A noise correction-based approach to support a recommender system in a highly sparse rating environment

Bag, Sujoy; Kumar, Srijan; Awasthi, Anjali; Tiwari, Manoj Kumar

doi:10.1016/j.dss.2019.01.001

Cited by 46 publications

(32 citation statements)

References 29 publications

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“…consumers' facial expression and related emotion) that requires a limited time of performance. In this way, our results reinforce the value of recommender systems in terms of value and time complexity reduction, as solicited by recent researches (Bag et al, 2019).…”

Section: Research Findingssupporting

confidence: 89%

Facilitating tourists' decision making through open data analyses: A novel recommender system

Pantano

Priporas

Stylos

et al. 2019

Tourism Management Perspectives

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A number of studies have recently been published reporting researchers' efforts to create new, more efficient recommender systems to support tourists' decision making. This current research operationalizes a recommender system by filtering user-generated data that is abundantly available online, based on individuals' evaluation criteria, to produce a dataset for analysis. Drawing upon an array of predictive models, this research proposes a new recommender system able to facilitate the tourist decision making process through successful managing of open data. It further presents a rating estimation method using ratings that pertain to online users-specified criteria (profile). The model is able to predict consumers' ratings of a certain product with high reliability starting from open data on their profiles.

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Section: Research Findingssupporting

confidence: 89%

Facilitating tourists' decision making through open data analyses: A novel recommender system

Pantano

Priporas

Stylos

et al. 2019

Tourism Management Perspectives

View full text Add to dashboard Cite

show abstract

“…However, the study does not provide an attribute analysis of all the previous NNM approaches in the literature nor direct comparisons in terms of datasets or algorithm complexities. One very recent study on NNM in RSs by Bag et al [15] introduced a sparsity-aware model by slightly amending the previous approach of Toledo et al in [9]. This study mentions few researches from the literature, however, it was very brief and lacked technical analysis.…”

Section: Related Workmentioning

confidence: 99%

Critique on Natural Noise in Recommender Systems

Jurdi

Abdo

Demerjian

et al. 2021

ACM Trans. Knowl. Discov. Data

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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.

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“…In general, process mining aims are at gathering data from process execution to prepare higher decisions, but this method works well for a structured process (static process). Bag et al 36 defined that RS is a good option for decision-making. In general, RS supports consumers but faces different challenges in sparse and noisy data.…”

Section: Desirable Performancementioning

confidence: 99%

Super‐process interoperability optimization architecture in healthcare ultra‐large‐scale systems: A graph‐based multi‐objective approach

Shirazi

2021

Concurrency and Computation

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The healthcare industry is more challenging and the super-process interoperability should be planned in order to architecture an informational technology structure of the required data for progression of care. Large-scale enterprises could be provided cross-organization super-processes operations. Therefore, each enterprise's business process management system (BPMS) may be executed cross-organizational BPM as a kind of the process model. This study proposed a Super-process Interoperable Optimized Architecture based on BPMS as a paradigm to satisfy the consistency of cross-organization operations in healthcare industry. For developing the healthcare ultra-large-scale process management system (H-ULS-PMS), the paper provided interoperability optimization with intelligent recommender system in estimation component of BPMS that has been altered to H-ULS-PMS. This system could be recommended the appropriate concept of crossed healthcare process according to the hospitals' requirements. A graph-based multi-objective optimization method has been applied to control distributed resources across the hospitals. The community cloud is used as proposed architecture infrastructure for largely scaled interoperability. A practical case in hospital as a research project of a hospital in Dusseldorf-Wersten of Germany has been studied to show the better interoperability and resource management of the proposed super-process interoperability architecture.

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A noise correction-based approach to support a recommender system in a highly sparse rating environment

Cited by 46 publications

References 29 publications

Facilitating tourists' decision making through open data analyses: A novel recommender system

Facilitating tourists' decision making through open data analyses: A novel recommender system

Critique on Natural Noise in Recommender Systems

Super‐process interoperability optimization architecture in healthcare ultra‐large‐scale systems: A graph‐based multi‐objective approach

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