (GS) para obtener variedades de plantas más productivas y en menor tiempo y existe evidencia que la SG está revolucionando el mejoramiento de plantas que ayudará a incrementar la producción de alimentos a nivel mundial. Métodos: dado que los modelos de predicción juegan un papel clave en GS, Montesinos-López et al. (2018) propusieron el algoritmo de filtrado colaborativo (IBCF) para la predicción genómica. Por esta razón, en este artículo comparamos el algoritmo IBCF con el modelo de predicción genómica más popular denominado mejor predictor lineal insesgado Bayesiano (GBLUP). Resultados: Encontramos que el GBLUP es superior en capacidad predictiva al modelo IBCF, pero el IBCF es competitivo con el modelo GBLUP ya que produjo predicciones muy similares, pero con la ventaja de que es eficiente en términos de tiempo de implementación. Conclusiones: encontramos que el GBLUP es mejor que el algoritmo IBCF, pero el IBCF es 400 veces más eficiente que el modelo GBLUP en términos de tiempo de implementación. Limitaciones: la principal limitación del estudio es que se realizó en términos univariados y es posible que el IBCF se desempeñe mejor con datos multivariados.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Benchmarking between item based collaborative filtering algorithm and genomic best linear unbiased prediction (GBLUP) model in terms of prediction accuracy for wheat and maize//Estudio comparativo en términos de capacidad predictiva para datos de trigo y maíz entre el algoritmo de filtrado colaborativo y el modelo genómico mejor predictor lineal insesgado (GBLUP)

Montesinos‐López¹,

Franco-Pérez²,

Luna-Vázquez³

et al. 2020

BIOTECNIA

View full text Add to dashboard Cite

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“…Collaborative filtering (CF) [3][4][5][6] is one of the most successful recommendation algorithms in both industry and academic communities. e basic idea of the technique is that people who share similar ratings tend to have similar preferences.…”

Section: Introductionmentioning

confidence: 99%

DeepFusion: Fusing User-Generated Content and Item Raw Content towards Personalized Product Recommendation

Gan

Zhang

2020

Complexity

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Personalized recommender systems, as effective approaches for alleviating information overload, have received substantial attention in the last decade. Learning effective latent factors plays the most important role in recommendation methods. Several recent works extracted latent factors from user-generated content such as ratings and reviews and suffered from the sparsity problem and the unbalanced distribution problem. To tackle these problems, we enrich the latent representations by incorporating user-generated content and item raw content. Deep neural networks have emerged as very appealing in learning effective representations in many applications. In this paper, we propose a novel deep neural architecture named DeepFusion to jointly learn user and item representations from numerical ratings, textual reviews, and item metadata. In this framework, we utilize multiple types of deep neural networks that are best suited for each type of heterogeneous inputs and introduce an extra layer to obtain the joint representations for users and items. Experiments conducted on the Amazon product data demonstrate that our approach outperforms multiple state-of-the-art baselines. We provide further insight into the design selections and hyperparameters of our recommendation method. In addition, we further explore the relative importance of various item metadata information on improving the rating prediction performance towards personalized product recommendation, which is extremely valuable for feature extraction in practice.

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“…Product big data influences MapReduce technique for fast stocking. Data abstraction problems framed as key-value pairs can be capably dispersed with Hadoop as well as HDFS [14,15]. …”

Section: Architecturementioning

confidence: 99%

A Review Paper on Ranking of Product on Big Data

2017

IJIET

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Abstract-Recommendation system has been used by enormous users. It used commonly in recent years, and are used in a variable areas in many popular applications which comprise of movies, songs, bulletin, files, research courses, online shopping, social networking sites, and products recommendation. Gradually, the extent of consumers, products and facts has mature rapidly, the big data scrutiny problem for examination of recommender systems. Here we are evaluating most efficient recommendation system. Conventional recommender systems frequently suffer from deficiency of scalability, efficiency and real time recommendation problems while processing or analysing documents taking place at huge scale. To get rid from these problems, a recommendation system is implemented in Apache Hadoop and Spark including MapReduce paradigm for Bigdata. Proposed Framework can have considerable enhancement in performance associated to traditional tools.

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Item-Based Collaborative Filtering Recommendation Algorithm Combining Item Category with Interestingness Measure

Cited by 51 publications

References 5 publications

DeepFusion: Fusing User-Generated Content and Item Raw Content towards Personalized Product Recommendation

A Review Paper on Ranking of Product on Big Data

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