A collaborative filtering recommendation framework utilizing social networks

Fareed, Aamir; Hassan, Saima; Belhaouari, Samir Brahim; Halim, Zahid

doi:10.1016/j.mlwa.2023.100495

Cited by 8 publications

(3 citation statements)

References 25 publications

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“…Then, based on the similarity of items selected by other users, the method projects recommendations for different items [13]. There are two main shortcomings in Collaborative Filtering, namely sparsity and scalability [14]. Sparsity arises when users provide few ratings, while scalability arises when the amount of data that needs to be searched for similarities is large enough.…”

Section: Research Methods 21 Collaborative Filteringmentioning

confidence: 99%

Alleviating cold start and sparsity problems in the micro, small, and medium enterprises marketplace using clustering and imputation techniques

Lestari,

Yulmaini,

Aswin

et al. 2024

IJECE

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Recommendation systems are often implemented in e-commerce and micro, small, and medium enterprises (MSMEs) marketplaces to improve consumer services by providing product recommendations according to their interests. However, it still faces problems, namely sparsity and cold start, thus affecting the quality of recommendations. This research proposes clustering and imputation techniques to overcome this problem. The clustering technique used is k-means, while the missing value imputation method uses average values. The imputation results are then implemented in the k-nearest neighbor (KNN) and naïve Bayes algorithms and evaluated based on performance accuracy. Experimental results show an increase in accuracy of 16.48% in the KNN algorithm from 83.52% to 100%. Meanwhile, the naïve Bayes algorithm increased accuracy by 35.30% from 64.70% to 100%.

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Section: Research Methods 21 Collaborative Filteringmentioning

confidence: 99%

Alleviating cold start and sparsity problems in the micro, small, and medium enterprises marketplace using clustering and imputation techniques

Lestari,

Yulmaini,

Aswin

et al. 2024

IJECE

View full text Add to dashboard Cite

show abstract

“…There are two more categories for the collaborative filtering strategy: model-based and memory-based [3,7,13]. In a model-based method, the model is trained, and predictions are produced using partial ratings.…”

Section: Introductionmentioning

confidence: 99%

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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“…Collaborative filtering contains two methods: model-based and neighbor-based [15][16][17]. The names of these two methods come from how they perform the learning activity based on the users' product preferences.…”

Section: Introductionmentioning

confidence: 99%

Time Loss Function-based Collaborative Filtering in Movie Recommender System

2023

IJIES

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Neighbor-based collaborative filtering is one of the prevalent recommendation approaches that apply similarity algorithms. Using a similarity algorithm to improve the accuracy of the recommendation system is one challenge in collaborative filtering. The computation of similarity now heavily relies on user rating and behavior scores. Users' preferences for each product kind (genre) demonstrate the user behavior's value. The algorithm's weakness is that it only considers genre data when estimating user behavior, regardless of when the user rated the item. Therefore, this study aims to develop a new similarity algorithm by considering the genre and the time weight of the item rating, which is called time loss function-based similarity (TLFSim). Newly assessed items have a higher weight than those estimated for a long time. Our experiment tested the TLFSim's performance compared to the userscore-probability-collaborative-filtering (UPCF) algorithm using the MovieLens 100k dataset. The experimental results demonstrate that the TLFSim algorithm surpasses the prior approach regarding recommendation accuracy, reducing mean absolute error (MAE) by 8.28% and root mean square error (RMSE) by 4.57%.

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A collaborative filtering recommendation framework utilizing social networks

Cited by 8 publications

References 25 publications

Alleviating cold start and sparsity problems in the micro, small, and medium enterprises marketplace using clustering and imputation techniques

Alleviating cold start and sparsity problems in the micro, small, and medium enterprises marketplace using clustering and imputation techniques

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

Time Loss Function-based Collaborative Filtering in Movie Recommender System

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