scholarly journals Do You Buy or Not? The Effect of Recommender Slogans on Retail Sites

2021 ◽  
Vol 11 (1) ◽  
pp. 11-20
Author(s):  
Hsiaoping Yeh ◽  
◽  
Tsung-Sheng Chang ◽  
Fenghung Kuo
Keyword(s):  
Recommender Systems ◽  
Information Overload ◽  
Relevant Information ◽  
Purchase Decisions ◽  
Price Promotion ◽  
Product Categories ◽  
Current Information ◽  
Online Retailers ◽  
Online Shoppers ◽  
Retail Shopping

Recommender systems solve the current information overload problem in the online world. By predicting and presenting relevant information, web users do not need to waste time searching and browsing for contents that they are interested in. However, in addition to the accurate contents, slogans associated catching the customers’ eyes are worthy of exploration. This study aims to discover the effects of various recommender slogans. Two categories of slogans were designed in the study: slogans associated with customer inputs and slogans associated with price promotion. Actual customers’ webpage clickstreams and purchase decisions were collected from a Taiwanese retail shopping Website. The effects of recommender slogans on product categories are different. Customers generally were drawn by the slogans associated with price promotion. This study brought to light the effects of different slogans on online shoppers. With the empirical findings, this study provides online retailers important guidelines regarding online customers’ behaviors towards the employment of recommender slogans.

Enhancing Citizens' Participation via Recommender Systems

2020 ◽  
pp. 624-650
Author(s):  
Luis Terán
Keyword(s):  
Decision Making ◽  
Semantic Web ◽  
Private Sector ◽  
Web 2.0 ◽  
Recommender Systems ◽  
Search Engines ◽  
Information Overload ◽  
Relevant Information ◽  
Research Topic ◽  
Government Services

With the introduction of Web 2.0, which includes users as content generators, finding relevant information is even more complex. To tackle this problem of information overload, a number of different techniques have been introduced, including search engines, Semantic Web, and recommender systems, among others. The use of recommender systems for e-Government is a research topic that is intended to improve the interaction among public administrations, citizens, and the private sector through reducing information overload on e-Government services. In this chapter, the use of recommender systems on eParticipation is presented. A brief description of the eGovernment Framework used and the participation levels that are proposed to enhance participation. The highest level of participation is known as eEmpowerment, where the decision-making is placed on the side of citizens. Finally, a set of examples for the different eParticipation types is presented to illustrate the use of recommender systems.

scholarly journals Using a Fuzzy-Based Cluster Algorithm for Recommending Candidates in E-Elections

2012 ◽  
pp. 684-705 ◽  
Author(s):  
Luis Terán ◽  
Andreas Ladner ◽  
Jan Fivaz ◽  
Stefani Gerber
Keyword(s):  
Recommender Systems ◽  
Information And Communication Technologies ◽  
Information Overload ◽  
Relevant Information ◽  
The Internet ◽  
Clustering Methods ◽  
Web Based ◽  
Information And Communication ◽  
Fuzzy Clustering Methods ◽  
Use Of The Internet

The use of the Internet now has a specific purpose: to find information. Unfortunately, the amount of data available on the Internet is growing exponentially, creating what can be considered a nearly infinite and ever-evolving network with no discernable structure. This rapid growth has raised the question of how to find the most relevant information. Many different techniques have been introduced to address the information overload, including search engines, Semantic Web, and recommender systems, among others. Recommender systems are computer-based techniques that are used to reduce information overload and recommend products likely to interest a user when given some information about the user’s profile. This technique is mainly used in e-Commerce to suggest items that fit a customer’s purchasing tendencies. The use of recommender systems for e-Government is a research topic that is intended to improve the interaction among public administrations, citizens, and the private sector through reducing information overload on e-Government services. More specifically, e-Democracy aims to increase citizens’ participation in democratic processes through the use of information and communication technologies. In this chapter, an architecture of a recommender system that uses fuzzy clustering methods for e-Elections is introduced. In addition, a comparison with the smartvote system, a Web-based Voting Assistance Application (VAA) used to aid voters in finding the party or candidate that is most in line with their preferences, is presented.

scholarly journals Crowdsourced Recommender System

2018 ◽  
Author(s):  
Mario Mallia Milanes ◽  
Matthew Montebello
Keyword(s):  
Web 2.0 ◽  
Recommender Systems ◽  
Recommender System ◽  
Information Overload ◽  
Relevant Information ◽  
Learning Systems ◽  
Learning Material ◽  
Timely Manner ◽  
Online Learners ◽  
E Learning

The use of artificially intelligent techniques to overcome specific shortcomings within e-learning systems is a well-researched area that keeps on evolving in an attempt to optimise such resourceful practices. The lack of personalization and the sentiment of isolation coupled with a feeling of being treated like all others, tends to discourage and push learners away from courses that are very well prepared academically and excellently projected intellectually. The use of recommender systems to deliver relevant information in a timely manner that is specifically differentiated to a unique learner is once more being investigated to alievate the e-learning issue of being impersonal.  The application of such a technique also assists the learner by reducing information overload and providing learning material that can be shared, criticized and reviewed at one’s own pace. In this paper we propose the use of a fully automated recommender system based on recent AI developments together with Web 2.0 applications and socially networked technologies. We argue that such technologies have provided the extra capabilities that were required to deliver a realistic and practical interfacing medium to assist online learners and take recommender systems to the next level.

Using a Fuzzy-Based Cluster Algorithm for Recommending Candidates in E-Elections

2012 ◽  
pp. 115-138 ◽  
Author(s):  
Luis Terán ◽  
Andreas Ladner ◽  
Jan Fivaz ◽  
Stefani Gerber
Keyword(s):  
Recommender Systems ◽  
Information And Communication Technologies ◽  
Information Overload ◽  
Relevant Information ◽  
The Internet ◽  
Clustering Methods ◽  
Web Based ◽  
Information And Communication ◽  
Fuzzy Clustering Methods ◽  
Use Of The Internet

The use of the Internet now has a specific purpose: to find information. Unfortunately, the amount of data available on the Internet is growing exponentially, creating what can be considered a nearly infinite and ever-evolving network with no discernable structure. This rapid growth has raised the question of how to find the most relevant information. Many different techniques have been introduced to address the information overload, including search engines, Semantic Web, and recommender systems, among others. Recommender systems are computer-based techniques that are used to reduce information overload and recommend products likely to interest a user when given some information about the user’s profile. This technique is mainly used in e-Commerce to suggest items that fit a customer’s purchasing tendencies. The use of recommender systems for e-Government is a research topic that is intended to improve the interaction among public administrations, citizens, and the private sector through reducing information overload on e-Government services. More specifically, e-Democracy aims to increase citizens’ participation in democratic processes through the use of information and communication technologies. In this chapter, an architecture of a recommender system that uses fuzzy clustering methods for e-Elections is introduced. In addition, a comparison with the smartvote system, a Web-based Voting Assistance Application (VAA) used to aid voters in finding the party or candidate that is most in line with their preferences, is presented.

Comparative Shopping on Agent Web Sites

2011 ◽  
pp. 124-128
Author(s):  
Ming Wang
Keyword(s):  
Web Sites ◽  
Credit Card ◽  
Information Overload ◽  
Relevant Information ◽  
Point Of View ◽  
The Internet ◽  
Purchase Order ◽  
Customer Reviews ◽  
Online Shoppers ◽  
The Individual

The enormous amount of commercial information available on the Internet makes online shoppers overwhelmed and it difficult to find relevant information. The recent development of shopping agents (bots) has offered a practical solution for this information overload problem. From the customer’s point of view, a shopping agent reduces search complexity, increases search efficiency, and supports user mobility. It has been proposed that the availability of agent Web sites is one of the reasons why e-markets should be more efficient (Mougayar, 1998). Shopping bots are created with agent software that assists online shoppers by automatically gathering shopping information from the Internet. In this comparative shopping environment, shopping agents can provide the customer with comparative prices for a searched product, customer reviews of the product, and reviews of the corresponding merchants. The agent will first locate the merchants’ Web sites selling the searched product. Then, the agent will collect information about the prices of the product and its features from these merchants. Once a customer selects a product with a merchant, the individual merchant Web site will process the purchase order and the delivery details. The shopping agent receives a commission on each sale made by a visitor to its site from the merchant selling the product on the Internet. Some auction agent Web sites provide a negotiation service through intelligent agent functions. Agents will represent both buyers and sellers. Once a buyer identifies a seller, the agent can negotiate the transaction. The agents will negotiate a price and then execute the transaction for their respective owners. The buyer’s agent will use a credit card account number to pay for the product. The seller’s agent will accept the payment and transmit the proper instructions to deliver the item under the terms agreed upon by the agent.

scholarly journals User-Query Processing through Dynamic Tweets Status Recommender System

2021 ◽  
Vol 58 (1) ◽  
pp. 5600-5606
Author(s):  
V. Kakulapati, D. Vasumathi, G. Suryanarayana
Keyword(s):  
Recommender Systems ◽  
Recommender System ◽  
User Interfaces ◽  
Online Social Networks ◽  
Information Overload ◽  
Relevant Information ◽  
Twitter User ◽  
User Query ◽  
Text Filtering ◽  
Twitter Users

With increasing user information volume in online social networks, recommender systems have been an effective method to limit such information overload. The requirements of recommender systems specified, with widespread adoption in many internet social Twitter, Facebook, and Google online applications. In recent years,  the  micro-blogging  in  Twitter  has  brought  greater  importance  to  online  users  as  a  channel  spreading knowledge  and  information.  Through  Twitter,  users  can  find  the  relevant  information  on  the  search  they perform,  but  understanding  the  past,  present,  and  future  information  relevant  to  the  investigation  source  is needed real-time information. Estimating the successful tweet status (history, ongoing, and prospective) among the huge population of Twitter members is important to satisfy the needs of Twitter online content readers. In this paper, a Dynamic Tweets Status Recommender System (DTSRS) is designed by creating a set of dynamic recommendations to a Twitter user based on usability, consisting of people who post tweets, which is exciting present and future. The proposed recommender system is implemented through two approaches: the first is to analyze  the  Twitter  member  online  tweets,  select  and  understand  the  content  of  that  tweet,  and  the  second predicts  the  understanding  of  the  tweet  content,  suggest  the  dynamic  status  of  the  tweets.  In  this  paper,  the Twitter user tweets' views are expressed after examining the depth of content, different types of user interfaces, text filtering, and machine learning technique. The set of results through tweets experimentations with database operators carried out to evaluate and comparability the proposed recommender system's performance.  

scholarly journals Do Your Neighbors Matter to You? : Focused on Chinese Jiu Ling Hou (Post-’90s) Generation Online Shoppers

2019 ◽  
pp. 118-126
Author(s):  
Hao Weng ◽  
Yiqi Hu ◽  
Zhen Li ◽  
Yichu Sun ◽  
Min Chung Han
Keyword(s):  
Consumer Behavior ◽  
Perceived Risk ◽  
Purchase Decisions ◽  
Behavior Intention ◽  
Purchasing Intention ◽  
Online Retailers ◽  
Online Shoppers ◽  
Chinese Consumer ◽  
Perceived Enjoyment ◽  
The Relationship

The post-’90s generation is made up of those born between 1990 and 1999 in China; it is also the generation that is driving e-commerce in China. To attract these post-’90s consumers, online retailers have adopted recommender systems based on previous purchases and personal preferences. However, current Chinese online retailers do not typically consider the purchasing histories of their neighbors, although those neighbors have been proven to influence consumer behavior intention in several fields of study. Thus, this study investigates neighbors’ influences on Chinese consumer behavior in online shopping. In particular, this study examines the relationship between neighbors’ purchase histories and consumers’ purchase decisions among Chinese post-’90s consumers. Furthermore, this research seeks to determine whether neighbors’ purchasing history has an influence on consumer perceptions (e.g., perceived enjoyment, perceived risk) and whether perceived enjoyment and perceived risk have influences on purchasing intention.

scholarly journals Recommender systems: Are users taking the recommendations seriously?

2021 ◽  
Vol 6 (1) ◽  
Author(s):  
DEEPAK R. ◽  
Aishwarya Korishettar
Keyword(s):  
Recommender Systems ◽  
Information Overload ◽  
Recommendation Systems ◽  
Demographic Characteristics ◽  
Purchase Decisions ◽  
Product Lines ◽  
Automated Process ◽  
Structured Questionnaire

In recent years, we have observed substantial choices provided to consumers due to the rise of e-commerce. The products and the product-related suggestions provided by the various recommender systems have been found to be noteworthy given the enormous data available and information overload. The customers are not worried about the complexity of these algorithms but want the automated process to recommend product-related items of their choices. The study is an attempt to understand whether customers make purchase decisions online using the suggestions recommended across various product lines. For the study, responses of the customers were collected using a structured questionnaire and results were analysed to examine if respondents of different demographic characteristics make purchase related decisions based on the suggestions by recommendation systems.

Impact of Online Review Grouping on Consumers' System Usage Behavior

2016 ◽  
Vol 24 (4) ◽  
pp. 45-66 ◽  
Author(s):  
Jia Li ◽  
Xinmiao Li ◽  
David C. Yen ◽  
Pengzhu Zhang
Keyword(s):  
Information Overload ◽  
Decision Aids ◽  
Information Source ◽  
Online Reviews ◽  
Perceived Usefulness ◽  
Intention To Use ◽  
Purchase Decisions ◽  
Customer Reviews ◽  
System Usage ◽  
Online Shoppers

Information overload is one of the major challenges for online shoppers. One possible solution to this aforementioned problem is to take advantage of the interactive decision aids (IDAs). Prior studies on IDAs have mainly focused on information overload problems caused by the products (e.g., recommendation agents) and hence, have overlooked the information overload problems related to online reviews. However, online reviews are becoming more popular and turning into a major information source in consumer purchase decisions. To bridge this gap, this study investigates the effect of message grouping, an IDA approach supporting review browsing, onto the consumer's information processing and system usage intention. An experiment with a one-factor, three-level design was conducted to test the proposed research model. It is noted that grouping customer reviews into task-related categories significantly increases users' perceived usefulness, system satisfaction and intention to use the system next time. However, grouping customer reviews into task-distracting categories may significantly decrease users' perceived usefulness, system satisfaction and intention to use the system.

Enhancing Citizens' Participation via Recommender Systems

2015 ◽  
pp. 99-123
Author(s):  
Luis Terán
Keyword(s):  
Decision Making ◽  
Semantic Web ◽  
Private Sector ◽  
Web 2.0 ◽  
Recommender Systems ◽  
Search Engines ◽  
Information Overload ◽  
Relevant Information ◽  
Research Topic ◽  
Government Services

With the introduction of Web 2.0, which includes users as content generators, finding relevant information is even more complex. To tackle this problem of information overload, a number of different techniques have been introduced, including search engines, Semantic Web, and recommender systems, among others. The use of recommender systems for e-Government is a research topic that is intended to improve the interaction among public administrations, citizens, and the private sector through reducing information overload on e-Government services. In this chapter, the use of recommender systems on eParticipation is presented. A brief description of the eGovernment Framework used and the participation levels that are proposed to enhance participation. The highest level of participation is known as eEmpowerment, where the decision-making is placed on the side of citizens. Finally, a set of examples for the different eParticipation types is presented to illustrate the use of recommender systems.

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