MetaEDL: Meta Evidential Learning For Uncertainty-Aware Cold-Start Recommendations

Neupane, Krishna; Zheng, Ervine; Yu, Qi

doi:10.1109/icdm51629.2021.00154

Cited by 9 publications

(2 citation statements)

References 11 publications

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“…Neupane et al [24] propose a method for quantifying the amount of evidence available when providing recommendations to cold-start users. They propose a meta-evidential method for doing so.…”

Section: Related Workmentioning

confidence: 99%

Recommendation Uncertainty in Implicit Feedback Recommender Systems

Coscrato

Bridge

2023

Communications in Computer and Information Science

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A Recommender System’s recommendations will each carry a certain level of uncertainty. The quantification of this uncertainty can be useful in a variety of ways. Estimates of uncertainty might be used externally; for example, showing them to the user to increase user trust in the abilities of the system. They may also be used internally; for example, deciding the balance of ‘safe’ and less safe recommendations. In this work, we explore several methods for estimating uncertainty. The novelty comes from proposing methods that work in the implicit feedback setting. We use experiments on two datasets to compare a number of recommendation algorithms that are modified to perform uncertainty estimation. In our experiments, we show that some of these modified algorithms are less accurate than their unmodified counterparts, but others are actually more accurate. We also show which of these methods are best at enabling the recommender to be ‘aware’ of which of its recommendations are likely to be correct and which are likely to be wrong.

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Section: Related Workmentioning

confidence: 99%

Recommendation Uncertainty in Implicit Feedback Recommender Systems

Coscrato

Bridge

2023

Communications in Computer and Information Science

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“…[33], [39], [45], [47], [48], [51], [56], [58], [59], [61], [62], [65], [68]- [71], [76], [78], [79]…”

Section: Mse Rmse Maementioning

confidence: 99%

User Cold Start Problem in Recommendation Systems: A Systematic Review

Yuan,

Hernandez

2023

IEEE Access

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The recommendation system makes recommendations based on the preferences of the users. These user preferences usually come from the user's basic information, item rating, historical data, and so on. The "user cold start problem" happens when a new user cannot be appropriately suggested due to a lack of more detailed preference information. In many instances, the user cold start problem hinders the use of the recommendation system. Many researchers are currently trying to discover a solution to the user cold start problem. Unfortunately, there are two drawbacks in the current systematic reviews of how to deal with the user cold start problem. First, systematic reviews on how to deal with the user cold start problem are scarce or outdated. Second, existing reviews lack the distinction between the user cold start problem and the item cold start problem. Nevertheless, the solutions to the two problems differ. To address these problems, our study thorough review of all literature published by researchers from January 2016 to April 2023 about 8 years. Firstly, this study analyzes the literatures on approaches that addressed the user cold start problem during the past eight years and divides them into two categories: data-driven technology and approach-driven technology, and then describes and classifies each type of technology in detail. Secondly, this study also analyzes the main evaluation criteria currently used in these methods to provide a reference for researchers in related fields. Finally, this paper also points out the future research direction of this field.

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Multi-modal Semi-supervised Evidential Recycle Framework for Alzheimer’s Disease Classification

Feng,

Chen,

et al. 2023

Lecture Notes in Computer Science

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MetaEDL: Meta Evidential Learning For Uncertainty-Aware Cold-Start Recommendations

Cited by 9 publications

References 11 publications

Recommendation Uncertainty in Implicit Feedback Recommender Systems

Recommendation Uncertainty in Implicit Feedback Recommender Systems

User Cold Start Problem in Recommendation Systems: A Systematic Review

Multi-modal Semi-supervised Evidential Recycle Framework for Alzheimer’s Disease Classification

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