Multi-objective Evolutionary Rank Aggregation for Recommender Systems

Oliveira, Samuel; Diniz, Victor; Lacerda, Anísio; Pappa, Gisele L.

doi:10.1109/cec.2018.8477669

Cited by 5 publications

(2 citation statements)

References 33 publications

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“…The authors tested the suggested solution on four datasets, and the results clearly indicate that the technique improved the quality of the generated recommendations. 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.…”

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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“…Some examples are reciprocal RS (Neve and Palomares 2019), conference review assignments (Nguyen et al 2018) or neighbors aggregation in KNN (Garcin et al 2009). Some authors also focus on learning the correct aggregation, e.g., via genetic programming (Oliveira et al 2018). Nature-inspired optimizations are nevertheless difficult to utilize in an online environment, and even in this case, the learned function is still item-wise.…”

Section: Aggregations In Recommender Systemsmentioning

confidence: 99%

Rank-sensitive proportional aggregations in dynamic recommendation scenarios

Balcar

Škrhák

Peška

2022

User Model User-Adap Inter

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In this paper, we focus on the problem of rank-sensitive proportionality preservation when aggregating outputs of multiple recommender systems in dynamic recommendation scenarios. We believe that individual recommenders may provide complementary views on the user’s preferences or needs, and therefore, their proportional (i.e. unbiased) aggregation may be beneficial for the long-term user satisfaction. We propose an aggregation framework (FuzzDA) based on a modified D’Hondt’s algorithm (DA) for proportional mandates allocation. Specifically, we adjusted DA to register fuzzy membership of items and modified the selection procedure to balance both relevance and proportionality criteria. Furthermore, we propose several iterative votes assignment strategies and negative implicit feedback incorporation strategies to make FuzzDA framework applicable in dynamic recommendation scenarios. Overall, the framework should provide benefits w.r.t. long-term novelty of recommendations, diversity of recommended items as well as overall relevance. We evaluated FuzzDA framework thoroughly both in offline simulations and in online A/B testing. Framework variants outperformed baselines w.r.t. click-through rate (CTR) in most of the evaluated scenarios. Some variants of FuzzDA also provided the best or close-to-best iterative novelty (while maintaining very high CTR). While the impact of the framework variants on user-wise diversity was not so extensive, the trade-off between CTR and diversity seems reasonable.

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MOEA-RS: A Content-Based Recommendation System Supported by a Multi-objective Evolutionary Algorithm

Almeida

Britto

2020

Artificial Intelligence and Soft Computing

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Multi-objective Evolutionary Rank Aggregation for Recommender Systems

Cited by 5 publications

References 33 publications

A Comparative Study of Rank Aggregation Methods in Recommendation Systems

A Comparative Study of Rank Aggregation Methods in Recommendation Systems

Rank-sensitive proportional aggregations in dynamic recommendation scenarios

MOEA-RS: A Content-Based Recommendation System Supported by a Multi-objective Evolutionary Algorithm

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