A Recommendation Method Based on Semantic Similarity and Complementarity Using Weighted Taxonomy: A Case on Construction Materials Dataset

2018 ◽  
Vol 17 (01) ◽  
pp. 1850010
Author(s):  
Karamollah Bagherifard ◽  
Mohsen Rahmani ◽  
Vahid Rafe ◽  
Mehrbakhsh Nilashi
Keyword(s):  
Data Mining ◽  
Recommender Systems ◽  
Semantic Similarity ◽  
Recommender System ◽  
Construction Materials ◽  
Similar Degree ◽  
Web Pages ◽  
New Approach ◽  
Main Components ◽  
The Relationship

Products and web pages are the main components of the e-commerce data knowledge and the relationship among them is an important issue to be highly considered in recommender systems. This study aims to focus on the similarity and complementarity relationships among the products that have wide applications in the recommender systems. In the previously proposed methods, products and their relationships were revealed using taxonomy and “IS-A” relationship. In addition, the similarity and complementarity calculations were conducted based on edge computation by assigning a similar degree to any edge. More specifically, the children of a concept in the taxonomy was supported by a similar father’s “IS-A” degree. In contrast, this study provides a new approach based on ontology, data mining, and automatic discovering algorithms for the relationships with different degrees for the edges among the concepts. Accordingly, these relationships are initialised according to the “IS-A” degree. With regard to this weighted taxonomy, the semantic similarity and complementarity are measured based on concept distance. In addition, the proposed recommender system is item-based, which uses semantic similarity and complementarity. The required data for the present study were collected from construction materials supplier. The results illustrated that our proposed method is effective for construction materials recommendation.

A Framework to Analyze Business Process Log in XML Format

2021 ◽  
Vol 12 (3) ◽  
pp. 2623-2630
Author(s):  
Ang Jin Sheng Et.al
Keyword(s):  
Data Mining ◽  
Business Process ◽  
Accurate Result ◽  
Structured Data ◽  
Web Pages ◽  
Web Searching ◽  
General Application ◽  
Data Mining Techniques ◽  
Xml Documents ◽  
The Relationship

XML has numerous uses in a wide variety of web pages and applications. Some common uses of XML include tasks for web publishing, web searching and automation, and general application such as for utilize, store, transfer and display business process log data. The amount of information expressed in XML has gone up rapidly. Many works have been done on sensible approaches to address issues related to the handling and review of XML documents. Mining XML documents offera way to understand both the structure and the content of XML documents. A common approach capable of analysing XML documents is frequent subtree mining.Frequent subtree mining is one of the data mining techniques that finds the relationship between transactions in a tree structured database. Due to the structure and the content of XML format, traditional data mining and statistical analysis hardly applied to get accurate result. This paper proposes a framework that can flatten a tree structured data into a flat and structured data, while preserving their structure and content.Enabling these XML documents into relational structured data allows a range of data mining techniques and statistical test can be applied and conducted to extract more information from the business process log.

scholarly journals Identification and Prediction of Human Behavior through Mining of Unstructured Textual Data

Symmetry ◽  
2020 ◽  
Vol 12 (11) ◽  
pp. 1902
Author(s):  
Mohammad Reza Davahli ◽  
Waldemar Karwowski ◽  
Edgar Gutierrez ◽  
Krzysztof Fiok ◽  
Grzegorz Wróbel ◽  
...  
Keyword(s):  
Social Sciences ◽  
Data Mining ◽  
Human Behavior ◽  
Leading Indicators ◽  
Inclusion Criteria ◽  
Primary Methods ◽  
Textual Data ◽  
Main Components ◽  
The Relationship ◽  
Insight Into

The identification of human behavior can provide useful information across multiple job spectra. Recent advances in applying data-based approaches to social sciences have increased the feasibility of modeling human behavior. In particular, studying human behavior by analyzing unstructured textual data has recently received considerable attention because of the abundance of textual data. The main objective of the present study was to discuss the primary methods for identifying and predicting human behavior through the mining of unstructured textual data. Of the 823 articles analyzed, 87 met the predefined inclusion criteria and were included in the literature review. Our results show that the included articles could be symmetrically classified into two groups. The first group of articles attempted to identify the leading indicators of human behavior in unstructured textual data. In this group, the data-based approaches had three main components: (1) collecting self-reported survey data, (2) collecting data from social media and extracting data features, and (3) applying correlation analysis to evaluate the relationship between two sets of data. In contrast, the second group focused on the accuracy of data-based approaches for predicting human behavior. In this group, the data-based approaches could be categorized into (1) approaches based on labeled unstructured textual data and (2) approaches based on unlabeled unstructured textual data. The review provides a comprehensive insight into unstructured textual data mining to identify and predict human behavior and personality traits.

scholarly journals AKNOBAS: A knowledge-based segmentation recommender system based on intelligent data mining techniques

2012 ◽  
Vol 9 (2) ◽  
pp. 713-740 ◽  
Author(s):  
Alejandro Rodríguez-González ◽  
Javier Torres-Niño ◽  
Enrique Jimenez-Domingo ◽  
Miguel Gomez-Berbis ◽  
Giner Alor-Hernandez
Keyword(s):  
Data Mining ◽  
Competitive Advantage ◽  
Recommender Systems ◽  
Business Model ◽  
Recommender System ◽  
Added Value ◽  
Proof Of Concept ◽  
Clustering Techniques ◽  
Knowledge Based ◽  
Substantial Progress

Recommender Systems have recently undergone an unwavering improvement in terms of efficiency and pervasiveness. They have become a source of competitive advantage in many companies which thrive on them as the technological core of their business model. In recent years, we have made substantial progress in those Recommender Systems outperforming the accuracy and added-value of their predecessors, by using cutting-edge techniques such as Data Mining and Segmentation. In this paper, we present AKNOBAS, a Knowledge-based Segmentation Recommender System, which follows that trend using Intelligent Clustering Techniques for Information Systems. The contribution of this Recommender System has been validated through a business scenario implementation proof-of-concept and provides a clear breakthrough of marshaling information through AI techniques.

Challenges and Applications of Recommender Systems in E-Commerce

2021 ◽  
pp. 175-188
Author(s):  
Taushif Anwar ◽  
V. Uma ◽  
Md Imran Hussain
Keyword(s):  
Recommender Systems ◽  
User Experience ◽  
Recommender System ◽  
Recommendation System ◽  
Main Idea ◽  
Important Task ◽  
Significant Challenge ◽  
Online Business ◽  
The One ◽  
The Relationship

E-commerce and online business are getting too much attention and popularity in this era. A significant challenge is helping a customer through the recommendation of a big list of items to find the one they will like the most efficiently. The most important task of a recommendation system is to improve user experience through the most relevant recommendation of items based on their past behaviour. In e-commerce, the main idea behind the recommender system is to establish the relationship between users and items to recommend the most relevant items to the particular user. Most of the e-commerce websites such as Amazon, Flipkart, E-Bay, etc. are already applying the recommender system to assist their users in finding appropriate items. The main objective of this chapter is to illustrate and examine the issues, attacks, and research applications related to the recommender system.

scholarly journals Apriori Algorithm for the Data Mining of Global Cyberspace Security Issues for Human Participatory Based on Association Rules

2021 ◽  
Vol 11 ◽  
Author(s):  
Zhi Li ◽  
Xuyu Li ◽  
Runhua Tang ◽  
Lin Zhang
Keyword(s):  
Data Mining ◽  
Association Rules ◽  
Total Sample ◽  
Web Pages ◽  
Apriori Algorithm ◽  
Cyber Crime ◽  
Cyber Attack ◽  
Security Issues ◽  
The Relationship ◽  
Cyberspace Security

This study explored the global cyberspace security issues, with the purpose of breaking the stereotype of people’s cognition of cyberspace problems, which reflects the relationship between interdependence and association. Based on the Apriori algorithm in association rules, a total of 181 strong rules were mined from 40 target websites and 56,096 web pages were associated with global cyberspace security. Moreover, this study analyzed support, confidence, promotion, leverage, and reliability to achieve comprehensive coverage of data. A total of 15,661 sites mentioned cyberspace security-related words from the total sample of 22,493 professional websites, accounting for 69.6%, while only 735 sites mentioned cyberspace security-related words from the total sample of 33,603 non-professional sites, accounting for 2%. Due to restrictions of language, the number of samples of target professional websites and non-target websites is limited. Meanwhile, the number of selections of strong rules is not satisfactory. Nowadays, the cores of global cyberspace security issues include internet sovereignty, cyberspace security, cyber attack, cyber crime, data leakage, and data protection.

An efficient semantic recommender method forArabic text

2019 ◽  
Vol 37 (2) ◽  
pp. 263-280 ◽  
Author(s):  
Bilal Hawashin ◽  
Shadi Alzubi ◽  
Tarek Kanan ◽  
Ayman Mansour
Keyword(s):  
Recommender Systems ◽  
Semantic Similarity ◽  
Recommender System ◽  
User Preferences ◽  
English Text ◽  
Arabic Text ◽  
Content Type ◽  
Semantic Relationships ◽  
Value Decomposition ◽  
Semantic Aspect

PurposeThis paper aims to propose a new efficient semantic recommender method for Arabic content.Design/methodology/approachThree semantic similarities were proposed to be integrated with the recommender system to improve its ability to recommend based on the semantic aspect. The proposed similarities are CHI-based semantic similarity, singular value decomposition (SVD)-based semantic similarity and Arabic WordNet-based semantic similarity. These similarities were compared with the existing similarities used by recommender systems from the literature.FindingsExperiments show that the proposed semantic method using CHI-based similarity and using SVD-based similarity are more efficient than the existing methods on Arabic text in term of accuracy and execution time.Originality/valueAlthough many previous works proposed recommender system methods for English text, very few works concentrated on Arabic Text. The field of Arabic Recommender Systems is largely understudied in the literature. Aside from this, there is a vital need to consider the semantic relationships behind user preferences to improve the accuracy of the recommendations. The contributions of this work are the following. First, as many recommender methods were proposed for English text and have never been tested on Arabic text, this work compares the performance of these widely used methods on Arabic text. Second, it proposes a novel semantic recommender method for Arabic text. As this method uses semantic similarity, three novel base semantic similarities were proposed and evaluated. Third, this work would direct the attention to more studies in this understudied topic in the literature.

scholarly journals Customer Loyalty Improves the Effectiveness of Recommender Systems Based on Complex Network

Information ◽  
2020 ◽  
Vol 11 (3) ◽  
pp. 171 ◽  
Author(s):  
Yun Bai ◽  
Suling Jia ◽  
Shuangzhe Wang ◽  
Binkai Tan
Keyword(s):  
Random Walk ◽  
Recommender Systems ◽  
Network Topology ◽  
Customer Loyalty ◽  
Recommender System ◽  
Recommendation Algorithm ◽  
Single Vertex ◽  
Vertex Set ◽  
Product Recommendations ◽  
The Relationship

Inferring customers’ preferences and recommending suitable products is a challenging task for companies, although recommender systems are constantly evolving. Loyalty is an indicator that measures the preference relationship between customers and products in the field of marketing. To this end, the aim of this study is to explore whether customer loyalty can improve the accuracy of the recommender system. Two algorithms based on complex networks are proposed: a recommendation algorithm based on bipartite graph and PersonalRank (BGPR), and a recommendation algorithm based on single vertex set network and DeepWalk (SVDW). In both algorithms, loyalty is taken as an attribute of the customer, and the relationship between customers and products is abstracted into the network topology. During the random walk among nodes in the network, product recommendations for customers are completed. Taking a real estate group in Malaysia as an example, the experimental results verify that customer loyalty can indeed improve the accuracy of the recommender system. We can also conclude that companies are more effective at recommending customers with moderate loyalty levels.

scholarly journals A Movie Recommender System using Ontology Based Semantic Similarity Measure

2019 ◽  
Vol 8 (12) ◽  
pp. 5490-5496
Keyword(s):  
Recommender Systems ◽  
Semantic Similarity ◽  
Similarity Measure ◽  
Recommender System ◽  
Domain Knowledge ◽  
Information Overload ◽  
Vital Role ◽  
The Internet ◽  
Semantic Similarity Measure ◽  
Usage Data

Continuous growth in information available on the Internet overwhelms the users during navigation. This information overload may result in users’ dissatisfaction which is undesirable. Users’ satisfaction is very important aspect in every domain. Recommender systems play a vital role in dealing with information overload problems. The recommender systems filter the huge information on the Internet to generate limited and personalized information to users. This helps in increasing users' satisfaction by retaining his/her interests during navigation. Pure Web usage data based recommender systems have been used from last few years. However, they lag in precise recommendations because of absence of domain knowledge. Further, the similarity measures play a vital role in recommendation process and hence affect the performance of the recommender systems. The performance of recommender systems can be enhanced through integration of domain knowledge with usage data. This paper presents an approach to movie recommender system that integrates domain knowledge with usage data. The ontology is used to represent domain knowledge. The proposed approach is based on a new ontology based semantic similarity measure. The experimental results prove that the recommendations’ quality andaccuracy of prediction can be enhanced through integration of ontological domain knowledge with Web usage data.

scholarly journals Assessment Methods for Evaluation of Recommender Systems: A Survey

2021 ◽  
Vol 46 (4) ◽  
pp. 393-421
Author(s):  
Madhusree Kuanr ◽  
Puspanjali Mohapatra
Keyword(s):  
Recommender Systems ◽  
Recommender System ◽  
Information Filtering ◽  
Assessment Methods ◽  
Past Behavior ◽  
Large Pool ◽  
Near Future ◽  
The Relationship

Abstract The recommender system (RS) filters out important information from a large pool of dynamically generated information to set some important decisions in terms of some recommendations according to the user’s past behavior, preferences, and interests. A recommender system is the subclass of information filtering systems that can anticipate the needs of the user before the needs are recognized by the user in the near future. But an evaluation of the recommender system is an important factor as it involves the trust of the user in the system. Various incompatible assessment methods are used for the evaluation of recommender systems, but the proper evaluation of a recommender system needs a particular objective set by the recommender system. This paper surveys and organizes the concepts and definitions of various metrics to assess recommender systems. Also, this survey tries to find out the relationship between the assessment methods and their categorization by type.

Colonialism and the Highland Clearances

2017 ◽  
Vol 8 (1) ◽  
pp. 22-48 ◽  
Author(s):  
Iain Mackinnon
Keyword(s):  
Industrial Development ◽  
Comparative Approach ◽  
Indigenous Research ◽  
Internal Colonialism ◽  
New Approach ◽  
A Site ◽  
Relationship Of ◽  
The Relationship ◽  
Imperial State ◽  
Late Modern

This article employs a new approach to studying internal colonialism in northern Scotland during the 18th and 19th centuries. A common approach to examining internal colonial situations within modern state territories is to compare characteristics of the internal colonial situation with attested attributes of external colonial relations. Although this article does not reject the comparative approach, it seeks to avoid criticisms that this approach can be misleading by demonstrating that promoters and managers of projects involving land use change, territorial dispossession and industrial development in the late modern Gàidhealtachd consistently conceived of their work as projects of colonization. It further argues that the new social, cultural and political structures these projects imposed on the area's indigenous population correspond to those found in other colonial situations, and that racist and racialist attitudes towards Gaels of the time are typical of those in colonial situations during the period. The article concludes that the late modern Gàidhealtachd has been a site of internal colonization where the relationship of domination between colonizer and colonized is complex, longstanding and occurring within the imperial state. In doing so it demonstrates that the history and present of the Gaels of Scotland belongs within the ambit of an emerging indigenous research paradigm.

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