A Novel String Distance Function Based on Most Frequent K Characters

Şeker, Şadi Evren; Altun, Oğuz; Ayan, Uğur; Mert, Cihan

doi:10.7763/ijmlc.2014.v4.408

Cited by 10 publications

(5 citation statements)

References 7 publications

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“…e lexical item-based recommendation is classified as content recommendation, which directly uses the review text to model users and products. Seker et al [16] extracted lexical items from users' published reviews and generates a user model with TF-IDF (term frequency-inverse document frequency) as lexical item weights, and the product model is based on the review set of the target product and finally makes recommendations based on the content similarity between the two. e literature recommendation system of [17] models the user based on the literature he has read, characterizes the lexical items with word vectors, and calculates the similarity between the user and the recommendation target (literature) up to the semantic level.…”

Section: Research On Review-based Recommendation Systemsmentioning

confidence: 99%

Construction of Machine Learning Model Based on Text Mining and Ranking of Meituan Merchants

Tang

Liao

Huang

et al. 2021

Scientific Programming

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In the Web 2.0 era, the problem of uneven quality and overload of online reviews is very serious, and the cognitive cost of obtaining valuable content from them is getting higher and higher. This paper explores an effective solution to address comment overload by means of information recommendation in order to improve the utilization of online information and information service quality. This paper proposes a review ranking recommendation scheme that focuses on the information quality of reviews and places more emphasis on satisfying users’ personal information need. The paper’s approach is used to extract and rank low-frequency keywords that appear only once in the comment set. The more useful the extracted phrases are, the more useful this review will be and the higher the usefulness votes will be, which can reflect the actual situation of this product more objectively and accurately and facilitate better consumption decisions for consumers. The experimental results show that users’ satisfaction with the perceived usefulness of the reviews is jointly influenced by the information quality of Meituan’s reviews and users’ individual information needs; the recommendation strategy achieves the organic integration of the two, and the evaluation results under three different recommendation modes show that compared with “interest recommendation” and “utility recommendation,” the satisfaction score of “fusion recommendation” is the highest

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Section: Research On Review-based Recommendation Systemsmentioning

confidence: 99%

Construction of Machine Learning Model Based on Text Mining and Ranking of Meituan Merchants

Tang

Liao

Huang

et al. 2021

Scientific Programming

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“…The distance function can be based on a natural language processing or a string distance function [28], [29].…”

Section: Sample Scenario For the Computerized Argument Delphi Mementioning

confidence: 99%

Computerized Argument Delphi Technique

Şeker

2015

IEEE Access

Self Cite

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The aim of this study is the computerization of the argument Delphi method. The Delphi method is mainly designed for qualitative prediction within a group of experts, where the experts make predictions and a facilitator controls these predictions until the experts end up with a level of consensus. Argument Delphi, as opposed to the classical Delphi model, is built on the contradictions of the ideas of the experts. Argument Delphi mainly focuses on a discussion topic and asks experts to create new arguments and criticize other arguments from other experts. After a certain level of contradiction, the method yields an amount of contradictory, criticized arguments and builds a decision over these antitheses, as in the Hegelian approach. This is the first time the argument Delphi method has been modeled in a graph of arguments and the problem of qualitative decision has been transferred into a graph problem using Delphi method. This paper is also the first time that argument aggregation and evaluation methods have been proposed. Moreover, the computerized version of argument Delphi is applied to real-world problems using crowd involvement through Facebook. The problem is defined as the prediction of petroleum prices for the end of year and more than 100 contributors from all around the world argued and criticized each other. This paper also discusses the findings of this case study. INDEX TERMSDelphi technique, decision support systems, qualitative methods, graph theory, social network, crowd opinion.

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“…Geometric distances are often measured in the Euclidean space, where a distance is a numerical description of the way items are lying far apart (Seker, Altun, Ayan & Mert, 2014). Distance is a metric function to describe that items are "close to" or "far away from" each other.…”

Section: Geometric Distancementioning

confidence: 99%

Optimized consortium formation through cluster analysis

Mampana¹,

Seeletse²,

Sithole³

2016

Problems and Perspectives in Management

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Some problems cannot be solved optimally and compromises become necessary. In some cases obtaining an optimal solution may require combining algorithms and iterations. This often occurs when the problem is complex and a single procedure does not reach optimality. This paper shows a conglomerate of algorithms iterated in tasks to form an optimal consortium using cluster analysis. Hierarchical methods and distance measures lead the process. Few companies are desirable in optimal consortium formation. However, this study shows that optimization cannot be predetermined based on a specific fixed number of companies. The experiential exercise forms an optimal consortium of four companies from six shortlisted competitors

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A Novel String Distance Function Based on Most Frequent K Characters

Cited by 10 publications

References 7 publications

Construction of Machine Learning Model Based on Text Mining and Ranking of Meituan Merchants

Construction of Machine Learning Model Based on Text Mining and Ranking of Meituan Merchants

Computerized Argument Delphi Technique

Optimized consortium formation through cluster analysis

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