Aggregation of Rankings Using Metaheuristics in Recommendation Systems

Bałchanowski, Michał; Boryczka, Urszula

doi:10.3390/electronics11030369

Cited by 7 publications

(2 citation statements)

References 28 publications

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“…In another publication [31], the authors proposed the Multi-objective Evolutionary Rank Aggregation (MERA) algorithm, which was an algorithm for multi-criteria optimization. The publication [32] suggested using the Differential Evolution algorithm, to directly optimize the AP measure for individual users in the system. This approach made it possible to find a vector, determining the preference of a given user over individual rankings.…”

Section: Related Workmentioning

confidence: 99%

A Comparative Study of Rank Aggregation Methods in Recommendation Systems

Bałchanowski

Boryczka

2023

Entropy

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The aim of a recommender system is to suggest to the user certain products or services that most likely will interest them. Within the context of personalized recommender systems, a number of algorithms have been suggested to generate a ranking of items tailored to individual user preferences. However, these algorithms do not generate identical recommendations, and for this reason it has been suggested in the literature that the results of these algorithms can be combined using aggregation techniques, hoping that this will translate into an improvement in the quality of the final recommendation. In order to see which of these techniques increase the quality of recommendations to the greatest extent, the authors of this publication conducted experiments in which they considered five recommendation algorithms and 20 aggregation methods. The research was carried out on the popular and publicly available MovieLens 100k and MovieLens 1M datasets, and the results were confirmed by statistical tests.

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Section: Related Workmentioning

confidence: 99%

A Comparative Study of Rank Aggregation Methods in Recommendation Systems

Bałchanowski

Boryczka

2023

Entropy

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“…The following are some directions for further research: To design a recommender system that is understandable and comprehensible using implicit hidden characteristics; To use metaheuristic techniques to enhance performance metrics [ 57 ]; To handle the “grey sheep” issue, which occurs when a customer cannot be matched with any other customer group, and the system is unable to produce helpful recommendations [ 58 ]; To provide dynamic predictions with the least amount of complexity; To develop an emotion-based movie recommendation model [ 59 , 60 ]; To integrate other advanced clustering methods such as twin contrastive learning for online clustering, structured autoencoders for subspace clustering, and XAI beyond classification: interpretable neural clustering [ 30 , 31 , 32 , 33 ] for further models’ improvement and to analyze clustering techniques contribution. …”

Section: Conclusion and Future Scopementioning

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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Graph-based comparative analysis of learning to rank datasets

Keyhanipour

2023

Int J Data Sci Anal

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Aggregation of Rankings Using Metaheuristics in Recommendation Systems

Cited by 7 publications

References 28 publications

A Comparative Study of Rank Aggregation Methods in Recommendation Systems

A Comparative Study of Rank Aggregation Methods in Recommendation Systems

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

Graph-based comparative analysis of learning to rank datasets

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