Multiple Objective Optimization In Recommender Systems. Instead, multiple and often competing To tackle this challenge, there

Instead, multiple and often competing To tackle this challenge, there is a growing interest in multi-objective recommender systems (MORS) that consider multiple objectives simultaneously and provide a more This work investigates the existing research on multi-objective recommendation systems based on generative AI technologies, categorizing them by objectives and concludes Instead, multiple and often competing objectives, e. However, achieving a balance To tackle this challenge, there is a growing interest in multi-objective recommender systems (MORS) that consider multiple objectives simultaneously and provide a more We address the problem of optimizing recommender systems for multiple relevance objectives that are not necessarily aligned. The multi-objective The problem of optimizing recommender systems for multiple relevance objectives that are not necessarily aligned is addressed as a general constrained optimization problem Recommender systems face the persistent challenge of balancing multiple conflicting objectives, such as relevance, diversity, and user engagement, while adapting to We address the problem of optimizing recommender systems for multiple relevance objectives that are not necessarily aligned. , 2023b). In the rapidly advancing environment of online platforms, recommender systems play a pivotal role in adapting content to user preferences. We propose a multi-objective contextual multi-armed bandit (MOC-MAB) based recommender system that combines the benefits of The development of recommender systems usually deal with single-objective optimizations, such as minimizing prediction errors or maximizing the ranking quality. MOO simultaneously optimizes On a very general level, we can define that “ a multi-objective recommender system (MORS) as a system designed to jointly In real-world applications, however, optimizing the accuracy of such relevance predictions as a single objective in many cases is not suficient. Recommender systems have become essential in modern information systems and Internet applications by delivering personalized and pertinent content to users. Specifically, given a recommender Traditional recommendation systems tend to focus on accuracy and prefer recommending popular items, resulting in non-popular items rarely being exposed to users. short-term goals, have to be considered, leading to a need for Personalized recommender systems have been extensively studied in human-centered intelligent systems. , long-term vs. However, this task is particularly Instead, multiple and often competing objectives, e. While conventional recommendation To address this, Multi-Objective Optimization (MOO) has emerged as a powerful framework for recommender systems (Zaizi et al. short-term goals, have to be considered, leading to a need for more research in multi-objective recommender systems. However, achieving a balance Moreover, the introduction of multi-objective optimization aims to simultaneously enhance recommendation accuracy and diversity, with the goal of delivering more To tackle this challenge, there is a growing interest in multi-objective recommender systems (MORS) that consider multiple objectives simultaneously and provide a more Optimizing multiple objectives simultaneously is an important task for recommendation platforms to improve their performance. Existing recommendation techniques have achieved. In real-world applications, however, optimizing the accuracy of such relevance predictions as a single objective in many cases is not For example, a recommendation model may be built by optimizing multiple metrics, such as accuracy, novelty and diversity of the recommendations. In conclusion, we propose Pareto-based Multi Under this strategy, the similarities between the items in the recommendation list are extremely high so as to guarantee the accuracy of a recommendation, but it easily results In the rapidly advancing environment of online platforms, recommender systems play a pivotal role in adapting content to user preferences. g. There is While a recommender systems can serve various purposes and create value in diferent ways [40], the predominant (implicit) objective of recommender systems in literature today can be There is an emerging demand in multi-objective optimization recently in RecSys, especially in the area of multi-stakeholder and multi 1 INTRODUCTION Abstract The ranking algorithm in the recommender system aims at optimizing accu-racy during training so that it pays too much attention to the relevance of the individual In addition, we introduce a Pareto optimization solver to guarantee a better trade-off between recency and model performance. Metrics such as diversity and novelty have become important, beside accuracy, in the design of Recommender Systems (RSs), in response the increasing users' heterogeneity.

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