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.

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.

scholarly journals Recommendation Systems: Algorithms, Challenges, Metrics, and Business Opportunities

2020 ◽  
Vol 10 (21) ◽  
pp. 7748
Author(s):  
Zeshan Fayyaz ◽  
Mahsa Ebrahimian ◽  
Dina Nawara ◽  
Ahmed Ibrahim ◽  
Rasha Kashef
Keyword(s):  
Recommender Systems ◽  
Recommendation System ◽  
Information Overload ◽  
State Of The Art ◽  
Recommendation Systems ◽  
Qualitative Evaluation ◽  
Potential Influence ◽  
Systems Research ◽  
Current State

Recommender systems are widely used to provide users with recommendations based on their preferences. With the ever-growing volume of information online, recommender systems have been a useful tool to overcome information overload. The utilization of recommender systems cannot be overstated, given its potential influence to ameliorate many over-choice challenges. There are many types of recommendation systems with different methodologies and concepts. Various applications have adopted recommendation systems, including e-commerce, healthcare, transportation, agriculture, and media. This paper provides the current landscape of recommender systems research and identifies directions in the field in various applications. This article provides an overview of the current state of the art in recommendation systems, their types, challenges, limitations, and business adoptions. To assess the quality of a recommendation system, qualitative evaluation metrics are discussed in the paper.

Revisiting Recommendation Systems

2013 ◽  
pp. 129-151
Author(s):  
Sagarika Bakshi ◽  
Sweta Sarkar ◽  
Alok Kumar Jagadev ◽  
Satchidananda Dehuri
Keyword(s):  
Recommender Systems ◽  
Significant Role ◽  
Information Overload ◽  
Recommendation Systems ◽  
Hybrid Models ◽  
User Preference ◽  
Individual Approach ◽  
Insight Into

Recommender systems are applied in a multitude of spheres and have a significant role in reduction of information overload on those websites that have the features of voting. Therefore, it is an urgent need for them to adapt and respond to immediate changes in user preference. To overcome the shortcomings of each individual approach to design the recommender systems, a myriad of ways to coalesce different recommender systems are proposed by researchers. In this chapter, the authors have presented an insight into the design of recommender systems developed, namely content-based and collaborative recommendations, their evaluation, their lacunae, and some hybrid models to enhance the quality of prediction.

Recommender Systems

2013 ◽  
Vol 4 (4) ◽  
pp. 32-46 ◽  
Author(s):  
Nikolaos Polatidis ◽  
Christos K. Georgiadis
Keyword(s):  
Recommender Systems ◽  
Rapid Growth ◽  
Information Overload ◽  
Recommendation Systems ◽  
The Internet ◽  
Valuable Customer

Due to the rapid growth of the internet in conjunction with the information overload problem the use of recommender systems has started to become necessary for both e-businesses and customers. However there are other factors such as privacy and trust that make customers suspicious. This paper gives an overview of recommendation systems, the benefits that both the business and the customers have and an explanation of the challenges, which if faced can make the personalization process better for both parties. Moreover an outline of current studies is given along with an overview of Amazon's recommendations in order to clarify that the use of recommender systems is beneficial for an e-business in many ways and also for a valuable customer of such business.

scholarly journals Antecedents of the importance of price in purchase decisions

2011 ◽  
Vol 51 (4) ◽  
pp. 370-381 ◽  
Author(s):  
Isabel Maria Rosa Diaz ◽  
Francisco Javier Rondán Cataluña
Keyword(s):  
Random Sample ◽  
Significant Relationship ◽  
Brand Loyalty ◽  
Ordinal Regression ◽  
Demographic Characteristics ◽  
Future Research ◽  
Purchase Decisions ◽  
Purchase Frequency ◽  
Shopping Enjoyment

This paper analyses the commercial and socio-demographic antecedents of the importance of price in buyers' decisions. The study uses ordinal regression in order to analyze the data obtained from a random sample of consumers of frequently purchased products; these consumers were surveyed in different stores. The results demonstrate that shopping enjoyment and brand loyalty have an influence over the importance of price. However, responsibility for shopping (purchase frequency) does not show a significant relationship. Furthermore, some interesting socio-demographic characteristics were found in the context of the study that can be analyzed in future research.

Knowledge-Based Recommendation Systems

2014 ◽  
Vol 10 (2) ◽  
pp. 1-19 ◽  
Author(s):  
Sarah Bouraga ◽  
Ivan Jureta ◽  
Stéphane Faulkner ◽  
Caroline Herssens
Keyword(s):  
Knowledge Base ◽  
Recommender Systems ◽  
Input Data ◽  
Recommendation Systems ◽  
Knowledge Based ◽  
Classification Framework ◽  
Knowledge Content ◽  
Main Ideas

Knowledge-Base Recommendation (or Recommender) Systems (KBRS) provide the user with advice about a decision to make or an action to take. KBRS rely on knowledge provided by human experts, encoded in the system and applied to input data, in order to generate recommendations. This survey overviews the main ideas characterizing a KBRS. Using a classification framework, the survey overviews KBRS components, user problems for which recommendations are given, knowledge content of the system, and the degree of automation in producing recommendations.

Socio-Demographic Characteristics and Hysteroscopic Findings among Women with Unsafe Abortion in South-South Nigeria

2021 ◽  
Vol 6 (03) ◽  
pp. 174-177
Author(s):  
Ekpo Effiong Edet ◽  
Efiok Eyo Efiok ◽  
Amarachukwu Nnaemezie Njoku ◽  
Sylvester Etenikang Abeshi ◽  
Ezukwa Omoronyia Ezukwa ◽  
...  
Keyword(s):  
Cross Sectional Study ◽  
Demographic Characteristics ◽  
Reproductive Age ◽  
Unsafe Abortion ◽  
Cervical Stenosis ◽  
Intrauterine Adhesions ◽  
Cross Sectional ◽  
Sociodemographic Data ◽  
Significant Difference ◽  
Structured Questionnaire

Background: Abortion is unsafe when it is carried out either by a person lacking the necessary skills or in an environment that does not conform to minimal standard, or both. It is complicated by intrauterine adhesions and secondary infertility. Objectives: To determine the socio-demographic features and hysteroscopic findings of women that had unsafe abortion in Calabar, Nigeria. Methodology: It was a cross-sectional study of 66 women in reproductive age, presenting in gynaecological clinic who consented to hysteroscopy between August 2019 to September 2020. A structured questionnaire was used to obtain sociodemographic data and hysteroscopy was done. Results: There was no significant difference in socio-demographic characteristics between women that have undergone unsafe abortion and those that have not. Cervical stenosis (76.9%) (p = 0.005) and intrauterine adhesions (68.3%) (p = 0.005) were significantly higher in women who had unsafe abortion. The odd ratios of cervical stenosis and intrauterine adhesions for 1 and ≥2 unsafe abortions were not significant. Conclusion: Cervical stenosis and intrauterine adhesions were significant complications of unsafe abortion in our environment. Multiple unsafe abortions do not confer significant higher risk of cervical stenosis or intrauterine adhesions.

Recommender Systems Review of Types, Techniques, and Applications

2015 ◽  
pp. 7260-7270 ◽  
Author(s):  
George A. Sielis ◽  
Aimilia Tzanavari ◽  
George A. Papadopoulos
Keyword(s):  
Recommender Systems ◽  
Recommendation Systems ◽  
Software Tools ◽  
Future Research ◽  
Research Directions ◽  
Different Types ◽  
Future Research Directions

Recommender or recommendation systems are software tools that make useful suggestions to users, by taking into account their profile, preferences and/or actions during interaction with an application or website. They are usually personalized and can refer to items to buy, people to connect to or books/ articles to read. Recommender Systems (RS) aim at helping users with their interaction by bringing to surface the information that is relevant to them, their needs, or their tasks. This article's objective is to present a review of the different types of RS, the techniques and methods used for building such systems, the algorithms used to generate the recommendations and how these systems can be evaluated. Finally, a number of topics are discussed as envisioned future research directions.

Recommender Technologies and Emerging Applications

2019 ◽  
pp. 458-470
Author(s):  
Young Park
Keyword(s):  
Recommender Systems ◽  
Recommender System ◽  
Information Overload ◽  
Future Research ◽  
Research Topics ◽  
Emerging Trends ◽  
System Approaches ◽  
Age Of Information

This chapter presents a brief overview of the field of recommender technologies and their emerging application domains. The authors explain the current major recommender system approaches within a unifying model, discuss emerging applications of recommender systems beyond traditional e-commerce, and outline emerging trends and future research topics, along with additional readings in the area of recommender technologies and applications. They believe that personalized recommender technologies will continue to advance and be applied in a variety of traditional and emerging application domains to assist users in the age of information overload.

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.

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