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

2020 ◽  
Author(s):  
Yun Bai ◽  
Suling Jia ◽  
Shuangzhe Wang ◽  
Binkai Tan
Keyword(s):  
Bipartite Graph ◽  
Customer Loyalty ◽  
Recommender System ◽  
Dimensional Space ◽  
Recommendation Algorithm ◽  
Single Vertex ◽  
Recommendation Algorithms ◽  
Vertex Set ◽  
Time And Energy ◽  
Better Than

A good recommender system can infer customers’ preferences based on their historical purchase records, and recommend products that the customers may be interested in, saving them a lot of time and energy. For enterprises, it is difficult to recommend accurately to each customer, and the bad recommendation may be counterproductive. Customer loyalty is an indicator that measures the preference relationship between customers and products in the field of marketing. A hypothesis is proposed in this study: if companies can divide customers into different groups based on customer loyalty, the recommendation effect on certain groups is better than that on overall customers. In this study, customer loyalty is measured by four features of the RFML model. All customers are viewed as points on a four-dimensional space, which are clustered by the k-means model. Two recommendation algorithms based on complex networks are tested: recommendation algorithm based on bipartite graph and PersonalRank (BGPR), and recommendation algorithm based on a single vertex set network and DeepWalk (SVDW). The experimental results show that customer loyalty has improved the effectiveness of the two algorithms over 14%, and the recommendation effect is the best on customer groups with a loyalty level of 4 (the highest level is 5). The recommendation algorithms with customer loyalty are better than using them alone.

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.

Link community detection by non-negative matrix factorization with multi-step similarities

2016 ◽  
Vol 30 (32n33) ◽  
pp. 1650370 ◽  
Author(s):  
Xianchao Tang ◽  
Guoqing Yang ◽  
Tao Xu ◽  
Xia Feng ◽  
Xiao Wang ◽  
...  
Keyword(s):  
Random Walk ◽  
Objective Function ◽  
Network Topology ◽  
Matrix Factorization ◽  
Adjacency Matrix ◽  
Community Structures ◽  
Optimization Scheme ◽  
Penalty Term ◽  
The Relationship ◽  
Non Negative Matrix Factorization

Uncovering community structures is a fundamental and important problem in analyzing the complex networks. While most of the methods focus on identifying node communities, recent works show the intuitions and advantages of detecting link communities in networks. In this paper, we propose a non-negative matrix factorization (NMF) based method to detect the link community structures. Traditional NMF-based methods mainly consider the adjacency matrix as the representation of network topology, while the adjacency matrix only shows the relationship between immediate neighbor nodes, which does not take the relationship between non-neighbor nodes into consideration. This may greatly reduce the information contained in the network topology, and thus leads to unsatisfactory results. Here, we address this by introducing multi-step similarities using the graph random walk approach so that the similarities between non-neighbor nodes can be captured. Meanwhile, in order to reduce impact caused by self-similarities (similarities between nodes themselves) and increase importance gained from similarities between other different nodes, we add a penalty term to our objective function. Then an efficient optimization scheme for the objective function is derived. Finally, we test the proposed method on both synthetic and real networks. Experimental results demonstrate the effectiveness of the proposed approach.

scholarly journals Users’ Responsiveness to Persuasive Techniques in Recommender Systems

2021 ◽  
Vol 4 ◽  
Author(s):  
Alaa Alslaity ◽  
Thomas Tran
Keyword(s):  
Personality Traits ◽  
Recommender Systems ◽  
Recommender System ◽  
User Study ◽  
Application Domain ◽  
Recommendation Algorithm ◽  
Artificial Intelligent ◽  
Gender Culture ◽  
Different Characteristics ◽  
The Impact

Understanding user’s behavior and their interactions with artificial-intelligent-based systems is as important as analyzing the performance of the algorithms used in these systems. For instance, in the Recommender Systems domain, the accuracy of the recommendation algorithm was the ultimate goal for most systems designers. However, researchers and practitioners have realized that providing accurate recommendations is insufficient to enhance users’ acceptance. A recommender system needs to focus on other factors that enhance its interactions with the users. Recent researches suggest augmenting these systems with persuasive capabilities. Persuasive features lead to increasing users’ acceptance of the recommendations, which, in turn, enhances users’ experience with these systems. Nonetheless, the literature still lacks a comprehensive view of the actual effect of persuasive principles on recommender users. To fill this gap, this study diagnoses how users of different characteristics get influenced by various persuasive principles that a recommender system uses. The study considers four users’ aspects: age, gender, culture (continent), and personality traits. The paper also investigates the impact of the context (or application domain) on the influence of the persuasive principles. Two application domains (namely eCommerce and Movie recommendations) are considered. A within-subject user study was conducted. The analysis of (279) responses revealed that persuasive principles have the potential to enhance users’ experience with recommender systems. The study also shows that, among the considered factors, culture, personality traits, and the domain of recommendations have a higher impact on the influence of persuasive principles than other factors. Based on the analysis of the results, the study provides insights and guidelines for recommender systems designers. These guidelines can be used as a reference for designing recommender systems with users’ experience in mind. We suggest that considering the results presented in this paper could help to improve recommender-users interaction.

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.

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.

PENGARUH KUALITAS PRODUK DAN KUALITAS PELAYANAN TERHADAP PENINGKATAN KEPUASAN DAN LOYALITAS PELANGGAN PERUSAHAAN DAERAH AIR MINUM (PDAM) KOTA MAKASSAR

2019 ◽  
Vol 3 (2) ◽  
pp. 228-244 ◽  
Author(s):  
Muhammad Rezha Pahlawan ◽  
Nurlia Nurlia ◽  
Abdul Rahman Laba ◽  
Erlina Pakki ◽  
Hardiyono Hardiyono
Keyword(s):  
Customer Satisfaction ◽  
Service Quality ◽  
Product Quality ◽  
Customer Loyalty ◽  
Indirect Effects ◽  
Analysis Method ◽  
Water Company ◽  
The Relationship ◽  
Intervening Variables ◽  
Customer Satisfaction And Loyalty

This study aims to determine the effect of Product Quality and Service Quality on Increasing Customer Satisfaction and Loyalty in Makassar Municipal Water Company (PDAM) both directly and through intervening variables or indirect effects. This study uses a quantitative approach conducted with the aim to explain the position of the variables studied and the relationship between one variable with another variable. This research will explain the causal relationship between variables through hypothesis testing. In this study, the analysis method used is path analysis using the SmartPLS program. The results of this study found that directly the product quality had a positive and significant effect on customer satisfaction, service quality had a positive and significant effect on customer satisfaction, product quality had a positive and significant effect on customer loyalty, service quality had a positive and not significant effect on customer loyalty, customer satisfaction positive and significant effect on customer loyalty. The indirect effect of this research is product quality has a positive and significant effect on customer loyalty if mediated by customer satisfaction. Service quality also has a positive and significant effect on customer loyalty if mediated by customer satisfaction.

FEATURE,NILAI, KEPUASAN DAN LOYALITAS PELANGAN

2017 ◽  
Vol 2 (1) ◽  
pp. 45-52
Author(s):  
Iha Haryani Hatta
Keyword(s):  
Structural Equation Modeling ◽  
Customer Satisfaction ◽  
Customer Loyalty ◽  
Significant Influence ◽  
Customer Value ◽  
Structural Equation ◽  
Equation Modeling ◽  
Relationship Of ◽  
The Relationship ◽  
Customer Satisfaction And Loyalty

The aim of this study was to determine the relationship of the features of the value, satisfaction, and customer loyalty; the effect on the value of customer satisfaction and loyalty; influence of satisfaction on customer loyalty. A total of 200 merchant PT. BANK XYZ as respondents was selected randomly. Analyses were performed using structural equation modeling (SEM). The analysis showed that the feature has a significant influence on customer value, but not for customer satisfaction and loyalty. The values has a significant influence on satisfaction, but not on customer loyalty. Satisfaction has a significant influence on customer loyalty. Discussion and conclusions described in the article. Keywords: features of the value, satisfaction, customer loyalty

LOYALITAS PELANGGAN: PERAN CITRA MEREK DAN KUALITAS PRODUK

2019 ◽  
Vol 4 (3) ◽  
pp. 391-400
Author(s):  
Machmed Tun Ganyang
Keyword(s):  
Regression Analysis ◽  
Product Quality ◽  
Customer Loyalty ◽  
Brand Image ◽  
Dominant Factor ◽  
Consumer Loyalty ◽  
The Relationship

This study aims to examine the relationship between brand image and product quality on customer loyalty in sports products. Data were collected through a questionnaire of 68 respondents. Through regression analysis, the results show that brand image and quality can explain variations in consumer loyalty with the brand image as a dominant factor. This finding implies that producers can increase consumer loyalty by enhancing brand image. Companies need to think about how to develop key messages so that consumers can clearly understand the position of the product. The unique aspects of the product need to be improved to create a special impression on the customer. Keywords: brand image, produk quality, customer loyalty

Identifying Attack Models for Securing Cluster-Based Recommendation System

2020 ◽  
Vol 14 ◽  
Author(s):  
Amreen Ahmad ◽  
Tanvir Ahmad ◽  
Ishita Tripathi
Keyword(s):  
Collaborative Filtering ◽  
Recommender System ◽  
Recommendation System ◽  
Relevant Information ◽  
System Function ◽  
Recommendation Algorithm ◽  
Underlying Network ◽  
Entire Dataset ◽  
Attack Models ◽  
Wide Usage

: The immense growth of information has led to the wide usage of recommender systems for retrieving relevant information. One of the widely used methods for recommendation is collaborative filtering. However, such methods suffer from two problems, scalability and sparsity. In the proposed research, the two issues of collaborative filtering are addressed and a cluster-based recommender system is proposed. For the identification of potential clusters from the underlying network, Shapley value concept is used, which divides users into different clusters. After that, the recommendation algorithm is performed in every respective cluster. The proposed system recommends an item to a specific user based on the ratings of the item’s different attributes. Thus, it reduces the running time of the overall algorithm, since it avoids the overhead of computation involved when the algorithm is executed over the entire dataset. Besides, the security of the recommender system is one of the major concerns nowadays. Attackers can come in the form of ordinary users and introduce bias in the system to force the system function that is advantageous for them. In this paper, we identify different attack models that could hamper the security of the proposed cluster-based recommender system. The efficiency of the proposed research is validated by conducting experiments on student dataset.

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