An improved memory-based collaborative filtering method based on the TOPSIS technique

Al-bashiri, Hael; Abdulgabber, Mansoor Abdullateef; Romli, Awanis; Kahtan, Hasan

doi:10.1371/journal.pone.0204434

Cited by 29 publications

(9 citation statements)

References 64 publications

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“…The ranking of all the scenarios is done, leaving the most appropriate of the scenarios which is then placed at the top of the arranged list. A very relevant technique that can be used to rank and select alternatives that are determined externally with reference to specific features is TOPSIS [56][57][58][59][60].…”

Section: ) Employing the Madm Method: Topsismentioning

confidence: 99%

SIM-PFED: A Simulation-Based Decision Making Model of Patient Flow for Improving Patient Throughput Time in Emergency Department

2021

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Healthcare sectors face multiple threats, and the hospital emergency department (ED) is one of the most crucial hospital areas. ED plays a key role in promoting hospitals' goals of enhancing service efficiency. ED is a complex system due to the stochastic behavior of patient arrivals, the unpredictability of the care required by patients, and the department's complex nature. Simulations are effective tools for analyzing and optimizing complex ED operations. Although existing ED simulation models have substantially improved ED performance in terms of ensuring patient satisfaction and effective treatment services, many deficiencies continue to exist in addressing the key challenge in ED, namely, long patient throughput time. The patient throughput time issue is affected by causative factors, such as waiting time, length of stay, and decision-making. This research aims to develop a new simulation model of patient flow for ED (SIM-PFED) to address the reported key challenge of the patient throughput time. SIM-PFED introduces a new process for patient flow in ED on the basis of the newly proposed operational patient flow by combining discrete event simulation and agent-based simulation and applying a multi-attribute decisionmaking method, namely, the technique for order preference by similarity to the ideal solution. Experiments were performed on three actual hospital ED datasets to assess the effectiveness of SIM-PFED. Experimental results revealed the superiority of SIM-PFED over other alternative models in reducing patient throughput time in ED by consuming less patient waiting time and having a shorter length of stay. The findings also demonstrated the effectiveness of SIM-PFED in helping ED decision-makers select the best scenarios to be implemented in ED for ensuring minimal throughput time while being cost effective.

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Section: ) Employing the Madm Method: Topsismentioning

confidence: 99%

SIM-PFED: A Simulation-Based Decision Making Model of Patient Flow for Improving Patient Throughput Time in Emergency Department

2021

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“…Collaborative Filtering content has component that uses a neighborhood-based algorithm. In neighborhood-based algorithms, a subset of users is choose based on their similarity to the active user, and weighted combination of their ratings is used to produce predictions for the active user [29,30].…”

Section: Technical Aspectsmentioning

confidence: 99%

Collaborative filtering content for parental control in mobile application chatting

Razali¹,

Ahmad²,

Diah³

2019

Bulletin EEI

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Mobile phone is an important medium of communication for most people regardless their age. One of the most used mobile phone application is chat application. Since mobile users are across different age groups, from youngsters to senior citizen, each age level has their own ways or styles of communication when using mobile application chatting. Adult mostly used proper syntax with complete sentences while youngsters normally use short forms with incomplete sentences. In addition, improper communication styles, usage of bad and inappropriate words has become a trend among youngsters. This matter gives negative impact on the education system in a short-term and national language preservation in a long term. Hence, in this paper, researchers present a tool that provides word recommendations for mobile application chatting. This tool would be is an education material to younger generation users with subtle approach. Implementation of the tool is by adapting Collaborative Filtering approach with User-Based model which focusing on recommendation on similar interest between users. Collaborative filtering content tool is functioning well during the functionality testing and it is thriving as a mobile application chatting guidance.

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“…Memory-and model-based techniques are commonly used to elucidate CF recommendations [3,[11][12][13][14][15]. Past studies have demonstrated the benefits of memory-based CF, wherein rating predictions are computed from the preferences of similar users via a rating matrix [12,[16][17][18][19]. Conversely, the model-based CF technique leverages a user-item rating matrix to initially build a predictive model using deep learning methods and then source the rating predictions from it [3,20].…”

Section: Introductionmentioning

confidence: 99%

Evolving Hierarchical and Tag Information via the Deeply Enhanced Weighted Non-Negative Matrix Factorization of Rating Predictions

2020

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Identifying the hidden features of items and users of a modern recommendation system, wherein features are represented as hierarchical structures, allows us to understand the association between the two entities. Moreover, when tag information that is added to items by users themselves is coupled with hierarchically structured features, the rating prediction efficiency and system personalization are improved. To this effect, we developed a novel model that acquires hidden-level hierarchical features of users and items and combines them with the tag information of items that regularizes the matrix factorization process of a basic weighted non-negative matrix factorization (WNMF) model to complete our prediction model. The idea behind the proposed approach was to deeply factorize a basic WNMF model to obtain hidden hierarchical features of user’s preferences and item characteristics that reveal a deep relationship between them by regularizing the process with tag information as an auxiliary parameter. Experiments were conducted on the MovieLens 100K dataset, and the empirical results confirmed the potential of the proposed approach and its superiority over models that use the primary features of users and items or tag information separately in the prediction process.

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An improved memory-based collaborative filtering method based on the TOPSIS technique

Cited by 29 publications

References 64 publications

SIM-PFED: A Simulation-Based Decision Making Model of Patient Flow for Improving Patient Throughput Time in Emergency Department

SIM-PFED: A Simulation-Based Decision Making Model of Patient Flow for Improving Patient Throughput Time in Emergency Department

Collaborative filtering content for parental control in mobile application chatting

Evolving Hierarchical and Tag Information via the Deeply Enhanced Weighted Non-Negative Matrix Factorization of Rating Predictions

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