Recommendation Algorithm Using Clustering-Based UPCSim (CB-UPCSim)

Widiyaningtyas, Triyanna; Hidayah, Indriana; Adji, Teguh Bharata

doi:10.3390/computers10100123

Cited by 8 publications

(9 citation statements)

References 29 publications

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“…Next, we will discuss the Collaborative Filtering (CF) approach and model from several studies. Study Year Approach Model Methods [22] 2021 memory-based (ranking oriented) TDD-BPR [23] 2021 memory_based (rating oriented)…”

Section: 𝑃(𝑣 𝑆 ̅ |𝑐) = 𝑃(𝑣|𝑐)𝑃(𝑆 ̅ |𝑐)mentioning

confidence: 99%

Systematic Literature Review on Recommender System: Approach, Problem, Evaluation Techniques, Datasets

Saifudin,

Widiyaningtyas

2024

IEEE Access

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Recommender systems are needed with the presence of the internet and social media. The benefits that are felt in the recommender system can make it easier for users to find suitable products and recommend other products, specifically with lots of information. Recommender systems continue to develop over time. This has led many researchers to continue to find the latest approach and evaluation techniques by comparing the performance of previously existing recommender systems. The main approaches that are often used in recommender systems are Content-Based Filtering (CBF), Collaborative Filtering (CF), and Hybrid Filtering (HBF). This time, we focus on conducting a Systematic Literature Review (SLR) of several research articles and analyzing methods for algorithms developed in building recommender systems. The SLR method consists of three stages: planning, implementation, and reporting. The research used as a comparison is between 2019-2023 using various existing data sets. There were 72 primary studies where the Collaborative Filtering method was used in 46 studies, Content-Based Filtering was used in 11 studies, and the Hybrid Filtering method was used in 15 studies. The results of this SLR process show the advantages and disadvantages of each method and type of evaluation developed in building a recommender system. Apart from that, several challenges arise with various existing problems. However, the model-based collaborative filtering method is one method that can minimize the problems of cold start, data sparsity, and scalability.

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Section: 𝑃(𝑣 𝑆 ̅ |𝑐) = 𝑃(𝑣|𝑐)𝑃(𝑆 ̅ |𝑐)mentioning

confidence: 99%

Systematic Literature Review on Recommender System: Approach, Problem, Evaluation Techniques, Datasets

Saifudin,

Widiyaningtyas

2024

IEEE Access

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“…The drawback of similarity measures was proposed in these studies [15][16][17][18][19], only considering the users' ratings in their similarity calculations, disregarding other variables that might affect the recommendation's performance (i.e., the prediction error is still high, ranging from 0.736 to 0.952). Furthermore, some researchers [20][21][22] have proposed similarity measures by integrating the users' ratings-based and behaviors-based similarities. The ratings score directly assigned to the products are what is included in the users' ratings.…”

Section: Related Workmentioning

confidence: 99%

“…After assigning weight to each similarity, the ultimate similarity is then calculated using a combination of these two similarities. There are several methods for allocating similarity weights to these similarities [20][21][22]. The threshold value determines the weighting for the similarity in [20].…”

Section: Related Workmentioning

confidence: 99%

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MF-NCG: Recommendation Algorithm Using Matrix Factorization-based Normalized Cumulative Genre

2024

IJIES

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Collaborative filtering has emerged as one of the most prevalent techniques for various commercial recommendations. Utilizing a similarity measure to identify similar neighbors is essential to collaborative filtering. Behavior scores and user ratings have recently become increasingly important factors in determining similarity. However, the added users' behavior scores generate a more complex computation. This research proposes a new similarity technique incorporating the matrix factorization and users' behavior score-based similarity to minimize computation time. The matrix factorization technique uses singular value decomposition (SVD), and the users' behavior score-based similarity employs normalized cumulative genre (NCG). Compared to the previous algorithm (i.e., users' scores probability-based collaborative filtering), the experimental findings with the MovieLens 1M and 100k datasets demonstrated a faster computing time. In addition, with these datasets, our similarity reduces the root mean square error (RMSE) by 8.14% and 11.99% and the mean absolute error (MAE) by 13.52% and 15.81%.

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“…Bharti et al [ 21 ] developed a model to deliver the best and fastest recommendations by maintaining and clustering current users and items of the system. Triyanna et al [ 22 ] also proposed a recommendation model that integrates clustering technique and user behavior score-based similarity to reduce model computation complexity. To avoid the data sparsity problem, the research [ 23 ] presented a general framework to cluster users with respect to their tastes when the registers stored about the interactions between users and products are extremely scarce.…”

Section: Introductionmentioning

confidence: 99%

Modeling and Applying Implicit Dormant Features for Recommendation via Clustering and Deep Factorization

Kutlimuratov

Abdusalomov

Oteniyazov

et al. 2022

Sensors

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E-commerce systems experience poor quality of performance when the number of records in the customer database increases due to the gradual growth of customers and products. Applying implicit hidden features into the recommender system (RS) plays an important role in enhancing its performance due to the original dataset’s sparseness. In particular, we can comprehend the relationship between products and customers by analyzing the hierarchically expressed hidden implicit features of them. Furthermore, the effectiveness of rating prediction and system customization increases when the customer-added tag information is combined with hierarchically structured hidden implicit features. For these reasons, we concentrate on early grouping of comparable customers using the clustering technique as a first step, and then, we further enhance the efficacy of recommendations by obtaining implicit hidden features and combining them via customer’s tag information, which regularizes the deep-factorization procedure. The idea behind the proposed method was to cluster customers early via a customer rating matrix and deeply factorize a basic WNMF (weighted nonnegative matrix factorization) model to generate customers preference’s hierarchically structured hidden implicit features and product characteristics in each cluster, which reveals a deep relationship between them and regularizes the prediction procedure via an auxiliary parameter (tag information). The testimonies and empirical findings supported the viability of the proposed approach. Especially, MAE of the rating prediction was 0.8011 with 60% training dataset size, while the error rate was equal to 0.7965 with 80% training dataset size. Moreover, MAE rates were 0.8781 and 0.9046 in new 50 and 100 customer cold-start scenarios, respectively. The proposed model outperformed other baseline models that independently employed the major properties of customers, products, or tags in the prediction process.

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Recommendation Algorithm Using Clustering-Based UPCSim (CB-UPCSim)

Cited by 8 publications

References 29 publications

Systematic Literature Review on Recommender System: Approach, Problem, Evaluation Techniques, Datasets

Systematic Literature Review on Recommender System: Approach, Problem, Evaluation Techniques, Datasets

MF-NCG: Recommendation Algorithm Using Matrix Factorization-based Normalized Cumulative Genre

Modeling and Applying Implicit Dormant Features for Recommendation via Clustering and Deep Factorization

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