Study and Classification of Recommender Systems: A Survey

2020 ◽  
pp. 1153-1163
Author(s):  
Mugdha Sharma ◽  
Laxmi Ahuja ◽  
Vinay Kumar
Keyword(s):  
Recommender Systems

scholarly journals Eye-Tracking-Based Classification of Information Search Behavior Using Machine Learning: Evidence from Experiments in Physical Shops and Virtual Reality Shopping Environments

2020 ◽  
Vol 31 (3) ◽  
pp. 675-691 ◽  
Author(s):  
Jella Pfeiffer ◽  
Thies Pfeiffer ◽  
Martin Meißner ◽  
Elisa Weiß
Keyword(s):  
Virtual Reality ◽  
Recommender Systems ◽  
Eye Movement ◽  
Information Search ◽  
Search Behavior ◽  
Movement Data ◽  
Conversion Rates ◽  
Shopping Environments ◽  
Information Search Behavior

How can we tailor assistance systems, such as recommender systems or decision support systems, to consumers’ individual shopping motives? How can companies unobtrusively identify shopping motives without explicit user input? We demonstrate that eye movement data allow building reliable prediction models for identifying goal-directed and exploratory shopping motives. Our approach is validated in a real supermarket and in an immersive virtual reality supermarket. Several managerial implications of using gaze-based classification of information search behavior are discussed: First, the advent of virtual shopping environments makes using our approach straightforward as eye movement data are readily available in next-generation virtual reality devices. Virtual environments can be adapted to individual needs once shopping motives are identified and can be used to generate more emotionally engaging customer experiences. Second, identifying exploratory behavior offers opportunities for marketers to adapt marketing communication and interaction processes. Personalizing the shopping experience and profiling customers’ needs based on eye movement data promises to further increase conversion rates and customer satisfaction. Third, eye movement-based recommender systems do not need to interrupt consumers and thus do not take away attention from the purchase process. Finally, our paper outlines the technological basis of our approach and discusses the practical relevance of individual predictors.

Analysis and Classification of Multi-Criteria Recommender Systems

2007 ◽  
Vol 10 (4) ◽  
pp. 415-441 ◽  
Author(s):  
Nikos Manouselis ◽  
Constantina Costopoulou
Keyword(s):  
Recommender Systems

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 Recommender in AI-enhanced Learning: An Assessment from the Perspective of Instructional Design

2020 ◽  
Vol 2 (1) ◽  
pp. 101-111
Author(s):  
Michael Kerres ◽  
Katja Buntins
Keyword(s):  
Instructional Design ◽  
Recommender Systems ◽  
Learning Experience ◽  
Social Comparisons ◽  
Future Research ◽  
Learning Paths ◽  
Context Of Learning ◽  
Individual Profiles ◽  
Enhanced Learning

AbstractAs tools for AI-enhanced human learning, recommender systems support learners in finding materials and sequencing learning paths. The paper explores how these recommenders improve the learning experience from a perspective of instructional design. It analyzes mechanisms underlying current recommender systems, and it derives concrete examples of how they operate: Recommenders are either expert-, criteria-, behavior-, or profile-based or rely on social comparisons. To verify this classification of five different mechanisms, we analyze a set of current publications on recommenders and find all the identified mechanisms with profile-based approaches as the most common. Social recommenders, though highly attractive in other sectors, reveal some drawbacks in the context of learning. In comparison, expert-based recommendations are easy to implement and often stand out as simple but effective ways for suggesting learning materials and learning paths to learners. They can be combined with other approaches based on social comparisons and individual profiles. The paper points out challenges in studying recommenders for learning and provides suggestions for future research.

RecSys Issues Ontology: A Knowledge Classification of Issues for Recommender Systems Researchers

2019 ◽  
Vol 22 (6) ◽  
pp. 1377-1418 ◽  
Author(s):  
Lawrence Bunnell ◽  
Kweku-Muata Osei-Bryson ◽  
Victoria Y. Yoon
Keyword(s):  
Recommender Systems ◽  
Knowledge Classification

A literature review and classification of recommender systems research

2012 ◽  
Vol 39 (11) ◽  
pp. 10059-10072 ◽  
Author(s):  
Deuk Hee Park ◽  
Hyea Kyeong Kim ◽  
Il Young Choi ◽  
Jae Kyeong Kim
Keyword(s):  
Literature Review ◽  
Recommender Systems ◽  
Systems Research

scholarly journals A LITERATURE REVIEW AND CLASSIFICATION OF BOOK RECOMMENDATION RESEARCH

2020 ◽  
Vol 5 (16) ◽  
pp. 15-34
Author(s):  
Feng Wang ◽  
Lingling Zhang ◽  
Xin Xu
Keyword(s):  
Recommender Systems ◽  
Research Community ◽  
Research Papers ◽  
Subject Field ◽  
Academic Literature ◽  
Time Period ◽  
Literature Reviews ◽  
The Subject

The act of reading has benefits for individuals and societies, which can be a long-term commitment. While the overload of books information and readers’ specific needs make book recommendation (BR) in demand, BR is receiving great attention from the research community with different perspectives. The increasing amount of research conducted with BR calls for a classification methodology regarding trends and distribution in this field. This paper presents a study of recommender systems in the domain of BR. The main goal of this work is to provide authors with insights on the trends of academic literature reviews in the proposed context and to present a comparison of different research approaches. The authors searched for up-to-date research papers related to recommender systems for BR within a time period of eighteen years, from 2000 to 2018. Starting from 2000, a significant amount of research related to the subject field of recommender systems was conducted, which led to the first ACM Conference on Recommender Systems. After the filtering process, 39 papers were finally selected from journals, conferences and theses in five different academic databases (i.e. IEEE, ACM, Science Direct, Springer and ProQuest). The general classification is presented in this work, in order to describe the recommendation approaches for BR. This work can be extended in the future to include novel methodologies and trends of recommender systems for BR or other fields.

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