An empirical study of natural noise management in group recommendation systems

2017 ◽  
Vol 94 ◽  
pp. 1-11 ◽  
Author(s):  
Jorge Castro ◽  
Raciel Yera ◽  
Luis Martínez
Keyword(s):  
Empirical Study ◽  
Recommendation Systems ◽  
Group Recommendation ◽  
Natural Noise ◽  
Noise Management

A Social Influence Approach for Group User Modeling in Group Recommendation Systems

2016 ◽  
Vol 31 (5) ◽  
pp. 40-48 ◽  
Author(s):  
Junpeng Guo ◽  
Yanlin Zhu ◽  
Aiai Li ◽  
Qipeng Wang ◽  
Weiguo Han
Keyword(s):  
Social Influence ◽  
User Modeling ◽  
Recommendation Systems ◽  
Group Recommendation

scholarly journals A Time-Aware Hybrid Approach for Intelligent Recommendation Systems for Individual and Group Users

Complexity ◽  
2021 ◽  
Vol 2021 ◽  
pp. 1-19
Author(s):  
Zhao Huang ◽  
Pavel Stakhiyevich
Keyword(s):  
Hybrid Approach ◽  
Recommendation Systems ◽  
User Preferences ◽  
User Preference ◽  
User Profiles ◽  
Group Recommendation ◽  
Group Recommendations ◽  
Time Awareness ◽  
Hybrid Recommendation ◽  
Time Aware

Although personal and group recommendation systems have been quickly developed recently, challenges and limitations still exist. In particular, users constantly explore new items and change their preferences throughout time, which causes difficulties in building accurate user profiles and providing precise recommendation outcomes. In this context, this study addresses the time awareness of the user preferences and proposes a hybrid recommendation approach for both individual and group recommendations to better meet the user preference changes and thus improve the recommendation performance. The experimental results show that the proposed approach outperforms several baseline algorithms in terms of precision, recall, novelty, and diversity, in both personal and group recommendations. Moreover, it is clear that the recommendation performance can be largely improved by capturing the user preference changes in the study. These findings are beneficial for increasing the understanding of the user dynamic preference changes in building more precise user profiles and expanding the knowledge of developing more effective and efficient recommendation systems.

scholarly journals Critique on Natural Noise in Recommender Systems

2021 ◽  
Vol 15 (5) ◽  
pp. 1-30
Author(s):  
Wissam Al Jurdi ◽  
Jacques Bou Abdo ◽  
Jacques Demerjian ◽  
Abdallah Makhoul
Keyword(s):  
Recommender Systems ◽  
User Behavior ◽  
Cold Start ◽  
User Experiences ◽  
Data Sparsity ◽  
Natural Noise ◽  
E Learning ◽  
Management Techniques ◽  
Noise Management

Recommender systems have been upgraded, tested, and applied in many, often incomparable ways. In attempts to diligently understand user behavior in certain environments, those systems have been frequently utilized in domains like e-commerce, e-learning, and tourism. Their increasing need and popularity have allowed the existence of numerous research paths on major issues like data sparsity, cold start, malicious noise, and natural noise, which immensely limit their performance. It is typical that the quality of the data that fuel those systems should be extremely reliable. Inconsistent user information in datasets can alter the performance of recommenders, albeit running advanced personalizing algorithms. The consequences of this can be costly as such systems are employed in abundant online businesses. Successfully managing these inconsistencies results in more personalized user experiences. In this article, the previous works conducted on natural noise management in recommender datasets are thoroughly analyzed. We adequately explore the ways in which the proposed methods measure improved performances and touch on the different natural noise management techniques and the attributes of the solutions. Additionally, we test the evaluation methods employed to assess the approaches and discuss several key gaps and other improvements the field should realize in the future. Our work considers the likelihood of a modern research branch on natural noise management and recommender assessment.

Sign in / Sign up

Export Citation Format

Share Document