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.

scholarly journals A study of neighbour selection strategies for POI recommendation in LBSNs

2018 ◽  
Vol 44 (6) ◽  
pp. 802-817 ◽  
Author(s):  
Carlos Rios ◽  
Silvia Schiaffino ◽  
Daniela Godoy
Keyword(s):  
Social Networks ◽  
Collaborative Filtering ◽  
Recommender Systems ◽  
Recommender System ◽  
Location Based Services ◽  
Geographical Information ◽  
Good Prediction ◽  
General Terms ◽  
The Impact ◽  
Filtering Approach

Location-based recommender systems (LBRSs) are gaining importance with the proliferation of location-based services provided by mobile devices as well as user-generated content in social networks. Collaborative approaches for recommendation rely on the opinions of like-minded people, so-called neighbours, for prediction. Thus, an adequate selection of such neighbours becomes essential for achieving good prediction results. The aim of this work is to explore different strategies to select neighbours in the context of a collaborative filtering–based recommender system for POI (places of interest) recommendations. Whereas standard methods are based on user similarity to delimit a neighbourhood, in this work several strategies are proposed based on direct social relationships and geographical information extracted from location-based social networks (LBSNs). The impact of the different strategies proposed has been evaluated and compared against the traditional collaborative filtering approach using a dataset from a popular network as Foursquare. In general terms, the proposed strategies for selecting neighbours based on the different elements available in a LBSN achieve better results than the traditional collaborative filtering approach. Our findings can be helpful both to researchers in the recommender systems area and to recommender system developers in the context of LBSNs, since they can take into account our results to design and provide more effective services considering the huge amount of knowledge produced in LBSNs.

scholarly journals Dynamic Group Recommendation Algorithm Based on Member Activity Level

2021 ◽  
Vol 2021 ◽  
pp. 1-18
Author(s):  
Junjie Jia ◽  
Yewang Yao ◽  
Zhipeng Lei ◽  
Pengtao Liu
Keyword(s):  
Recommender Systems ◽  
Activity Level ◽  
Group Recommendation ◽  
Recommendation Algorithm ◽  
Group Recommender Systems ◽  
Dynamic Group ◽  
Recent Activity ◽  
Group Profile ◽  
The Impact ◽  
Group Recommender

The rapid development of social networks has led to an increased desire for group entertainment consumption, making the study of group recommender systems a hotspot. Existing group recommender systems focus too much on member preferences and ignore the impact of member activity level on recommendation results. To this end, a dynamic group recommendation algorithm based on the activity level of members is proposed. Firstly, the algorithm predicts the unknown preferences of members using a time-series-oriented rating prediction model. Secondly, considering the dynamic change of member activity level, the group profile is generated by designing a sliding time window to investigate the recent activity level of each member in the group at the recommended moment, and preference is aggregated based on the recent activity level of members. Finally, the group recommendations are generated based on the group profile. The experimental results show that the algorithm in this paper achieves a better recommendation result.

scholarly journals Contextual Personality-Aware Recommender System Versus Big Data Recommender System

2021 ◽  
pp. 163-173
Author(s):  
Marcin Szmydt
Keyword(s):  
Big Data ◽  
Personality Traits ◽  
Collaborative Filtering ◽  
Recommender Systems ◽  
Recommender System ◽  
Factor Model ◽  
Five Factor Model ◽  
Customer Reviews ◽  
System Versus ◽  
Personality Theories

Many personality theories suggest that personality influences customer shopping preference. Thus, this research analyses the potential ability to improve the accuracy of the collaborative filtering recommender system by incorporating the Five-Factor Model personality traits data obtained from customer text reviews. The study uses a large Amazon dataset with customer reviews and information about verified customer product purchases. However, evaluation results show that the model leveraging big data by using the whole Amazon dataset provides better recommendations than the recommender systems trained in the contexts of the customer personality traits.

Recommender System: Towards Classification of Human Intentions in E-Shopping Using Machine Learning

2019 ◽  
Vol 16 (10) ◽  
pp. 4280-4285
Author(s):  
Babaljeet Kaur ◽  
Richa Sharma ◽  
Shalli Rani ◽  
Deepali Gupta
Keyword(s):  
Machine Learning ◽  
Recommender Systems ◽  
Recommender System ◽  
Machine Learning Algorithms ◽  
Application Domain ◽  
Random Forest Algorithm ◽  
Manual Search ◽  
Online Shoppers ◽  
New Item

Recommender systems were introduced in mid-1990 for assisting the users to choose a correct product from innumerable choices available. The basic concept of a recommender system is to advise a new item or product to the users instead of the manual search, because when user wants to buy a new item, he is confused about which item will suit him better and meet the intended requirements. From google news to netflix and from Instagram to LinkedIn, recommender systems have spread their roots in almost every application domain possible. Now a days, lots of recommender system are available for every field. In this paper, overview of recommender system, recommender approaches, application areas and the challenges of recommender system, is given. Further, we study conduct an experiment on online shoppers’ intention to predict the behavior of shoppers using Machine learning algorithms. Based on the results, it is observed that Random forest algorithm performs the best with 93% ROC value.

scholarly journals A genre trust model for defending shilling attacks in recommender systems

2021 ◽  
Author(s):  
Li Yang ◽  
Xinxin Niu
Keyword(s):  
Collaborative Filtering ◽  
Recommender Systems ◽  
Recommender System ◽  
Trust Model ◽  
Trust Value ◽  
Shilling Attack ◽  
Trust Degree ◽  
Different Types ◽  
And Behavior ◽  
The Impact

AbstractShilling attacks have been a significant vulnerability of collaborative filtering (CF) recommender systems, and trust in CF recommender algorithms has been proven to be helpful for improving the accuracy of system recommendations. As a few studies have been devoted to trust in this area, we explore the benefits of using trust to resist shilling attacks. Rather than simply using user-generated trust values, we propose the genre trust degree, which differ in terms of the genres of items and take both trust value and user credibility into consideration. This paper introduces different types of shilling attack methods in an attempt to study the impact of users’ trust values and behavior features on defending against shilling attacks. Meanwhile, it improves the approach used to calculate user similarities to form a recommendation model based on genre trust degrees. The performance of the genre trust-based recommender system is evaluated on the Ciao dataset. Experimental results demonstrated the superior and comparable genre trust degrees recommended for defending against different types of shilling attacks.

scholarly journals Customer Satisfaction of Recommender System: Examining Accuracy and Diversity in Several Types of Recommendation Approaches

2021 ◽  
Vol 13 (11) ◽  
pp. 6165
Author(s):  
Jae-Kyeong Kim ◽  
Il-Young Choi ◽  
Qinglong Li
Keyword(s):  
Customer Satisfaction ◽  
Recommender Systems ◽  
Recommender System ◽  
Data Sets ◽  
Sustainable Competitive Advantage ◽  
Customer Data ◽  
The Sustainable Development ◽  
Diverse Data ◽  
And Behavior ◽  
The Impact

Information technology and the popularity of mobile devices allow for various types of customer data, such as purchase history and behavior patterns, to be collected. As customer data accumulate, the demand for recommender systems that provide customized services to customers is growing. Global e-commerce companies offer recommender systems to gain a sustainable competitive advantage. Research on recommender systems has consistently suggested that customer satisfaction will be highest when the recommendation algorithm is accurate and recommends a diversity of items. However, few studies have investigated the impact of accuracy and diversity on customer satisfaction. In this research, we seek to identify the factors determining customer satisfaction when using the recommender system. To this end, we develop several recommender systems and measure their ability to deliver accurate and diverse recommendations and their ability to generate customer satisfaction with diverse data sets. The results show that accuracy and diversity positively affect customer satisfaction when applying a deep learning-based recommender system. By contrast, only accuracy positively affects customer satisfaction when applying traditional recommender systems. These results imply that developers or managers of recommender systems need to identify factors that further improve customer satisfaction with the recommender system and promote the sustainable development of e-commerce.

Interpretable video tag recommendation with multimedia deep learning framework

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Zekun Yang ◽  
Zhijie Lin
Keyword(s):  
Deep Learning ◽  
Recommender Systems ◽  
Recommender System ◽  
User Study ◽  
Customer Engagement ◽  
It Adoption ◽  
Content Type ◽  
Video Sharing ◽  
Learning Framework ◽  
Proposed Model

PurposeTags help promote customer engagement on video-sharing platforms. Video tag recommender systems are artificial intelligence-enabled frameworks that strive for recommending precise tags for videos. Extant video tag recommender systems are uninterpretable, which leads to distrust of the recommendation outcome, hesitation in tag adoption and difficulty in the system debugging process. This study aims at constructing an interpretable and novel video tag recommender system to assist video-sharing platform users in tagging their newly uploaded videos.Design/methodology/approachThe proposed interpretable video tag recommender system is a multimedia deep learning framework composed of convolutional neural networks (CNNs), which receives texts and images as inputs. The interpretability of the proposed system is realized through layer-wise relevance propagation.FindingsThe case study and user study demonstrate that the proposed interpretable multimedia CNN model could effectively explain its recommended tag to users by highlighting keywords and key patches that contribute the most to the recommended tag. Moreover, the proposed model achieves an improved recommendation performance by outperforming state-of-the-art models.Practical implicationsThe interpretability of the proposed recommender system makes its decision process more transparent, builds users’ trust in the recommender systems and prompts users to adopt the recommended tags. Through labeling videos with human-understandable and accurate tags, the exposure of videos to their target audiences would increase, which enhances information technology (IT) adoption, customer engagement, value co-creation and precision marketing on the video-sharing platform.Originality/valueThe proposed model is not only the first explainable video tag recommender system but also the first explainable multimedia tag recommender system to the best of our knowledge.

scholarly journals IMPACT ANALYSIS OF PROFILE INJECTION ATTACKS IN RECOMMENDER SYSTEM

2021 ◽  
Vol 9 (1) ◽  
pp. 472-478
Author(s):  
M Ashish Kumar, Yudhvir Singh, Vikas Siwach, Harkesh Sehrawat
Keyword(s):  
Recommender Systems ◽  
Recommender System ◽  
Impact Analysis ◽  
Service Providers ◽  
Data Set ◽  
Injection Attacks ◽  
Attack Scenario ◽  
The Right ◽  
The Impact

Recommender systems are the backbone of all the prediction-based service platforms e.g. Facebook, Amazon, LinkedIn etc. Even companies now a days are using the recommender systems to show users personalized ads. These service providers capture the right audience for their services/ products and hence, improve overall sales. Social networking platforms are using recommender systems for connecting people of similar interests which is almost impossible without recommender systems.  Collaborative filtering-based recommender system is most widely used recommender system. It is used in this research to predict the rating for a specific movie. Accuracy of the prediction define the performance of the overall system. The quality of predictions is degraded by the attackers by injection of fake profiles. In this paper, the various types of profile injection attacks are explained and the attack scenario gets extended to measure the performance of these attacks. Empirical results on the real world publicly available data set shows that these attacks are highly vulnerable. The impact of these attacks in several conditions has been measured and it is tried to find the scenarios where these attacks are more powerful.

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.

Sign in / Sign up

Export Citation Format

Share Document