A Recommendation System with the Use of Comprehensive Trend Indication Based on Weighted Complete Graph

2012 ◽  
Vol 16 (2) ◽  
pp. 266-272
Author(s):  
Takuya Sugimoto ◽  
◽  
Tetsuya Toyota ◽  
Hajime Nobuhara ◽  
Keyword(s):  
Graduate Students ◽  
Collaborative Filtering ◽  
Undergraduate Students ◽  
Complete Graph ◽  
Recommendation System ◽  
Product Information ◽  
Point Of View ◽  
Office Workers ◽  
Internet Shopping ◽  
Typical Method

Recently, Internet shopping has become widespread, websites of which are equipped with a recommendation system to help users easily find their target items from among vast product information. As a typical method to create recommendation information, collaborative filtering is used but it has a problem that recommendation results tend to be biased toward the same category. Since this study intends recommendation with a high discoverability from a large point of view of category, we define dissimilarity between products based on information on Browse Node ID held by some products in Amazon and use k-medoids to newly categorize the products. Moreover, we create a weighted complete graph with those categories as nodes and indicate the trend across different categories. The proposed system estimates and recommends a category strongly related to a category that is thought to be unknown to the user but the user will like based on information of the weighted complete graph. We evaluate the effectiveness of the proposed system through experiments with 9 undergraduate students, 12 graduate students, and 2 office workers as subjects and show that the proposed system is better in recommending unknown products to the user than existing recommendation systems.

scholarly journals Contexts in a paper recommendation system with collaborative filtering

2012 ◽  
Vol 13 (5) ◽  
pp. 56 ◽  
Author(s):  
Pinata Winoto ◽  
Tiffany Y. Tang ◽  
Gordon I. McCalla
Keyword(s):  
Graduate Students ◽  
Collaborative Filtering ◽  
Undergraduate Students ◽  
Information Seeking ◽  
Recommendation System ◽  
Contextual Information ◽  
Learning Goals ◽  
Context Aware ◽  
Learning Groups ◽  
Filtering Techniques

Making personalized paper recommendations to users in an educational domain is not a trivial task of simply matching users’ interests with a paper topic. Therefore, we proposed a context-aware multidimensional paper recommendation system that considers additional user and paper features. Earlier experiments on experienced graduate students demonstrated the significance of this approach using modified collaborative filtering techniques. However, two key issues remain: (1) How would the modified filtering perform when target users are inexperienced undergraduate students who have a different pedagogical background and contextual information-seeking goals, such as task- and course-related goals, from those of graduate students?; (2) Should we combine graduates and undergraduates in the same pool, or should we separate them? We conducted two studies aimed at addressing these issues and they showed that (1) the system can be effectively used for inexperienced learners; (2) recommendations are less effective for different learning groups (with different pedagogical features and learning goals) than they are for the same learning groups. Based on the results obtained from these studies, we suggest several context-aware filtering techniques for different learning scenarios.<br /><br />

scholarly journals The Development of E-Commerce Recommendation System Based on Collaborative Filtering

2012 ◽  
Vol 6-7 ◽  
pp. 636-640 ◽  
Author(s):  
Guo Fang Kuang
Keyword(s):  
Experimental Data ◽  
Collaborative Filtering ◽  
Recommendation System ◽  
Product Information ◽  
Data Sets ◽  
User Evaluation ◽  
User Interest ◽  
Target User ◽  
Purchase Process ◽  
Sales Staff

The recommendation system in the e-commerce is to provide customers with product information and recommendations to help customers decide what to buy goods and analog sales staff to recommend merchandise to complete the purchase process. Collaborative filtering process is based on known user evaluation to predict the target user interest in the target, and then recommended to the target user. This paper proposes the development of E-commerce recommendation system based on Collaborative filtering. Experimental data sets prove that the proposed algorithm is effective and reasonable.

scholarly journals Percepções de estudantes de matemática sobre pesquisa

2019 ◽  
Vol 14 (31) ◽  
pp. 260-274
Author(s):  
Zulma Elizabete de Freitas Madruga ◽  
Maria Elizabete Souza Couto
Keyword(s):  
Data Analysis ◽  
Graduate Students ◽  
Undergraduate Students ◽  
Public University ◽  
Point Of View ◽  
Scientific Methodology ◽  
Theoretical Studies ◽  
Is Research ◽  
Palabras Clave ◽  
Undergraduate Thesis

RESUMO Este artigo objetiva apresentar e analisar as percepções de estudantes de graduação em Matemática (Licenciatura e Bacharelado) em relação à pesquisa. Trata-se de uma pesquisa de abordagem qualitativa e que está em andamento. Os participantes são alunos de uma universidade pública do sul da Bahia, estes, responderam à pergunta “O que é pesquisa? ”, no início da disciplina Trabalho de Conclusão de Curso (TCC), quando se depararam pela primeira vez com a metodologia científica. Como método de análise dos dados, utilizou-se a Análise Textual Discursiva (ATD). Os resultados indicam que os alunos já apresentam percepções sobre o que é pesquisa, como se desenvolve, os elementos que constituem um projeto de pesquisa e a necessidade de estudos teóricos para aprofundar e avançar nos estudos. E parecem entender que por meio da pesquisa é possível captar e apreender uma nova compreensão sobre a realidade estudada. Por fim, os alunos do bacharelado e da licenciatura percebem a pesquisa como princípio formativo para ajudá-los a avançar nos seus conhecimentos. Palavras-chave: Pesquisa. Trabalho de Conclusão de Curso. Percepção de estudantes. ABSTRACT The following paper aims to present and analyze the perceptions of undergraduate students in Mathematics (Major and Bachelor) when it comes to research. This is a qualitative and ongoing study. The participants are students of a public university in the south of Bahia, who answered the question "What is research?" At the beginning of the Undergraduate Thesis Program (TCC), when they first had contact with scientific methodology. As a method of data analysis, the authors used the Discursive Textual Analysis (DTA). The results indicate that students already have perceptions about what research is, how to develop it, the elements that constitute a research project and the need for theoretical studies to deepen and advance in the studies. Moreover, they seem to understand that through research it is possible to have a new point of view regarding the reality studied. Finally, undergraduate and graduate students perceive research as a formative principle to help them enlarge their knowledge. Keywords: Research. Undergraduate Thesis. Perception of students. RESUMEN Este artículo objetiva presentar y analizar las percepciones de estudiantes de graduación en Matemáticas (Licenciatura y Bachillerato) en relación a la investigación. Se trata de una investigación de enfoque cualitativo y que está en marcha. Los participantes son alumnos de una universidad pública del sur de Bahia, éstos, respondieron la pregunta "¿Qué es investigación?", Al inicio de la disciplina Trabajo de Conclusión de Curso (TCC), cuando se encontraron por primera vez con la metodología científica. Como método de análisis de los datos, se utilizó el análisis textual discursivo (ATD). Los resultados indican que los alumnos ya presentan percepciones sobre lo que es investigación, como se desarrolla, los elementos que constituyen un proyecto de investigación y la necesidad de estudios teóricos para profundizar y avanzar en los estudios. Y parecen entender que por medio de la investigación es posible captar y aprehender una nueva comprensión sobre la realidad estudiada. Por fin, los alumnos del bachillerato y de la licenciatura perciben la investigación como principio formativo para ayudarles a avanzar en sus conocimientos. Palabras clave: Investigación. Proyecto final de curso. Percepción de estudiantes.  DOI: http://dx.doi.org/10.22169/revint.v14i31.1463

Advocating for Transgender and Non-Binary Affirmative Spaces in Graduate Education

2020 ◽  
Author(s):  
Douglas Knutson ◽  
Em Matsuno ◽  
Chloe Goldbach ◽  
Halleh Hashtpari ◽  
Nathan Grant Smith
Keyword(s):  
Graduate Students ◽  
Graduate Education ◽  
Undergraduate Students ◽  
Ecological Model ◽  
Well Being ◽  
Educational Success ◽  
Post Secondary Education ◽  
Identity Exploration ◽  
Post Secondary ◽  
Social Ecological

Nearly 50% of graduate students report experiencing emotional or psychological distress during their enrollment in graduate school. Levels of distress are particularly high for transgender and non-binary graduate students who experience daily discrimination and marginalization. Universities and colleges have yet to address and accommodate the needs and experiences of transgender and non-binary graduate students. Given the multitude of challenges these students may face, educational settings should not present additional barriers to educational success and well-being. In an effort to improve graduate education for transgender and non-binary students, we add to the existing scholarship on affirming work with transgender undergraduate students by addressing the unique concerns of graduate students. We utilize a social-ecological model to identify sources of discrimination in post-secondary education and to provide transgender- and non-binary-affirming recommendations at structural, interpersonal, and individual levels. For practitioners who wish to do personal work, we provide guidance for multicultural identity exploration. A table of recommendations and discussion of ways to implement our recommendations are provided.

Identifying Attack Models for Securing Cluster-Based Recommendation System

2020 ◽  
Vol 14 ◽  
Author(s):  
Amreen Ahmad ◽  
Tanvir Ahmad ◽  
Ishita Tripathi
Keyword(s):  
Collaborative Filtering ◽  
Recommender System ◽  
Recommendation System ◽  
Relevant Information ◽  
System Function ◽  
Recommendation Algorithm ◽  
Underlying Network ◽  
Entire Dataset ◽  
Attack Models ◽  
Wide Usage

: The immense growth of information has led to the wide usage of recommender systems for retrieving relevant information. One of the widely used methods for recommendation is collaborative filtering. However, such methods suffer from two problems, scalability and sparsity. In the proposed research, the two issues of collaborative filtering are addressed and a cluster-based recommender system is proposed. For the identification of potential clusters from the underlying network, Shapley value concept is used, which divides users into different clusters. After that, the recommendation algorithm is performed in every respective cluster. The proposed system recommends an item to a specific user based on the ratings of the item’s different attributes. Thus, it reduces the running time of the overall algorithm, since it avoids the overhead of computation involved when the algorithm is executed over the entire dataset. Besides, the security of the recommender system is one of the major concerns nowadays. Attackers can come in the form of ordinary users and introduce bias in the system to force the system function that is advantageous for them. In this paper, we identify different attack models that could hamper the security of the proposed cluster-based recommender system. The efficiency of the proposed research is validated by conducting experiments on student dataset.

scholarly journals Extrapolative Collaborative Filtering Recommendation System with Word2Vec for Purchased Product for SMEs

2021 ◽  
Vol 13 (13) ◽  
pp. 7156
Author(s):  
Kyoung Jun Lee ◽  
Yu Jeong Hwangbo ◽  
Baek Jeong ◽  
Ji Woong Yoo ◽  
Kyung Yang Park
Keyword(s):  
Collaborative Filtering ◽  
High Performance ◽  
Recommendation System ◽  
Small And Medium Enterprises ◽  
Personal Information ◽  
Business Environment ◽  
Share Data ◽  
Artificial Intelligence Technology ◽  
Medium Enterprises ◽  
Start Ups

Many small and medium enterprises (SMEs) want to introduce recommendation services to boost sales, but they need to have sufficient amounts of data to introduce these recommendation services. This study proposes an extrapolative collaborative filtering (ECF) system that does not directly share data among SMEs but improves recommendation performance for small and medium-sized companies that lack data through the extrapolation of data, which can provide a magical experience to users. Previously, recommendations were made utilizing only data generated by the merchant itself, so it was impossible to recommend goods to new users. However, our ECF system provides appropriate recommendations to new users as well as existing users based on privacy-preserved payment transaction data. To accomplish this, PP2Vec using Word2Vec was developed by utilizing purchase information only, excluding personal information from payment company data. We then compared the performances of single-merchant models and multi-merchant models. For the merchants with more data than SMEs, the performance of the single-merchant model was higher, while for the SME merchants with fewer data, the multi-merchant model’s performance was higher. The ECF System proposed in this study is more suitable for the real-world business environment because it does not directly share data among companies. Our study shows that AI (artificial intelligence) technology can contribute to the sustainability and viability of economic systems by providing high-performance recommendation capability, especially for small and medium-sized enterprises and start-ups.

scholarly journals User profile correlation-based similarity (UPCSim) algorithm in movie recommendation system

2021 ◽  
Vol 8 (1) ◽  
Author(s):  
Triyanna Widiyaningtyas ◽  
Indriana Hidayah ◽  
Teguh B. Adji
Keyword(s):  
Collaborative Filtering ◽  
Recommendation System ◽  
User Behavior ◽  
Correlation Coefficients ◽  
User Profile ◽  
Profile Data ◽  
Similarity Algorithm ◽  
Previous Algorithm ◽  
Movie Recommendation ◽  
Recommendation Accuracy

AbstractCollaborative filtering is one of the most widely used recommendation system approaches. One issue in collaborative filtering is how to use a similarity algorithm to increase the accuracy of the recommendation system. Most recently, a similarity algorithm that combines the user rating value and the user behavior value has been proposed. The user behavior value is obtained from the user score probability in assessing the genre data. The problem with the algorithm is it only considers genre data for capturing user behavior value. Therefore, this study proposes a new similarity algorithm – so-called User Profile Correlation-based Similarity (UPCSim) – that examines the genre data and the user profile data, namely age, gender, occupation, and location. All the user profile data are used to find the weights of the similarities of user rating value and user behavior value. The weights of both similarities are obtained by calculating the correlation coefficients between the user profile data and the user rating or behavior values. An experiment shows that the UPCSim algorithm outperforms the previous algorithm on recommendation accuracy, reducing MAE by 1.64% and RMSE by 1.4%.

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.

A checklist to help students analyze published articles in basic medical sciences.

1995 ◽  
Vol 268 (6) ◽  
pp. S21 ◽  
Author(s):  
P K Rangachari ◽  
S Mierson
Keyword(s):  
Graduate Students ◽  
Undergraduate Students ◽  
Biomedical Sciences ◽  
Medical Sciences ◽  
Student Responses ◽  
Life Long Learning ◽  
Research Article ◽  
Reading Textbooks ◽  
Scientific Life ◽  
Basic Medical

Because critical analysis of published information is an essential component of scientific life, it is important that students be trained in its practice. Undergraduate students who are more accustomed to reading textbooks and taking lecture notes find it difficult to appreciate primary publications. To help such students, we have developed a checklist that helps them analyze different components of a research article in basic biomedical sciences. Students used the checklist to analyze critically a published article. The students were assigned an article and asked to write a paper (maximum 2 pages of single-spaced type) assessing it. This assignment has been found useful to both undergraduate and graduate students in pharmacology and physiology. Student responses to a questionnaire were highly favorable; students thought the exercise provided them with some of the essential skills for life-long learning.

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