scholarly journals Plataforma de colaboración en la nube mediante filtros colaborativos en ambientes educativos [Platform for collaboration in the cloud using collaborative filtering in educational environments]

2014 ◽  
Author(s):  
Jhon Haide Cano Beltrán ◽  
Álvaro Javier Muñoz daza
Keyword(s):  
Collaborative Filtering ◽  
Recommendation System ◽  
Collective Intelligence ◽  
Collaborative Systems ◽  
Group Level ◽  
Collective Knowledge ◽  
Educational Environments ◽  
Palabras Clave ◽  
Collaborative Platform ◽  
La Red

Resumen La inteligencia colectiva ha permitido resolver más problemas a nivel de grupo que si lo hicieran de manera individual, esta afirmación conlleva a que los estudiantes y profesores dejan de ser actores pasivos para convertirse en actores activos, las técnicas de filtro colaborativo permiten inferir recomendaciones y estrategias para la generación de conocimiento colectivo. Este conocimiento colectivo tiene dos premisas, la primera es que el conocimiento colectivo se da entre dos o más personas y segundo que exista interacciones entre ellas, aunado a estas dos premisas la web provee un espacio fascinante que cumplen con las condiciones anteriores y además genera espacios de participación y colaboración, lo que posibilita la construcción de redes de tipo colaborativo, de esta manera se tiene la red como plataforma y el software como un servicio, un sistema de recomendación permite guiar al usuario en la obtención de información de acuerdo a las preferencias, de allí que se puedan obtener patrones y recomendaciones sobre un tema específico. Palabras Clave: Plataforma Colaborativa, Educación, Sistemas Colaborativos, Motor de Sugerencias, Ecosistemas Digitales.   Abstract Collective intelligence has solved more problems at group level if they did individually, this statement entails those students and teachers are no longer passive actors to become active participants, collaborative filtering techniques to infer recommendations and strategies for the generation of collective knowledge. This collective knowledge has two premises, the first is that the collective knowledge exists between two or more people and second that there is interaction between them, these two premises together with the web provides a fascinating space that meet the above conditions and generates spaces of participation and collaboration, enabling the construction of a collaborative network, so you have the network as platform and software as a service, a recommendation system can guide the user in obtaining information according to the preferences, from there you can get patterns and recommendations on a specific topic. Keywords: Collaborative Platform, Education, Collaborative Systems, Engine Tips, Digital Ecosystems.

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.

Song Recommendations Based on Artists with Cosine Similarity Algorithms and K-Nearest Neighbor

2020 ◽  
Vol 8 (4) ◽  
pp. 367
Author(s):  
Muhammad Arief Budiman ◽  
Gst. Ayu Vida Mastrika Giri
Keyword(s):  
Collaborative Filtering ◽  
Mobile Phones ◽  
Recommendation System ◽  
Nearest Neighbor ◽  
Cosine Similarity ◽  
Music Recommendation ◽  
K Nearest Neighbor ◽  
Filtering Method ◽  
K Nearest Neighbor Algorithm ◽  
Music Recommendation System

The development of the music industry is currently growing rapidly, millions of music works continue to be issued by various music artists. As for the technologies also follows these developments, examples are mobile phones applications that have music subscription services, namely Spotify, Joox, GrooveShark, and others. Application-based services are increasingly in demand by users for streaming music, free or paid. In this paper, a music recommendation system is proposed, which the system itself can recommend songs based on the similarity of the artist that the user likes or has heard. This research uses Collaborative Filtering method with Cosine Similarity and K-Nearest Neighbor algorithm. From this research, a system that can recommend songs based on artists who are related to one another is generated.

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