Document recommendation based on the analysis of group trust and user weightings

2019 ◽  
Vol 45 (6) ◽  
pp. 845-862 ◽  
Author(s):  
Chin-Hui Lai ◽  
Yu-Chieh Chang
Keyword(s):  
Collaborative Filtering ◽  
Recommender Systems ◽  
Information Overload ◽  
Trust Model ◽  
Or Groups ◽  
Personal Trust ◽  
Knowledge Intensive ◽  
Group Trust ◽  
Support Work

Collaborative filtering (CF) has been applied in various domains to resolve problems related to information overload. In a knowledge-intensive environment, most works are processed through teamwork. A user on a team can reference task-related documents from other trusted members to support work on the task. However, the traditional personalised recommender systems no longer meet the demand of teams or groups. Therefore, this work proposes a novel document recommendation method based on a group-based trust model. Our method will analyse the degrees of trust among users in a group and then identify the trustworthy users. The proposed group trust consists of a hybrid personal trust (HPT) model and users’ importance (i.e. users’ activity, similarity and reputation) in a group. Group-based trust is then integrated with the user-based CF to recommend documents to users. The experiments demonstrate that the proposed method can provide better performance than other trust-based recommendation methods; it not only obtains reliable trust values to increase the accuracy of predictions but also enhances the recommendation quality.

An Effective Recommender System Based on Clustering Technique for TED Talks

2020 ◽  
Vol 15 (1) ◽  
pp. 35-51 ◽  
Author(s):  
Faiz Maazouzi ◽  
Hafed Zarzour ◽  
Yaser Jararweh
Keyword(s):  
Collaborative Filtering ◽  
Recommender Systems ◽  
Recommender System ◽  
Information Overload ◽  
State Of The Art ◽  
Pearson Correlation ◽  
Enormous Amount ◽  
Powerful Mechanism ◽  
Ted Talks

With the enormous amount of information circulating on the Web, it is becoming increasingly difficult to find the necessary and useful information quickly and efficiently. However, with the emergence of recommender systems in the 1990s, reducing information overload became easy. In the last few years, many recommender systems employ the collaborative filtering technology, which has been proven to be one of the most successful techniques in recommender systems. Nowadays, the latest generation of collaborative filtering methods still requires further improvements to make the recommendations more efficient and accurate. Therefore, the objective of this article is to propose a new effective recommender system for TED talks that first groups users according to their preferences, and then provides a powerful mechanism to improve the quality of recommendations for users. In this context, the authors used the Pearson Correlation Coefficient (PCC) method and TED talks to create the TED user-user matrix. Then, they used the k-means clustering method to group the same users in clusters and create a predictive model. Finally, they used this model to make relevant recommendations to other users. The experimental results on real dataset show that their approach significantly outperforms the state-of-the-art methods in terms of RMSE, precision, recall, and F1 scores.

scholarly journals Enhancing Recommender System with Collaborative Filtering and User Experiences Filtering

2021 ◽  
Vol 11 (24) ◽  
pp. 11890
Author(s):  
Silvana Vanesa Aciar ◽  
Ramón Fabregat ◽  
Teodor Jové ◽  
Gabriela Aciar
Keyword(s):  
Collaborative Filtering ◽  
Recommender Systems ◽  
Recommender System ◽  
Information Overload ◽  
User Experiences ◽  
Relevant Feedback ◽  
Filtering Method

Recommender systems have become an essential part in many applications and websites to address the information overload problem. For example, people read opinions about recommended products before buying them. This action is time-consuming due to the number of opinions available. It is necessary to provide recommender systems with methods that add information about the experiences of other users, along with the presentation of the recommended products. These methods should help users by filtering reviews and presenting the necessary answers to their questions about recommended products. The contribution of this work is the description of a recommender system that recommends products using a collaborative filtering method, and which adds only relevant feedback from other users about recommended products. A prototype of a hotel recommender system was implemented and validated with real users.

scholarly journals A genre trust model for defending shilling attacks in recommender systems

2021 ◽  
Author(s):  
Li Yang ◽  
Xinxin Niu
Keyword(s):  
Collaborative Filtering ◽  
Recommender Systems ◽  
Recommender System ◽  
Trust Model ◽  
Trust Value ◽  
Shilling Attack ◽  
Trust Degree ◽  
Different Types ◽  
And Behavior ◽  
The Impact

AbstractShilling attacks have been a significant vulnerability of collaborative filtering (CF) recommender systems, and trust in CF recommender algorithms has been proven to be helpful for improving the accuracy of system recommendations. As a few studies have been devoted to trust in this area, we explore the benefits of using trust to resist shilling attacks. Rather than simply using user-generated trust values, we propose the genre trust degree, which differ in terms of the genres of items and take both trust value and user credibility into consideration. This paper introduces different types of shilling attack methods in an attempt to study the impact of users’ trust values and behavior features on defending against shilling attacks. Meanwhile, it improves the approach used to calculate user similarities to form a recommendation model based on genre trust degrees. The performance of the genre trust-based recommender system is evaluated on the Ciao dataset. Experimental results demonstrated the superior and comparable genre trust degrees recommended for defending against different types of shilling attacks.

scholarly journals Improve the Performance of Advice Systems Based on Cooperative Liquidation Using Trust Relationships

2019 ◽  
Vol 27 (1) ◽  
pp. 87-106
Author(s):  
EEva Diab Hriekes ◽  
Yosser AlSayed Souleiman AlAtassi
Keyword(s):  
Social Context ◽  
Collaborative Filtering ◽  
Recommender Systems ◽  
Information Overload ◽  
User Preferences ◽  
User Interactions ◽  
Trust Relationships ◽  
Recommendation Accuracy ◽  
User Reputation ◽  
User Ratings

Recommender systems are one of the recent inventions to deal with information overload problem and provide users with personalized recommendations that may be of their interests. Collaborative filtering is the most popular and widely used technique to build recommender systems and has been successfully employed in many applications. However, collaborative filtering suffers from several inherent issues that affect the recommendation accuracy such as: data sparsity and cold start problems caused by the lack of user ratings, so the recommendation results are often unsatisfactory. To address these problems, we propose a recommendation method called “MFGLT” that enhance the recommendation accuracy of collaborative filtering method using trust-based social networks by leveraging different  user's situations (as a trustor and as a trustee) in these networks to model user preferences. Specifically, we propose model-based method that uses matrix factorization technique and exploit both local social context represented by modeling explicit user interactions and implicit user interactions with other users, and also the global social context represented by the user reputation in the whole social network for making recommendations. Experimental results based on real-world dataset demonstrate that our approach gives better performance than the other trust-aware recommendation approaches, in terms of prediction accuracy.  

scholarly journals Fusion of Internal Similarity to Improve the Accuracy of Recommendation Algorithm

2022 ◽  
Vol 24 (1) ◽  
pp. 139-140
Author(s):  
Dr.S. Dhanabal ◽  
◽  
Dr.K. Baskar ◽  
R. Premkumar ◽  
◽  
...  
Keyword(s):  
Collaborative Filtering ◽  
Recommender Systems ◽  
Information Overload ◽  
Real Life ◽  
Similarity Matrix ◽  
Recommendation Algorithm ◽  
Simulation Experiments ◽  
Average Similarity ◽  
Recommendation Accuracy ◽  
Item Item

Collaborative filtering algorithms (CF) and mass diffusion (MD) algorithms have been successfully applied to recommender systems for years and can solve the problem of information overload. However, both algorithms suffer from data sparsity, and both tend to recommend popular products, which have poor diversity and are not suitable for real life. In this paper, we propose a user internal similarity-based recommendation algorithm (UISRC). UISRC first calculates the item-item similarity matrix and calculates the average similarity between items purchased by each user as the user’s internal similarity. The internal similarity of users is combined to modify the recommendation score to make score predictions and suggestions. Simulation experiments on RYM and Last.FM datasets, the results show that UISRC can obtain better recommendation accuracy and a variety of recommendations than traditional CF and MD algorithms.

Hybrid Recommender System Using Emotional Fingerprints Model

2019 ◽  
Vol 9 (3) ◽  
pp. 48-70
Author(s):  
Anthony Nosshi ◽  
Aziza Saad Asem ◽  
Mohammed Badr Senousy
Keyword(s):  
Collaborative Filtering ◽  
Recommender Systems ◽  
Recommender System ◽  
Information Overload ◽  
Experimental Simulation ◽  
Key Factors ◽  
Easy Task ◽  
Needed Information ◽  
Hybrid Recommender System ◽  
Hybrid Recommender

With today's information overload, recommender systems are important to help users in finding needed information. In the movies domain, finding a good movie to watch is not an easy task. Emotions play an important role in deciding which movie to watch. People usually express their emotions in reviews or comments about the movies. In this article, an emotional fingerprint-based model (EFBM) for movies recommendation is proposed. The model is based on grouping movies by emotional patterns of some key factors changing in time and forming fingerprints or emotional tracks, which are the heart of the proposed recommender. Then, it is incorporated into collaborative filtering to detect the interest connected with topics. Experimental simulation is conducted to understand the behavior of the proposed approach. Results are represented to evaluate the proposed recommender.

scholarly journals A Novel Time-Aware Hybrid Recommendation Scheme Combining User Feedback and Collaborative Filtering

2020 ◽  
Vol 2020 ◽  
pp. 1-16
Author(s):  
Hongzhi Li ◽  
Dezhi Han
Keyword(s):  
Collaborative Filtering ◽  
Recommender Systems ◽  
Clustering Algorithm ◽  
Information Overload ◽  
User Feedback ◽  
User Preferences ◽  
User Preference ◽  
Logistic Regression Algorithm ◽  
Hybrid Recommendation ◽  
Time Aware

Nowadays, recommender systems are used widely in various fields to solve the problem of information overload. Collaborative filtering and content-based models are representative solutions in recommender systems; however, the content-based model has some shortcomings, such as single kind of recommendation results and lack of effective perception of user preferences, while for the collaborative filtering model, there is a cold start problem, and such a model is greatly affected by its adopted clustering algorithm. To address these issues, a hybrid recommendation scheme is proposed in this paper, which is based on both collaborative filtering and content-based. In this scheme, we propose the concept of time impact factor, and a time-aware user preference model is built based on it. Also, user feedback on recommendation items is utilized to improve the accuracy of our proposed recommendation model. Finally, the proposed hybrid model combines the results of content recommendation and collaborative filtering based on the logistic regression algorithm.

A Collaborative Filtering Recommendation Algorithm Based on Product Clustering

2012 ◽  
Vol 267 ◽  
pp. 87-90
Author(s):  
Pu Wang
Keyword(s):  
Electronic Commerce ◽  
Collaborative Filtering ◽  
Recommender Systems ◽  
Recommendation System ◽  
Information Overload ◽  
Personalized Recommendation ◽  
The Internet ◽  
Recommendation Algorithm ◽  
The Core ◽  
Intelligent Technology

E-commerce recommendation system is one of the most important and the most successful application field of information intelligent technology. Recommender systems help to overcome the problem of information overload on the Internet by providing personalized recommendations to the customers. Recommendation algorithm is the core of the recommendation system. Collaborative filtering recommendation algorithm is the personalized recommendation algorithm that is used widely in e-commerce recommendation system. Collaborative filtering has been a comprehensive approach in recommendation system. But data are always sparse. This becomes the bottleneck of collaborative filtering. Collaborative filtering is regarded as one of the most successful recommender systems within the last decade, which predicts unknown ratings by analyzing the known ratings. In this paper, an electronic commerce collaborative filtering recommendation algorithm based on product clustering is given. In this approach, the clustering of product is used to search the recommendation neighbors in the clustering centers.

scholarly journals Context-Aware Computational Trust Model for Recommender Systems

2020 ◽  
Vol 4 (6) ◽  
Author(s):  
Edwin O. Ngwawe ◽  
Elisha O. Abade ◽  
Stephen N. Mburu
Keyword(s):  
Recommender Systems ◽  
Customer Service ◽  
Information Overload ◽  
Trust Model ◽  
Recommendation Algorithm ◽  
Computational Trust ◽  
Work From Home ◽  
Networking Technologies ◽  
The Government ◽  
Potential Customers

With increase in computing and networking technologies, many organizations have managed to place their services online with the aim of achieving efficiency in customer service as well as reach more potential customers, also with communicable diseases such as COVID-19 and need for social distancing, many people are encouraged to work from home, including shopping. To meet this objective in areas with poor Internet connectivity, the government of Kenya recently announced partnership with Google Inc for use of Google Loon. This has come up with challenges which include information overload on the side of the end consumer as well as security loopholes such as dishonest vendors preying on unsuspecting consumers. Recommender systems have been used to alleviate these two challenges by helping online users select the best item for their case. However, most recommender systems, especially common filtering recommendation algorithm (CFRA) based systems still rely on presenting output based on selections of nearest neighbors (most similar users – birds of the same feathers flock together). This leaves room for manipulation of the output by mimicking the features of their target and then picking malicious item such that when the recommender system runs, it will output the same malicious item to the target – a trust issue. Data to construct trust is equally a challenge. In this research, we propose to address this issue by creating a trust adjustment factor (TAF) for recommender systems for online services.

NINU: An Incremental User-Based Algorithm for Data Sparsity Recommender Systems

2012 ◽  
Vol 201-202 ◽  
pp. 428-432
Author(s):  
Yang Zhang ◽  
Hua Shen ◽  
Guo Shun Zhou
Keyword(s):  
Collaborative Filtering ◽  
Recommender Systems ◽  
Web Sites ◽  
Rapid Growth ◽  
Information Overload ◽  
Pearson Correlation ◽  
Sparse Data ◽  
Data Set ◽  
Online Prediction

Collaborative Filtering (CF) algorithms are widely used in recommender systems to deal with information overload. However, with the rapid growth in the amount of information and the number of visitors to web sites in recent years, CF researchers are facing challenges with improving the quality of recommendations for users with sparse data and improving the scalability of the CF algorithms. To address these issues, an incremental user-based algorithm combined with item-based approach is proposed in this paper. By using N-nearest users and N-nearest items in the prediction generation, the algorithm requires an O(N) space for storing necessary similarities for the online prediction computation and at the same time gets improvement of scalability. The experiments suggest that the incremental user-based algorithm provides better quality than the best available classic Pearson correlation-based CF algorithms when the data set is sparse.

Sign in / Sign up

Export Citation Format

Share Document