An adaptive framework for personalized recommendation algorithms

2014 ◽  
Author(s):  
Jianchang Tang ◽  
Xinhuai Tang
Keyword(s):  
Personalized Recommendation ◽  
Recommendation Algorithms ◽  
Adaptive Framework

scholarly journals Augmented Context-Based Conceptual User Modeling for Personalized Recommendation System in Online Social Networks

2020 ◽  
Vol 14 (3) ◽  
pp. 1-19
Author(s):  
Ammar Alnahhas ◽  
Bassel Alkhatib
Keyword(s):  
Social Networks ◽  
Online Social Networks ◽  
Recommendation System ◽  
Contextual Information ◽  
Detailed Comparison ◽  
Personalized Recommendation ◽  
User Models ◽  
Proposed Model ◽  
Recommendation Algorithms ◽  
Novel Method

As the data on the online social networks is getting larger, it is important to build personalized recommendation systems that recommend suitable content to users, there has been much research in this field that uses conceptual representations of text to match user models with best content. This article presents a novel method to build a user model that depends on conceptual representation of text by using ConceptNet concepts that exceed the named entities to include the common-sense meaning of words and phrases. The model includes the contextual information of concepts as well, the authors also show a novel method to exploit the semantic relations of the knowledge base to extend user models, the experiment shows that the proposed model and associated recommendation algorithms outperform all previous methods as a detailed comparison shows in this article.

scholarly journals A Triangular Personalized Recommendation Algorithm for Improving Diversity

2018 ◽  
Vol 2018 ◽  
pp. 1-11 ◽  
Author(s):  
Biao Cai ◽  
Xiaowang Yang ◽  
Yusheng Huang ◽  
Hongjun Li ◽  
Qiang Sang
Keyword(s):  
Collaborative Filtering ◽  
Information Overload ◽  
Personalized Recommendation ◽  
Recommendation Algorithm ◽  
Filtering Method ◽  
Online Databases ◽  
Recommendation Algorithms ◽  
Recommendation Accuracy ◽  
Third Dimension ◽  
Triangular Area

Recommendation systems are used when searching online databases. As such they are very important tools because they provide users with predictions of the outcomes of different potential choices and help users to avoid information overload. They can be used on e-commerce websites and have attracted considerable attention in the scientific community. To date, many personalized recommendation algorithms have aimed to improve recommendation accuracy from the perspective of vertex similarities, such as collaborative filtering and mass diffusion. However, diversity is also an important evaluation index in the recommendation algorithm. In order to study both the accuracy and diversity of a recommendation algorithm at the same time, this study introduced a “third dimension” to the commonly used user/product two-dimensional recommendation, and a recommendation algorithm is proposed that is based on a triangular area (TR algorithm). The proposed algorithm combines the Markov chain and collaborative filtering method to make recommendations for users by building a triangle model, making use of the triangulated area. Additionally, recommendation algorithms based on a triangulated area are parameter-free and are more suitable for applications in real environments. Furthermore, the experimental results showed that the TR algorithm had better performance on diversity and novelty for real datasets of MovieLens-100K and MovieLens-1M than did the other benchmark methods.

CAN DISSIMILAR USERS CONTRIBUTE TO ACCURACY AND DIVERSITY OF PERSONALIZED RECOMMENDATION?

2010 ◽  
Vol 21 (10) ◽  
pp. 1217-1227 ◽  
Author(s):  
WEI ZENG ◽  
MING-SHENG SHANG ◽  
QIAN-MING ZHANG ◽  
LINYUAN LÜ ◽  
TAO ZHOU
Keyword(s):  
Collaborative Filtering ◽  
Recommender System ◽  
High Accuracy ◽  
Personalized Recommendation ◽  
Recommendation Algorithm ◽  
Filtering Algorithm ◽  
Recommendation Algorithms ◽  
Collaborative Filtering Algorithm ◽  
High Diversity ◽  
Better Than

Recommender systems are becoming a popular and important set of personalization techniques that assist individual users with navigating through the rapidly growing amount of information. A good recommender system should be able to not only find out the objects preferred by users, but also help users in discovering their personalized tastes. The former corresponds to high accuracy of the recommendation, while the latter to high diversity. A big challenge is to design an algorithm that provides both highly accurate and diverse recommendation. Traditional recommendation algorithms only take into account the contributions of similar users, thus, they tend to recommend popular items for users ignoring the diversity of recommendations. In this paper, we propose a recommendation algorithm by considering both the effects of similar and dissimilar users under the framework of collaborative filtering. Extensive analyses on three datasets, namely MovieLens, Netflix and Amazon, show that our method performs much better than the standard collaborative filtering algorithm for both accuracy and diversity.

scholarly journals Personalized Movie Recommendation Method Based on Deep Learning

2021 ◽  
Vol 2021 ◽  
pp. 1-12
Author(s):  
Jingdong Liu ◽  
Won-Ho Choi ◽  
Jun Liu
Keyword(s):  
Deep Learning ◽  
Rapid Development ◽  
Research Field ◽  
Personalized Recommendation ◽  
Complex Structures ◽  
Selection Efficiency ◽  
Training Set ◽  
Deep Architecture ◽  
Recommendation Algorithms ◽  
Movie Recommendation

With the rapid development of network technology and entertainment creation, the types of movies have become more and more diverse, which makes users wonder how to choose the type of movies. In order to improve the selection efficiency, recommend Algorithm came into being. Deep learning is a research field that has received extensive attention from scholars in recent years. Due to the characteristics of its deep architecture, deep learning models can learn more complex structures. Therefore, deep learning algorithms in speech recognition, machine translation, image recognition, and other fields have achieved impressive results. This article mainly introduces the research of personalized movie recommendation methods based on deep learning and intends to provide ideas and directions for the research of personalized movie recommendation under deep learning. This paper proposes a research method of personalized movie recommendation methods based on deep learning, including an overview of personalized recommendation and collaborative filtering recommendation algorithms, which are used to conduct research experiments on personalized movie recommendation methods based on deep learning. The experimental results in this paper show that the accuracy of the training set of the Seq2Seq model based on the LSTM recurrent neural network reaches 96.27% and the accuracy of the test set reaches 95.89%, which can be better for personalized movie recommendation.

scholarly journals Design of Personalized Recommendation Algorithms based on Big Data Framework

2021 ◽  
Vol 2138 (1) ◽  
pp. 012025
Author(s):  
Fang Liu
Keyword(s):  
Big Data ◽  
Information Overload ◽  
Recommendation Systems ◽  
Recall Rate ◽  
Personalized Recommendation ◽  
Processing Technologies ◽  
Data Framework ◽  
Recommendation Algorithms ◽  
Data Environment ◽  
Better Than

Abstract The issue of information overload has become increasingly prominent since there are various kinds of data generated daily. A good recommendation systems can better deal with such problems. However, traditional recommendation systems for a single machine are suffering from the computing bottleneck in the environment of massive data. An individual recommendation algorithm is unable to gratify desiring users. To tackle this problem, we designed and implemented three kinds of recommendation algorithms based on big data framework in this paper. Besides, we improved the traditional recommendation algorithms leveraging the prevailing big data processing technologies. Finally, we evaluated the efficiency of the algorithm through recall rate, precision rate and coverage. Experiments show that the hybrid model-based recommendation algorithms which can be applied to the bulk data environment are better than the single recommendation algorithms.

Recommendation of sustainable economic learning course based on text vector model and support vector machine

2020 ◽  
pp. 1-11
Author(s):  
Xiangfei Ma
Keyword(s):  
Support Vector Machine ◽  
Recommendation System ◽  
Comprehensive Analysis ◽  
Information Space ◽  
Vector Model ◽  
Personalized Recommendation ◽  
Support Vector ◽  
Retrieval Accuracy ◽  
Recommendation Algorithms ◽  
Massive Information

The sustainable economic learning course recommendation can quickly find the knowledge information that the user really needs from the massive information space and realize the personalized recommendation to the user. However, the occurrence of trust attacks seriously affects the normal recommendation function of the recommendation system, resulting in its failure to provide users with reliable and reliable recommendation results. In order to solve the vulnerability of the recommendation system to the support attack, based on text vector model and support vector machine, this paper makes a comprehensive analysis of the current research status of the robust recommendation technology. Moreover, based on the idea of suspicious user metrics, this paper has conducts in-depth research on how to design highly robust recommendation algorithms, and constructs a highly reliable sustainable economic learning course recommendation model. In addition to this, this research tests the performance of the system from two perspectives of course recommendation satisfaction and system retrieval accuracy. The experiment proves that the model constructed in this paper performs well in the recommendation of sustainable economic learning courses.

scholarly journals Research of the Context Recommendation Algorithm Based on the Tripartite Graph Model in Complex Systems

Complexity ◽  
2020 ◽  
Vol 2020 ◽  
pp. 1-11
Author(s):  
Fei Long
Keyword(s):  
Complex Systems ◽  
Information Overload ◽  
Rapid Development ◽  
Three Dimensional ◽  
Graph Model ◽  
Personalized Recommendation ◽  
Three Dimensional Model ◽  
Recommendation Algorithm ◽  
Tripartite Graph ◽  
Recommendation Algorithms

With the rapid development of information technology, the information overload has become a very serious problem in web information environment. The personalized recommendation came into being. Current recommending algorithms, however, are facing a series of challenges. To solve the problem of the complex context, a new context recommendation algorithm based on the tripartite graph model is proposed for the three-dimensional model in complex systems. Improving the accuracy of the recommendation by the material diffusion, through the heat conduction to improve the diversity of the recommended objects, and balancing the accuracy and diversity through the integration of resources thus realize the personalized recommendation. The experimental results show that the proposed context recommendation algorithm based on the tripartite graph model is superior to other traditional recommendation algorithms in recommendation performance.

scholarly journals Evaluation of Recommendation System for Sustainable E-Commerce: Accuracy, Diversity and Customer Satisfaction

2020 ◽  
Author(s):  
Qinglong Li ◽  
Ilyoung Choi ◽  
Jaekyeong Kim
Keyword(s):  
Customer Satisfaction ◽  
Deep Neural Networks ◽  
Recommendation System ◽  
Personalized Recommendation ◽  
Sustainable Competitive Advantage ◽  
Customer Data ◽  
The Sustainable Development ◽  
Personalized Services ◽  
Recommendation Algorithms ◽  
Positive Effect

With the development of information technology and the popularization of mobile devices, collecting various types of customer data such as purchase history or behavior patterns became possible. As the customer data being accumulated, there is a growing demand for personalized recommendation services that provide customized services to customers. Currently, global e-commerce companies offer personalized recommendation services to gain a sustainable competitive advantage. However, previous research on recommendation systems has consistently raised the issue that the accuracy of recommendation algorithms does not necessarily lead to the satisfaction of recommended service users. It also claims that customers are highly satisfied when the recommendation system recommends diverse items to them. In this study, we want to identify the factors that determine customer satisfaction when using the recommendation system which provides personalized services. To this end, we developed a recommendation system based on Deep Neural Networks (DNN) and measured the accuracy of recommendation service, the diversity of recommended items and customer satisfaction with the recommendation service. The experimental results of is the study showed that both recommendation system accuracy and diversity would have a positive effect on customer satisfaction. These results can further improve customer satisfaction with the recommendation system and promote the sustainable development of e-commerce.

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