Improving the Accuracy of Collaborative Filtering-Based Recommendations by Considering the Temporal Variance of Top-N Neighbors

Singh, Pradeep; Setta, Showmik; Pramanik, Pijush Kanti Dutta; Choudhury, Prasenjit

doi:10.1007/978-981-15-1286-5_1

Cited by 11 publications

(4 citation statements)

References 8 publications

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“…The recommended list should be composed of items not evaluated by the active user and sorted in decreasing order of predicted rating values. An RS that offers a few better items with higher prediction ratings is known as the 'Top-N' Recommender system (Singh et al, 2020). • fuzzy real-coded genetic algorithm (Fuzzy-RCGA)…”

Section: Gupta and Kant (2020a)mentioning

confidence: 99%

Optimization of fuzzy similarity by genetic algorithm in user‐based collaborative filtering recommender systems

2022

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The most important subjects in the memory‐based collaborative filtering recommender system (RS) are to accurately calculate the similarities between users and finally finding interesting recommendations for active users. The main purpose of this research is to provide a list of the best items for recommending in less time. The fuzzy‐genetic collaborative filtering (FGCF) approach recommends items by optimizing fuzzy similarities in the continuous genetic algorithm (CGA). In this method, first, the crisp values of user ratings are converted to fuzzy ratings, and then the fuzzy similarities are calculated. Similarity values are placed into the genes of the genetic algorithm, optimized, and finally, they are used in fuzzy prediction. Therefore, the fuzzy system is used twice in this process. Experimental results on RecSys, Movielens 100 K, and Movielens 1 M datasets show that FGCF improves the collaborative filtering RS performance in terms of quality and accuracy of recommendations, time and space complexities. The FGCF method is robust against the sparsity of data due to the correct choice of neighbours and avoids the users' different rating scales problem but it not able to solve the cold‐start challenge.

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Section: Gupta and Kant (2020a)mentioning

confidence: 99%

Optimization of fuzzy similarity by genetic algorithm in user‐based collaborative filtering recommender systems

2022

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“…Therefore, it can be concluded that the MovieLens dataset contains only the ratings of the authentic users. This dataset consists of 943 users, 1682 movies, and 1,00,000 ratings [25][26][27]. These ratings are integer values between 1 and 5, where 1 denotes the lowest rating and 5 denotes the highest rating.…”

Section: Experimental Analysis Of the Effect Of The Proposed Attack On Recommendation Systemmentioning

confidence: 99%

Generating A New Shilling Attack for Recommendation Systems

Singh¹,

Pramanik²,

Sardar³

et al. 2022

Computers, Materials &Amp; Continua

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A collaborative filtering-based recommendation system has been an integral part of e-commerce and e-servicing. To keep the recommendation systems reliable, authentic, and superior, the security of these systems is very crucial. Though the existing shilling attack detection methods in collaborative filtering are able to detect the standard attacks, in this paper, we prove that they fail to detect a new or unknown attack. We develop a new attack model, named Obscure attack, with unknown features and observed that it has been successful in biasing the overall top-N list of the target users as intended. The Obscure attack is able to push target items to the top-N list as well as remove the actual rated items from the list. Our proposed attack is more effective at a smaller number of k in top-k similar user as compared to other existing attacks. The effectivity of the proposed attack model is tested on the MovieLens dataset, where various classifiers like SVM, J48, random forest, and naïve Bayes are utilized.

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“…(1) We introduce a novel rating-based recommendation formulation algorithm that exploits the certainty aspect of rating predictions to generate more accurate and useful recommendations. Since the presented algorithm targets the recommendation formulation stage, for the assessment of the algorithm performance, we utilize pertinent metrics, and more specifically (i), the average rating value of the top-N recommended items, (ii) precision, and (iii) the normalized discounted cumulative gain (NDCG) [7][8][9][10][11]. (3) We analyze the additional computational and storage costs incurred in order to compute, store, and utilize the additional data needed by the proposed algorithm, demonstrating its feasibility and applicability.…”

Section: Introductionmentioning

confidence: 99%

Exploiting Rating Prediction Certainty for Recommendation Formulation in Collaborative Filtering

Margaris,

Sgardelis,

Spiliotopoulos

et al. 2024

BDCC

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Collaborative filtering is a popular recommender system (RecSys) method that produces rating prediction values for products by combining the ratings that close users have already given to the same products. Afterwards, the products that achieve the highest prediction values are recommended to the user. However, as expected, prediction estimation may contain errors, which, in the case of RecSys, will lead to either not recommending a product that the user would actually like (i.e., purchase, watch, or listen) or to recommending a product that the user would not like, with both cases leading to degraded recommendation quality. Especially in the latter case, the RecSys would be deemed unreliable. In this work, we design and develop a recommendation algorithm that considers both the rating prediction values and the prediction confidence, derived from features associated with rating prediction accuracy in collaborative filtering. The presented algorithm is based on the rationale that it is preferable to recommend an item with a slightly lower prediction value, if that prediction seems to be certain and safe, over another that has a higher value but of lower certainty. The proposed algorithm prevents low-confidence rating predictions from being included in recommendations, ensuring the recommendation quality and reliability of the RecSys.

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Improving the Accuracy of Collaborative Filtering-Based Recommendations by Considering the Temporal Variance of Top-N Neighbors

Cited by 11 publications

References 8 publications

Optimization of fuzzy similarity by genetic algorithm in user‐based collaborative filtering recommender systems

Optimization of fuzzy similarity by genetic algorithm in user‐based collaborative filtering recommender systems

Generating A New Shilling Attack for Recommendation Systems

Exploiting Rating Prediction Certainty for Recommendation Formulation in Collaborative Filtering

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