scholarly journals Critique on Natural Noise in Recommender Systems

2021 ◽  
Vol 15 (5) ◽  
pp. 1-30
Author(s):  
Wissam Al Jurdi ◽  
Jacques Bou Abdo ◽  
Jacques Demerjian ◽  
Abdallah Makhoul
Keyword(s):  
Recommender Systems ◽  
User Behavior ◽  
Cold Start ◽  
User Experiences ◽  
Data Sparsity ◽  
Natural Noise ◽  
E Learning ◽  
Management Techniques ◽  
Noise Management

Recommender systems have been upgraded, tested, and applied in many, often incomparable ways. In attempts to diligently understand user behavior in certain environments, those systems have been frequently utilized in domains like e-commerce, e-learning, and tourism. Their increasing need and popularity have allowed the existence of numerous research paths on major issues like data sparsity, cold start, malicious noise, and natural noise, which immensely limit their performance. It is typical that the quality of the data that fuel those systems should be extremely reliable. Inconsistent user information in datasets can alter the performance of recommenders, albeit running advanced personalizing algorithms. The consequences of this can be costly as such systems are employed in abundant online businesses. Successfully managing these inconsistencies results in more personalized user experiences. In this article, the previous works conducted on natural noise management in recommender datasets are thoroughly analyzed. We adequately explore the ways in which the proposed methods measure improved performances and touch on the different natural noise management techniques and the attributes of the solutions. Additionally, we test the evaluation methods employed to assess the approaches and discuss several key gaps and other improvements the field should realize in the future. Our work considers the likelihood of a modern research branch on natural noise management and recommender assessment.

scholarly journals Comparing quantitative and comment-based ratings for recommending open educational resources

2021 ◽  
Author(s):  
Dalia Hanna ◽  
Abdolreza Abhari ◽  
Alexander Ferworn
Keyword(s):  
Sentiment Analysis ◽  
Recommender Systems ◽  
Open Educational Resources ◽  
Educational Resources ◽  
Open Education ◽  
User Comments ◽  
E Learning ◽  
And Performance ◽  
Open Education Resources

The recent application of recommender systems for educational resources and e-learning has facilitated online and accessible education on social networks. However, there are currently few studies about the methods for evaluation and performance measurement of these recommender systems in the complicated environment of educational and social networking platforms. The purpose of this research paper is to investigate the effectiveness of using sentiment analysis methods for educational resources based on user comments and compare it with the quantitative approach based on user rating to recommend best open learning resources (OER) available through online OER repositories. The quality of the OER will be justified by comparing the user rating and the users' reviews. The quality of users' reviews is based on calculating the term frequency for selected positive and negative terms, then determining the similarity among the comments. Comments with positive or negative words confirm the high and low ratings respectively. Keywords: Open Education Resources, OER, Recommender Systems, Sentiment Analysis.

State of the Art Recommendation Approaches

2020 ◽  
pp. 1621-1651
Author(s):  
Bhupesh Rawat ◽  
Sanjay K. Dwivedi
Keyword(s):  
Recommender Systems ◽  
Domain Knowledge ◽  
Information Overload ◽  
State Of The Art ◽  
New Techniques ◽  
Contextual Data ◽  
Additional Information ◽  
E Learning ◽  
Traditional Approaches

Recommender systems have been used successfully in order to deal with information overload problems in a wide variety of domains ranging from e-commerce, e-tourism, to e-learning. They typically predict the ratings of unseen items by a user and recommend the top N items based on user's profile. Moreover, the profile can be enriched further by using additional information such as contextual data, domain knowledge, and tagging information among others for improving the quality of recommendations. Traditional approaches have not been effective in exploiting these additional data sources. Hence, new techniques need to be developed for extracting and integrating them into the recommendation process. In this article, the authors present a survey on state of the art recommendation approaches their algorithms, issues and also provides further research directions for developing smart and intelligent recommender systems.

scholarly journals Comparing quantitative and comment-based ratings for recommending open educational resources

2021 ◽  
Author(s):  
Dalia Hanna ◽  
Abdolreza Abhari ◽  
Alexander Ferworn
Keyword(s):  
Sentiment Analysis ◽  
Recommender Systems ◽  
Open Educational Resources ◽  
Educational Resources ◽  
Open Education ◽  
User Comments ◽  
E Learning ◽  
And Performance ◽  
Open Education Resources

The recent application of recommender systems for educational resources and e-learning has facilitated online and accessible education on social networks. However, there are currently few studies about the methods for evaluation and performance measurement of these recommender systems in the complicated environment of educational and social networking platforms. The purpose of this research paper is to investigate the effectiveness of using sentiment analysis methods for educational resources based on user comments and compare it with the quantitative approach based on user rating to recommend best open learning resources (OER) available through online OER repositories. The quality of the OER will be justified by comparing the user rating and the users' reviews. The quality of users' reviews is based on calculating the term frequency for selected positive and negative terms, then determining the similarity among the comments. Comments with positive or negative words confirm the high and low ratings respectively. Keywords: Open Education Resources, OER, Recommender Systems, Sentiment Analysis.

Web Content Recommendation Methods Based on Reinforcement Learning

2011 ◽  
pp. 2353-2380
Author(s):  
Nima Taghipour ◽  
Ahmad Kardan
Keyword(s):  
Reinforcement Learning ◽  
Recommender Systems ◽  
Information Overload ◽  
User Behavior ◽  
Web Content ◽  
Web Usage ◽  
Content Recommendation ◽  
Conceptual Relationships ◽  
The Web

Information overload is no longer news; the explosive growth of the Internet has made this issue increasingly serious for Web users. Recommender systems aim at directing users through this information space, toward the resources that best meet their needs and interests. In this chapter we introduce our novel machine learning perspective toward the web recommendation problem, based on reinforcement learning. Our recommendation method makes use of the web usage and content data to learn a predictive model of users’ behavior on the web and exploits the learned model to make web page recommendations. Unlike other recommender systems, our system does not use the static patterns discovered from web usage data, instead it learns to make recommendations as the actions it performs in each situation. In the proposed method we combined the conceptual and usage information in order to gain a more general model of user behavior and improve the quality of web recommendations. A hybrid web recommendation method is proposed by making use of the conceptual relationships among web resources to derive a novel model of the problem, enriched with semantic knowledge about the usage behavior. The method is evaluated under different settings and it is shown how this method can improve the overall quality of recommendations.

PageRank Algorithm-Based Recommender System Using Uniformly Average Rating Matrix

2019 ◽  
pp. 99-112
Author(s):  
Bathrinath S. ◽  
Saranyadevi S. ◽  
Thirumalai Kumaran S. ◽  
Saravanasankar S.
Keyword(s):  
Recommender Systems ◽  
Large Scale ◽  
User Behavior ◽  
Personalized Recommendation ◽  
Pagerank Algorithm ◽  
Large Scale Networks ◽  
Matrix Construction ◽  
Item Item ◽  
New Item

Applications of web data mining is the prediction of user behavior with respect to items. Recommender systems are being applied in knowledge discovery techniques to the problem of making decisions on personalized recommendation of information. Traditional CF approaches involve the amount of effort increases with number of users. Hence, new recommender systems need to be developed to process high quality recommendations for large-scale networks. In this chapter, a model for UAR matrix construction method for item rank calculations, a Page Rank-based item ranking approach are proposed. The analysis of various techniques for computing item-item similarities to identify relationship between the selected items and to produce a qualified recommendation for users to acquire the items as their wish. As a result, the new item rank-based approaches improve the quality of recommendation outcome. Results show that the proposed UAR method outperforms than the existing method. The same method is applied for the large real-time rating dataset like Movie Lens.

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