Improving Scalability, Sparsity and Cold Start user Issues in Collaborative Tagging with Incremental Clustering and Trust

2019 ◽  
Vol 12 ◽  
Author(s):  
Latha Banda ◽  
Karan Singh
Keyword(s):  
Collaborative Filtering ◽  
Recommender Systems ◽  
Web Sites ◽  
Cold Start ◽  
Incremental Clustering ◽  
Collaborative Tagging ◽  
Or Efficiency ◽  
Huge Data ◽  
Day By Day

: As web sites grows the complexity increases in many websites due to huge data. To maintain these data it is very difficult because there are many number of users are increasing day by day. As per information of many websites, there is very insufficient data to get the accuracy or efficiency of web sites. To improve the quality of websites, Recommender Systems are introduced. On the basis of these recommender systems, user gives the ratings to an item and then the reviews are generated for each an item so that the user might know the information of items relevant to his preferences. Here the RS is classified into content-based and collaborative filtering. Later tagging also included in this collaborative filtering. The main issues of Collaborative Filtering are Scalability, cold-start user and sparsity problems. We propose and explore the benefits of collaborative filtering based on tagging for sparseness, scalability and Cold start user issues.

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.

Modelling trust networks using resistive circuits for trust-aware recommender systems

2016 ◽  
Vol 43 (1) ◽  
pp. 135-144 ◽  
Author(s):  
Mehdi Hosseinzadeh Aghdam ◽  
Morteza Analoui ◽  
Peyman Kabiri
Keyword(s):  
Collaborative Filtering ◽  
Recommender Systems ◽  
Recommender System ◽  
Cold Start ◽  
User Preferences ◽  
Item Selection ◽  
Trust Networks ◽  
Trust Inference ◽  
Trust Information

Recommender systems have been widely used for predicting unknown ratings. Collaborative filtering as a recommendation technique uses known ratings for predicting user preferences in the item selection. However, current collaborative filtering methods cannot distinguish malicious users from unknown users. Also, they have serious drawbacks in generating ratings for cold-start users. Trust networks among recommender systems have been proved beneficial to improve the quality and number of predictions. This paper proposes an improved trust-aware recommender system that uses resistive circuits for trust inference. This method uses trust information to produce personalized recommendations. The result of evaluating the proposed method on Epinions dataset shows that this method can significantly improve the accuracy of recommender systems while not reducing the coverage of recommender systems.

scholarly journals A New Similarity Measure for User-based Collaborative Filtering in Recommender Systems

2015 ◽  
Vol 14 (9) ◽  
pp. 6118-6128 ◽  
Author(s):  
T. Srikanth ◽  
M. Shashi
Keyword(s):  
Collaborative Filtering ◽  
Recommender Systems ◽  
Similarity Measure ◽  
Recommender System ◽  
Distance Measure ◽  
Current User ◽  
Challenging Problem ◽  
Popular Approach ◽  
Similarity Calculation

Collaborative filtering is a popular approach in recommender Systems that helps users in identifying the items they may like in a wagon of items. Finding similarity among users with the available item ratings so as to predict rating(s) for unseen item(s) based on the preferences of likeminded users for the current user is a challenging problem. Traditional measures like Cosine similarity and Pearson correlation’s correlation exhibit some drawbacks in similarity calculation. This paper presents a new similarity measure which improves the performance of Recommender System. Experimental results on MovieLens dataset show that our proposed distance measure improves the quality of prediction. We present clustering results as an extension to validate the effectiveness of our proposed method.

scholarly journals Syn Rec: A Prediction Technique using Collaborative Filtering and Synergy Score

2019 ◽  
Vol 8 (5S3) ◽  
pp. 457-463
Keyword(s):  
Social Interactions ◽  
Collaborative Filtering ◽  
Recommender Systems ◽  
Recommender System ◽  
Effective Technique ◽  
Novel Technique ◽  
Trust Relation ◽  
Prediction Technique ◽  
User Trust

Recommender systems are techniques designed to produce personalized recommendations. Data sparsity, scalability cold start and quality of prediction are some of the problems faced by a recommender system. Traditional recommender systems consider that all the users are independent and identical, its an assumption which leads to a total ignorance of social interactions and trust among user. Trust relation among users ease the work of recommender systems to produce better quality of recommendations. In this paper, an effective technique is proposed using trust factor extracted with help of ratings given so that quality can be improved and better predictions can be done. A novel-technique has been proposed for recommender system using film-trust dataset and its effectiveness has been justified with the help of experiments.

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.

An Ontology-Based Collaborative Filtering Personalized Recommendation

2012 ◽  
Vol 267 ◽  
pp. 79-82
Author(s):  
Pu Wang
Keyword(s):  
Collaborative Filtering ◽  
Web Sites ◽  
Information Overload ◽  
Cold Start ◽  
Personalized Recommendation ◽  
Collaborative Approach ◽  
The Matrix ◽  
Special Domain ◽  
Sparsity Problem ◽  
The Given

Recommender systems have been successfully used to tackle the problem of information overload, where users of products have too many choices and overwhelming amount of information about each choice. Personalization is widely used in various fields to provide users with more suitable and personalized service. Many e-commerce web sites such as online shop retailers make use of recommendation systems. In order to make recommendations to a user, collaborative filtering is an important personalized recommendation technique applied widely in E-commerce. The collaborative approach faces the hard issue of cold start problem and the matrix sparsity problem. The paper presents a collaborative filtering personalized recommendation approach based on ontology in the special domain. The method combines ontology technology and item-based collaborative filtering. The given recommendation approach can tackle the traditional recommenders problems, such as matrix sparsity and cold start problems.

scholarly journals An Ontology-Based Recommender System with an Application to the Star Trek Television Franchise

2019 ◽  
Vol 11 (9) ◽  
pp. 182 ◽  
Author(s):  
Paul Sheridan ◽  
Mikael Onsjö ◽  
Claudia Becerra ◽  
Sergio Jimenez ◽  
George Dueñas
Keyword(s):  
Collaborative Filtering ◽  
Recommender Systems ◽  
Recommender System ◽  
System Development ◽  
Cold Start ◽  
Star Trek ◽  
User Preference ◽  
Future Research ◽  
Television Series ◽  
K Nearest Neighbors

Collaborative filtering based recommender systems have proven to be extremely successful in settings where user preference data on items is abundant. However, collaborative filtering algorithms are hindered by their weakness against the item cold-start problem and general lack of interpretability. Ontology-based recommender systems exploit hierarchical organizations of users and items to enhance browsing, recommendation, and profile construction. While ontology-based approaches address the shortcomings of their collaborative filtering counterparts, ontological organizations of items can be difficult to obtain for items that mostly belong to the same category (e.g., television series episodes). In this paper, we present an ontology-based recommender system that integrates the knowledge represented in a large ontology of literary themes to produce fiction content recommendations. The main novelty of this work is an ontology-based method for computing similarities between items and its integration with the classical Item-KNN (K-nearest neighbors) algorithm. As a study case, we evaluated the proposed method against other approaches by performing the classical rating prediction task on a collection of Star Trek television series episodes in an item cold-start scenario. This transverse evaluation provides insights into the utility of different information resources and methods for the initial stages of recommender system development. We found our proposed method to be a convenient alternative to collaborative filtering approaches for collections of mostly similar items, particularly when other content-based approaches are not applicable or otherwise unavailable. Aside from the new methods, this paper contributes a testbed for future research and an online framework to collaboratively extend the ontology of literary themes to cover other narrative content.

scholarly journals Deep Matrix Factorization Approach for Collaborative Filtering Recommender Systems

2020 ◽  
Vol 10 (14) ◽  
pp. 4926 ◽  
Author(s):  
Raúl Lara-Cabrera ◽  
Ángel González-Prieto ◽  
Fernando Ortega
Keyword(s):  
Collaborative Filtering ◽  
Recommender Systems ◽  
Matrix Factorization ◽  
Sources Of Information ◽  
Factorization Approach ◽  
Filtering Method ◽  
Layered Architecture ◽  
Fundamental Part ◽  
Many Sources

Providing useful information to the users by recommending highly demanded products and services is a fundamental part of the business of many top tier companies. Recommender Systems make use of many sources of information to provide users with accurate predictions and novel recommendations of items. Here we propose, DeepMF, a novel collaborative filtering method that combines the Deep Learning paradigm with Matrix Factorization (MF) to improve the quality of both predictions and recommendations made to the user. Specifically, DeepMF performs successive refinements of a MF model with a layered architecture that uses the acquired knowledge in a layer as input for subsequent layers. Experimental results showed that the quality of both the predictions and recommendations of DeepMF overcome the baselines.

Recommender Systems Using Collaborative Tagging

2020 ◽  
Vol 16 (3) ◽  
pp. 183-200
Author(s):  
Latha Banda ◽  
Karan Singh ◽  
Le Hoang Son ◽  
Mohamed Abdel-Basset ◽  
Pham Huy Thong ◽  
...  
Keyword(s):  
Social Networking ◽  
Recommender Systems ◽  
Recommender System ◽  
Prediction Accuracy ◽  
Clustering Algorithms ◽  
Incremental Clustering ◽  
Collaborative Tagging ◽  
Accuracy Ratio ◽  
Hybrid Recommender ◽  
Gradual Decay

Collaborative tagging is a useful and effective way for classifying items with respect to search, sharing information so that users can be tagged via online social networking. This article proposes a novel recommender system for collaborative tagging in which the genre interestingness measure and gradual decay are utilized with diffusion similarity. The comparison has been done on the benchmark recommender system datasets namely MovieLens, Amazon datasets against the existing approaches such as collaborative filtering based on tagging using E-FCM, and E-GK clustering algorithms, hybrid recommender systems based on tagging using GA and collaborative tagging using incremental clustering with trust. The experimental results ensure that the proposed approach achieves maximum prediction accuracy ratio of 9.25% for average of various splits data of 100 users, which is higher than the existing approaches obtained only prediction accuracy of 5.76%.

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