A Survey on Collaborative Filtering Based Recommendation System

Suganeshwari, G.; Ibrahim, Syed

doi:10.1007/978-3-319-30348-2_42

Cited by 45 publications

(32 citation statements)

References 46 publications

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“…Primarily, userbased K-nearest neighbors (UserKNN) algorithm [22] is chosen as the baseline. By importing the proposed model to KNN, it is called hybrid feature-based KNN (HFB-KNN) algorithm.…”

Section: Methodsmentioning

confidence: 99%

Leveraging Image Visual Features in Content-Based Recommender System

Deng

Ren

Qin

et al. 2018

Scientific Programming

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Content-based (CB) and collaborative filtering (CF) recommendation algorithms are widely used in modern e-commerce recommender systems (RSs) to improve user experience of personalized services. Item content features and user-item rating data are primarily used to train the recommendation model. However, sparse data would lead such systems unreliable. To solve the data sparsity problem, we consider that more latent information would be imported to catch users' potential preferences. erefore, hybrid features which include all kinds of item features are used to excavate users' interests. In particular, we find that the image visual features can catch more potential preferences of users. In this paper, we leverage the combination of user-item rating data and item hybrid features to propose a novel CB recommendation model, which is suitable for rating-based recommender scenarios. e experimental results show that the proposed model has better recommendation performance in sparse data scenarios than conventional approaches. Besides, training offline and recommendation online make the model has higher efficiency on large datasets.

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Section: Methodsmentioning

confidence: 99%

Leveraging Image Visual Features in Content-Based Recommender System

Deng

Ren

Qin

et al. 2018

Scientific Programming

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“…The CF filtering is classified into Memory based and Model based CF. Figure 1 shows the functional structure of the CF method [2].…”

Section: Collaborative Filtering (Cf)mentioning

confidence: 99%

“…Three common algorithms of this method are: 1) Random Algorithm, 2) Mean Algorithm and, 3) Neighbor- Figure 1. The functional structure of the CF method [2].…”

Section: Collaborative Filtering (Cf)mentioning

confidence: 99%

“…Content-based filtering is based on item description rather than similarity of other users [2]. It uses machine learning algorithms to make a model of user preferences.…”

Section: Content-based Filteringmentioning

confidence: 99%

“…Each recommendation approach has advantages and disadvantages. In this survey, first, the classical and traditional filtering ways are introduced and then the paper is focused on introducing and comparing the recent publications of EC approach in the recommender systems [2] [3] [4].…”

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confidence: 99%

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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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Review-Based User Profiling: A Systematic Mapping Study

Dong

et al. 2019

Enterprise, Business-Process and Information Systems Modeling

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A Survey on Collaborative Filtering Based Recommendation System

Cited by 45 publications

References 46 publications

Leveraging Image Visual Features in Content-Based Recommender System

Leveraging Image Visual Features in Content-Based Recommender System

Recommender Systems Based on Evolutionary Computing: A Survey

Review-Based User Profiling: A Systematic Mapping Study

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