Using Social Networks to Solve Data Sparsity Problem in One-Class Collaborative Filtering

2010 ◽  
Author(s):  
Hamza Kaya ◽  
Ferda Nur Alpaslan
Keyword(s):  
Social Networks ◽  
Collaborative Filtering ◽  
Data Sparsity ◽  
Sparsity Problem

A hybrid approach for article recommendation in research social networks

2017 ◽  
Vol 44 (5) ◽  
pp. 696-711 ◽  
Author(s):  
Jianshan Sun ◽  
Yuanchun Jiang ◽  
Xusen Cheng ◽  
Wei Du ◽  
Yezheng Liu ◽  
...  
Keyword(s):  
Social Networks ◽  
Social Network ◽  
Social Networking ◽  
Collaborative Filtering ◽  
Digital Libraries ◽  
User Study ◽  
Hybrid Approach ◽  
Quality Information ◽  
Complex Task ◽  
Data Sparsity

With the prevalence of research social networks, determining effective methods for recommending scientific articles to online scholars has become a challenging and complex task. Current studies on article recommendation works are focused on digital libraries and reference sharing websites while studies on research social networking websites have seldom been conducted. Existing content-based approaches or collaborative filtering approaches suffer from the problem of data sparsity. The quality information of articles has been largely ignored in previous studies, thus raising the need for a unified recommendation framework. We propose a hybrid approach to combine relevance, connectivity and quality to recommend scientific articles. The effectiveness of the proposed framework and methods is verified using a user study on a real research social network website. The results demonstrate that our proposed methods outperform baseline methods.

Combining Item Rating Similarity and Item Classification Similarity for Better Recommendation Quality

2012 ◽  
Vol 461 ◽  
pp. 289-292
Author(s):  
Kai Zhou
Keyword(s):  
Collaborative Filtering ◽  
Recommender Systems ◽  
Fundamental Problem ◽  
The Other ◽  
Recommendation Algorithm ◽  
Data Sparsity ◽  
Filtering Method ◽  
Product Recommendation ◽  
Sparsity Problem

Recommender systems are becoming increasingly popular, and collaborative filtering method is one of the most important technologies in recommender systems. The ability of recommender systems to make correct predictions is fundamentally determined by the quality and fittingness of the collaborative filtering that implements them. It is currently mainly used for business purposes such as product recommendation. Collaborative filtering has two types. One is user based collaborative filtering using the similarity between users to predict and the other is item based collaborative filtering using the similarity between items. Although both of them are successfully applied in wide regions, they suffer from a fundamental problem of data sparsity. This paper gives a personalized collaborative filtering recommendation algorithm combining the item rating similarity and the item classification similarity. This method can alleviate the data sparsity problem in the recommender systems

scholarly journals Determining Trust Based Examination on Social Networks for Hotel Recommendation

2019 ◽  
Vol 8 (9S4) ◽  
pp. 218-221 ◽  
Keyword(s):  
Social Networks ◽  
Social Trust ◽  
Research Work ◽  
Cold Start ◽  
Data Sparsity ◽  
Sparsity Problem ◽  
Implicit And Explicit ◽  
Fake Reviews ◽  
Trust Information

The objective of this research work is to enhance the performance of a novel recommendation site for tavern by mining the data about all kinds of hotel in websites. When we are about to view the rating of a hotel given by other people, we can’t rely accurately on it as there are many fake reviews. So we can overcome the problem by viewing only the ratings given by our friends and friends of friends in our trusted network. TrustSVD algorithm considered for ensuring this. By considering both the implicit and explicit opinions of ratings the predictions are made. The proposed technique is used to merge with social trust information thus from that we can get the trusted network. Thus, by using the trusted network reviews we can avoid the fake reviews. In case of cold start and data sparsity problem the friends of friends list is considered.

An Analysis of Group Recommendation Strategies

2010 ◽  
Vol 14 (6) ◽  
pp. 729-734 ◽  
Author(s):  
Shlomo Berkovsky ◽  
◽  
Jill Freyne
Keyword(s):  
Collaborative Filtering ◽  
Recommender Systems ◽  
Insufficient Data ◽  
Group Recommendation ◽  
Data Sparsity ◽  
Aggregated Data ◽  
Sparsity Problem ◽  
Group Recommendations

Collaborative filtering recommender systems often suffer from a data sparsity problem, where systems have insufficient data to generate accurate recommendations. To partially resolve this, the use of group aggregated data in the collaborative filtering recommendations process has been suggested. Although group recommendations are typically less accurate than personalized recommendations, they can be more accurate than generic ones, which are the natural fall back when personalized recommendations cannot be generated. This work presents a study that exploits a dataset of recipe ratings from families of users, in order to evaluate the accuracy of several group recommendation strategies and weighting models.

Integrating Sentiment Analysis on Hybrid Collaborative Filtering Method in a Big Data Environment

2020 ◽  
Vol 19 (02) ◽  
pp. 385-412 ◽  
Author(s):  
P. Shanmuga Sundari ◽  
M. Subaji
Keyword(s):  
Collaborative Filtering ◽  
Recommendation System ◽  
Semantic Analysis ◽  
Least Square ◽  
Data Sparsity ◽  
Filtering Method ◽  
Sparsity Problem ◽  
Hybrid Collaborative Filtering ◽  
User Ratings ◽  
Hybrid Recommendation

Most of the traditional recommendation systems are based on user ratings. Here, users provide the ratings towards the product after use or experiencing it. Accordingly, the user item transactional database is constructed for recommendation. The rating based collaborative filtering method is well known method for recommendation system. This system leads to data sparsity problem as the user is unaware of other similar items. Web cataloguing service such as tags plays a significant role to analyse the user’s perception towards a particular product. Some system use tags as additional resource to reduce the data sparsity issue. But these systems require lot of specific details related to the tags. Existing system either focuses on ratings or tags based recommendation to enhance the accuracy. So these systems suffer from data sparsity and efficiency problem that leads to ineffective recommendations accuracy. To address the above said issues, this paper proposed hybrid recommendation system (Iter_ALS Iterative Alternate Least Square) to enhance the recommendation accuracy by integrating rating and emotion tags. The rating score reveals overall perception of the item and emotion tags reflects user’s feelings. In the absence of emotional tags, scores found in rating is assumed as positive or negative emotional tag score. Lexicon based semantic analysis on emotion tags value is adopted to represent the exclusive value of tag. Unified value is represented into Iter_ALS model to reduce the sparsity problem. In addition, this method handles opinion bias between ratings and tags. Experiments were tested and verified using a benchmark project of MovieLens dataset. Initially this model was tested with different sparsity levels varied between 0%-100 percent and the results obtained from the experiments shows the proposed method outperforms with baseline methods. Further tests were conducted to authenticate how it handles opinion bias by users before recommending the item. The proposed method is more capable to be adopted in many real world applications

Research of Data Sparsity Based on Collaborative Filtering Algorithm

2013 ◽  
Vol 462-463 ◽  
pp. 856-860
Author(s):  
Li Min Liu ◽  
Peng Xiang Zhang ◽  
Le Lin ◽  
Zhi Wei Xu
Keyword(s):  
Collaborative Filtering ◽  
Experimental Results ◽  
Recommendation Algorithm ◽  
Data Sparsity ◽  
Filtering Algorithm ◽  
Data Sparseness ◽  
Collaborative Filtering Algorithm ◽  
Scoring Matrix ◽  
Sparsity Problem ◽  
Improved Algorithm

During the traditional collaborative filtering recommendation algorithm be impacted by itself data sparseness problem. It can not provide accurate recommendation result. In this paper, Using traditional collaborative filtering algorithm and the concept of similar level, take advantage of the idea of data populating to solve sparsity problem, then using the Weighted Slope One algorithm to recommend calculating. Experimental results show that the improved algorithm solved the problem of the recommendation results of low accuracy because of the sparse scoring matrix, and it improved the algorithm recommended results to a certain extent.

scholarly journals Recommendation System Using Autoencoders

2020 ◽  
Vol 10 (16) ◽  
pp. 5510 ◽  
Author(s):  
Diana Ferreira ◽  
Sofia Silva ◽  
António Abelha ◽  
José Machado
Keyword(s):  
Collaborative Filtering ◽  
Recommendation System ◽  
Mean Squared Error ◽  
Singular Value ◽  
High Dimensions ◽  
Data Sparsity ◽  
Filtering Method ◽  
Squared Error ◽  
Sparsity Problem ◽  
Value Decomposition

The magnitude of the daily explosion of high volumes of data has led to the emergence of the Big Data paradigm. The ever-increasing amount of information available on the Internet makes it increasingly difficult for individuals to find what they need quickly and easily. Recommendation systems have appeared as a solution to overcome this problem. Collaborative filtering is widely used in this type of systems, but high dimensions and data sparsity are always a main problem. With the idea of deep learning gaining more importance, several works have emerged to improve this type of filtering. In this article, a product recommendation system is proposed where an autoencoder based on a collaborative filtering method is employed. A comparison of this model with the Singular Value Decomposition is made and presented in the results section. Our experiment shows a very low Root Mean Squared Error (RMSE) value, considering that the recommendations presented to the users are in line with their interests and are not affected by the data sparsity problem as the datasets are very sparse, 0.996. The results are quite promising achieving an RMSE value of 0.029 in the first dataset and 0.010 in the second one.

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