An Integrative Framework on the Psychological Variables Explaining the Consumers’ Use of E-Commerce-Based Recommendation Systems

2010 ◽  
pp. 350-364 ◽  
Author(s):  
Francisco J. Martínez-López ◽  
Juan Carlos Gázquez-Abad ◽  
Inma Rodríguez-Ardura ◽  
Claudia C. Cabal-Cruz
Keyword(s):  
Conceptual Model ◽  
Recommendation System ◽  
Recommendation Systems ◽  
Research Problem ◽  
Psychological Process ◽  
Psychological Perspective ◽  
Psychological Variables ◽  
Integrative Framework ◽  
Computer Mediated ◽  
Research Propositions

A conceptual model to understand the consumer’s adoption and use of certain website recommendation system is presented; the research problem is tackled from a psychological perspective. The authors base on, adapt and integrate classical theories of consumer behavior with particular theories developed in the framework of computer-mediated environments. The model proposed, along with the relations analyzed among the variables considered (a total of 16 research propositions), should be of help for recommendation systems designer and website managers, in order to work with systems more aware of the psychological process experienced by consumers when interacting with them.

scholarly journals Fashion Recommendation Systems, Models and Methods: A Review

Informatics ◽  
2021 ◽  
Vol 8 (3) ◽  
pp. 49
Author(s):  
Samit Chakraborty ◽  
Md. Saiful Hoque ◽  
Naimur Rahman Jeem ◽  
Manik Chandra Biswas ◽  
Deepayan Bardhan ◽  
...  
Keyword(s):  
Recommendation System ◽  
Recommendation Systems ◽  
Fast Fashion ◽  
Potential Models ◽  
Scholarly Article ◽  
Enormous Amount ◽  
Shopping Experience ◽  
Tremendous Amount ◽  
Systems Models ◽  
Filtering Techniques

In recent years, the textile and fashion industries have witnessed an enormous amount of growth in fast fashion. On e-commerce platforms, where numerous choices are available, an efficient recommendation system is required to sort, order, and efficiently convey relevant product content or information to users. Image-based fashion recommendation systems (FRSs) have attracted a huge amount of attention from fast fashion retailers as they provide a personalized shopping experience to consumers. With the technological advancements, this branch of artificial intelligence exhibits a tremendous amount of potential in image processing, parsing, classification, and segmentation. Despite its huge potential, the number of academic articles on this topic is limited. The available studies do not provide a rigorous review of fashion recommendation systems and the corresponding filtering techniques. To the best of the authors’ knowledge, this is the first scholarly article to review the state-of-the-art fashion recommendation systems and the corresponding filtering techniques. In addition, this review also explores various potential models that could be implemented to develop fashion recommendation systems in the future. This paper will help researchers, academics, and practitioners who are interested in machine learning, computer vision, and fashion retailing to understand the characteristics of the different fashion recommendation systems.

User opinions driven social recommendation system

2021 ◽  
Vol 25 (1) ◽  
pp. 21-31
Author(s):  
Lakshmikanth Paleti ◽  
P. Radha Krishna ◽  
J.V.R. Murthy
Keyword(s):  
Social Networks ◽  
Collaborative Filtering ◽  
Recommendation System ◽  
Recommendation Systems ◽  
Social Recommendation ◽  
Novel Approach ◽  
Tripartite Graphs ◽  
Content Based Filtering

Recommendation systems provide reliable and relevant recommendations to users and also enable users’ trust on the website. This is achieved by the opinions derived from reviews, feedbacks and preferences provided by the users when the product is purchased or viewed through social networks. This integrates interactions of social networks with recommendation systems which results in the behavior of users and user’s friends. The techniques used so far for recommendation systems are traditional, based on collaborative filtering and content based filtering. This paper provides a novel approach called User-Opinion-Rating (UOR) for building recommendation systems by taking user generated opinions over social networks as a dimension. Two tripartite graphs namely User-Item-Rating and User-Item-Opinion are constructed based on users’ opinion on items along with their ratings. Proposed approach quantifies the opinions of users and results obtained reveal the feasibility.

FLAGS AS A NEW ASSESSMENT OF MENTAL HEALTH STATUS

2003 ◽  
Vol 31 (6) ◽  
pp. 593-602
Author(s):  
Jawad A. Fatayer
Keyword(s):  
Mental Health ◽  
Health Status ◽  
Clinical Experience ◽  
Mental Health Status ◽  
Arab Countries ◽  
Psychological Perspective ◽  
Social Psychological ◽  
Psychological Variables ◽  
New Instrument ◽  
Social Psychological Perspective

Mental health is examined from a social-psychological perspective based on years of clinical experience in the USAand some Arab countries. Three hundred and fifty-two participated to examine the validity and the reliability of this new instrument. Eleven social-psychological variables, integrated with five central emotions, make up the FLAGS assessment, which proved to have strength and dependability.

A qualitative investigation of smaller firm e‐business development

2004 ◽  
Vol 11 (3) ◽  
pp. 349-361 ◽  
Author(s):  
Ian Fillis ◽  
Ulf Johansson ◽  
Beverly Wagner
Keyword(s):  
Conceptual Model ◽  
Decision Maker ◽  
Entrepreneurial Orientation ◽  
Business Development ◽  
Future Research ◽  
Qualitative Investigation ◽  
Research Propositions ◽  
Depth Interviews

A previous paper by the authors drew on existing research on e‐business and the smaller firm, developed a conceptual model and a set of research propositions. This paper analyses a series of qualitative, in‐depth interviews of owner/managers of smaller firms in central Scotland in order to test the research propositions. Results indicate that industry and sectoral factors play an important role in the level of e‐business development achieved. In many cases the customer determines the need for e‐business adoption, rather than any internally planned programme of adoption. Other important factors include the degree of entrepreneurial orientation of the key decision maker and the ability to exploit appropriate competencies. Recommendations for encouragement of e‐business development are made and suggestions for future research are included.

scholarly journals Applying uncertainty reduction strategy for improving performance of questionnaire technique of solving cold user problem

2021 ◽  
Vol 13 (2) ◽  
pp. 47-53
Author(s):  
M. Abubakar ◽  
K. Umar
Keyword(s):  
Collaborative Filtering ◽  
Recommendation System ◽  
Research Work ◽  
Recommendation Systems ◽  
Cold Start ◽  
New Product ◽  
Uncertainty Reduction ◽  
Experimental Result ◽  
Reduction Strategy ◽  
Product Recommendation

Product recommendation systems are information filtering systems that uses ratings and predictions to make new product suggestions. There are many product recommendation system techniques in existence, these include collaborative filtering, content based filtering, knowledge based filtering, utility based filtering and demographic based filtering. Collaborative filtering techniques is known to be the most popular product recommendation system technique. It utilizes user’s previous product ratings to make new product suggestions. However collaborative filtering have some weaknesses, which include cold start, grey sheep issue, synonyms issue. However the major weakness of collaborative filtering approaches is cold user problem. Cold user problem is the failure of product recommendation systems to make product suggestions for new users. Literature investigation had shown that cold user problem could be effectively addressed using active learning technique of administering personalized questionnaire. Unfortunately, the result of personalized questionnaire technique could contain some user preference uncertainties where the product database is too large (as in Amazon). This research work addresses the weakness of personalized questionnaire technique by applying uncertainty reduction strategy to improve the result obtained from administering personalized questionnaire. In our experimental design we perform four different experiments; Personalized questionnaire approach of solving user based coldstart was implemented using Movielens dataset of 1M size, Personalized questionnaire approach of solving user based cold start was implemented using Movielens dataset of 10M size, Personalized questionnaire with uncertainty reduction was implemented using Movielens dataset of 1M size, and also Personalized  questionnaire with uncertainty reduction was implemented using Movielens dataset of 10M size. The experimental result shows RMSE, Precision and Recall improvement of 0.21, 0.17 and 0.18 respectively in 1M dataset and 0.17, 0.14 and 0.20 in 10M dataset respectively over personalized questionnaire.

scholarly journals Disbelief, Disengagement, Discontinuance, and Disaffiliation An Integrative Framework for the Study of Religious Deidentification

2021 ◽  
Author(s):  
Daryl R. Van Tongeren ◽  
C. Nathan DeWall
Keyword(s):  
Social Identity ◽  
Conceptual Model ◽  
Future Research ◽  
Religious Change ◽  
Unified Theory ◽  
Four Dimensions ◽  
Integrative Framework ◽  
Spiritual Struggles

Religion offers a powerful social identity. However, religious change is common across the lifespan, including some who leave religion altogether. Research on leaving religion has mostly been fragmented and failed to embrace a unified theory that captures the breadth of the extant research and appreciates the crucial nuances revealed by empirical findings. Toward that end, we adopt Saroglou’s (2011) classification of four dimensions of religiousness to introduce a conceptual model to explain the varieties of different research strands that have examined those leaving religion and integrate them into a unified theory of religious change. We explain how religious deidentification describes how one no longer identifies as religious, and this process can occur via disbelief, disengagement, discontinuance, and disaffiliation. We contend that religious and spiritual struggles are potential pathways by which deidentification may occur. Finally, we situate the work on religious deidentification among other related constructs and provide a brief framework for future research on the nonreligious experience.

scholarly journals Movie Recommenadation System

2021 ◽  
Vol 9 (4) ◽  
pp. 13-16
Author(s):  
Gandhali Malve ◽  
Lajree Lohar ◽  
Tanay Malviya ◽  
Shirish Sabnis
Keyword(s):  
Machine Learning ◽  
Recommender System ◽  
Recommendation System ◽  
Learning Algorithms ◽  
Recommendation Systems ◽  
Machine Learning Algorithms ◽  
The Internet ◽  
Amount Of Information ◽  
System Recommendation ◽  
User Ratings

Today the amount of information in the internet growth very rapidly and people need some instruments to find and access appropriate information. One of such tools is called recommendation system. Recommendation systems help to navigate quickly and receive necessary information. Many of us find it difficult to decide which movie to watch and so we decided to make a recommender system for us to better judge which movie we are more likely to love. In this project we are going to use Machine Learning Algorithms to recommend movies to users based on genres and user ratings. Recommendation system attempt to predict the preference or rating that a user would give to an item.

scholarly journals Concept-based Recommendation System for Finding Serendipity

2021 ◽  
Vol 12 (3) ◽  
pp. 2184-2190
Author(s):  
Kodai Tsukahara Et.al
Keyword(s):  
Information Retrieval ◽  
Recommendation System ◽  
Recommendation Systems ◽  
Research Purpose ◽  
Current Information ◽  
New Information ◽  
Similar Information ◽  
Information Recommendation ◽  
Filter Bubble ◽  
Output Information

Current information recommendation systems obtain users’ preferences from Web browsing histories and activities such as purchase of products, and efficiently provide the users with their preferable information. In such a case, however, the same or similar information is always recommended, which is called filter bubble and it decreases the users’ satisfaction to the systems. If information recommendation systems could provide users with something surprising and useful as output information, the user’s satisfaction to the systems would drastically increase. Therefore, “serendipity” is paid attention to in this research. In this paper, a new information recommendation system using a concept-based information retrieval is proposed to provide the users with serendipitous information. In this system, concepts which describe features or roles of items are input instead of the items themselves, and information which can meet the concepts are output as candidates of serendipitous information. The serendipitous information is extracted from the output information using the criteria which are the indexes of serendipity defined in this research. Through the evaluation experiment, it is revealed that the proposed system achieves the accuracy of 70% for the serendipitous information determination and the accuracy of 100% for the information retrieval, which are satisfactory for this research purpose.

scholarly journals A New Weighted-learning Approach for Exploiting Data Sparsity in Tag-based Item Recommendation Systems

2021 ◽  
Vol 14 (1) ◽  
pp. 387-399
Author(s):  
Noor Ifada ◽  
◽  
Richi Nayak ◽  
Keyword(s):  
Recommendation System ◽  
Recommendation Systems ◽  
Learning Approach ◽  
Tensor Model ◽  
Learning Approaches ◽  
Data Sparsity ◽  
Tensor Models ◽  
Sparsity Problem ◽  
Weighted Rank

The tag-based recommendation systems that are built based on tensor models commonly suffer from the data sparsity problem. In recent years, various weighted-learning approaches have been proposed to tackle such a problem. The approaches can be categorized by how a weighting scheme is used for exploiting the data sparsity – like employing it to construct a weighted tensor used for weighing the tensor model during the learning process. In this paper, we propose a new weighted-learning approach for exploiting data sparsity in tag-based item recommendation system. We introduce a technique to represent the users’ tag preferences for leveraging the weighted-learning approach. The key idea of the proposed technique comes from the fact that users use different choices of tags to annotate the same item while the same tag may be used to annotate various items in tag-based systems. This points out that users’ tag usage likeliness is different and therefore their tag preferences are also different. We then present three novel weighting schemes that are varied in manners by how the ordinal weighting values are used for labelling the users’ tag preferences. As a result, three weighted tensors are generated based on each scheme. To implement the proposed schemes for generating item recommendations, we develop a novel weighted-learning method called as WRank (Weighted Rank). Our experiments show that considering the users' tag preferences in the tensor-based weightinglearning approach can solve the data sparsity problem as well as improve the quality of recommendation.

Personalized Content Recommendation Engine for Web Publishing Services Using Textmining and Predictive Analytics

2017 ◽  
pp. 113-124
Author(s):  
Başar Öztayşi ◽  
Ahmet Tezcan Tekin ◽  
Cansu Özdikicioğlu ◽  
Kerim Caner Tümkaya
Keyword(s):  
Recommendation System ◽  
Predictive Analytics ◽  
Recommendation Systems ◽  
Personalized Recommendation ◽  
Web Contents ◽  
Interest Level ◽  
History Of ◽  
Content Recommendation ◽  
Publishing Services ◽  
Web Publishing

Recommendation systems have become very important especially for internet based business such as e-commerce and web publishing. While content based filtering and collaborative filtering are most commonly used groups in recommendation systems there are still researches for new approaches. In this study, a personalized recommendation system based on text mining and predictive analytics is proposed for a real world web publishing company. The approach given in this chapter first preprocesses existing web contents, integrate the structured data with history of a specific user and create an extended TDM for the user. Then this data is used for prediction of the users interest in new content. In order to reach that point, SVM, K-NN and Naïve Bayesian methods are used. Finally, the best performing method is used for determining the interest level of the user in a new content. Based on the forecasted interest levels the system recommends among the alternatives.

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