Recommender systems: models, challenges and opportunities

Hodovychenko, Mykola A.; Gorbatenko, Anastasiia A.

doi:10.15276/hait.06.2023.20

Cited by 2 publications

(2 citation statements)

References 36 publications

(36 reference statements)

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“…The primary objective of this system is to examine the user's past actions and preferences, construct a model, and autonomously suggest goods or products that align with the user's interests. Subsequently, a customized list is generated for the user [2].…”

Section: Introduction Formulation Of the Problemmentioning

confidence: 99%

Methods of preference aggregation in group recommender systems

Gorbatenko,

Hodovychenko

2024

AAIT

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The rapid growth of data volumes has led to information overload, which impedes informed decision-making. To solve this problem, recommender systems have emerged that analyze user preferences and offer relevant products on their own. One type of recommender system is group recommender systems, which are designed to facilitate collaborative decision-making, increase user engagement, and promote diversity and inclusion. However, these systems face challenges such as accommodating diverse group preferences and maintaining transparency in recommendation processes. In this study, we propose a method for aggregating preferences in group recommendation systems to retain as much information as possible from group members and improve the accuracy of recommendations. The proposed method provides recommendations to groups of users by avoiding the aggregation process in the first steps of recommendation, which preserves information throughout the group recommendation process and delays the aggregation step to provide accurate and diverse recommendations. When the object of a collaborative filtering-based recommender system is not a single user but a group of users, the strategy for calculating similarity between individual users to find similarity should be adapted to avoid aggregating the preferences of group members in the first step. In the proposed model, the nearest neighbors of a group of users are searched, so the method of finding neighbors is adapted to compare individual users with the group profile. An experimental study has shown that the proposed method achieves a satisfactory balance between accuracy and diversity. This makes it well suited for providing recommendations to large groups in situations where accuracy is more or less important compared to diversity. These results support the assumption that retaining all information from group members without using aggregation techniques can improve the performance of group recommender systems, taking into account various features.

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Section: Introduction Formulation Of the Problemmentioning

confidence: 99%

Methods of preference aggregation in group recommender systems

Gorbatenko,

Hodovychenko

2024

AAIT

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“…The proliferation of information has made it more difficult for users to locate the information they need. The purpose of internet search engines such as Google and Bing are to provide relevant and helpful information to users, but they are also becoming less important due to the challenge of locating relevant information among thousands of results [1].…”

Section: Introduction Formulation Of the Problemmentioning

confidence: 99%

Reaching consensus in group recommendation systems

Gorbatenko,

Hodovychenko

2024

HAIT

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Conventional group recommender systems fail to take into account the impact of group dynamics on group recommendations, such as the process of reconciling individual preferences during collective decision-making. This scenario has been previously examined in the context of group decision making, specifically in relation to consensus reaching procedures. In such processes, experts engage in negotiations to determine their preferences and ultimately pick a mutually agreed upon option. The objective of the consensus procedure is to prevent dissatisfaction among group members about the suggestion. Prior studies have tried to accomplish this characteristic in group recommendation by using the minimal operator for the process of aggregating recommendations. Nevertheless, the use of this operator ensures just a minimal degree of consensus on the proposal, but it does not provide a satisfactory level of agreement among group members over the group recommendation. This paper focuses on analyzing consensus reaching procedures in the context of group recommendation for group decision making. The goal of the study is to use consensus reaching processes to provide group recommendations that satisfy all members of the group. Additionally, study aims to enhance group recommender systems by ensuring an acceptable level of agreement among users regarding the group recommendation. Therefore, group recommender systems are expanded by including consensus reaching mechanisms to facilitate group decision making. In the context of group decision making, a collective resolution is reached by a group of persons, who may be specialists, from a pool of options or potential solutions to the issue at hand. To do this, each specialist obtains their preferences about each possibility. The conventional selection techniques for group decision-making difficulties fail to include the possibility of dissent among experts over the chosen choice. This issue is alleviated by using consensus-building techniques, in which a substantial degree of agreement is attained prior to picking the ultimate decision. To facilitate alignment of experts' tastes, they repeatedly modify them to increase their proximity. Prior to making collective choices, it is sometimes necessary to establish a certain degree of consensus. Thus, this paper presents a group recommendation architecture that utilizes automated consensus reaching models to provide accepted suggestions. More specifically, we are considering the minimal cost consensus model and the automated consensus support system model that relies on input. The minimal cost consensus model calculates the collective suggestion of a group by adjusting individual preferences based on a cost function. This is achieved via the use of linear programming. The feedback-based automated consensus support system model mimics the interaction between group members and a moderator. The moderator offers adjustments to individual suggestions in order to bring them closer together and achieve a high degree of agreement before generating the group recommendation. Both models are assessed and contrasted with baseline procedures in the testing.

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Recommender systems: models, challenges and opportunities

Cited by 2 publications

References 36 publications

Methods of preference aggregation in group recommender systems

Methods of preference aggregation in group recommender systems

Reaching consensus in group recommendation systems

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