Recommendation Algorithms and Web Mining

A special issue of Information (ISSN 2078-2489). This special issue belongs to the section "Artificial Intelligence".

Deadline for manuscript submissions: closed (31 May 2023) | Viewed by 24432

Special Issue Editor

Istituto di Analisi dei Sistemi ed Informatica (IASI), Consiglio Nazionale delle Ricerche (CNR), 00185 Rome, Italy
Interests: information retrieval; text mining; web mining; social media analysis

Special Issue Information

Dear Colleagues,

People access the web daily to stay informed, keep in touch with friends, and use online services. Today, web users can share experiences with friends, and the explosion of available data makes users feel overwhelmed with information. Thus, they need to be guided to find content that interests them. Furthermore, the number of online services has increased, and users can rely on several online platforms for buying products, booking flights, or downloading songs. Given the multitude of offers, users may need support in the decision process for making transactions.

Recommendation systems have been largely used for suggesting products as well as for addressing the problem of information overload. A typical recommendation system suggests new items to users based on their preferences. Items can be products, books, but also textual content (e.g., news, tweets) and multimedia files (e.g., songs, movies). Recommendation algorithms are even employed in social networks for suggesting friends and in location-based social networks for recommending venues.

Designing recommendation systems for web users poses significant challenges. Collaborative filtering and content-based approaches have to face different problems ranging from the heterogeneous and dynamic nature of the web to scalability issues. Moreover, recommendation systems may suffer from data sparsity and lacking user profiles due to privacy concerns.

Web mining can help to improve the accuracy and scalability of recommendation systems. The analysis of users’ previous activities, such as navigational data, posts, purchases, and reviews, are important clues to better understand user interests, predict future actions, and timely satisfy users’ needs.

The MDPI Information journal invites submissions to a Special Issue on “Recommendation Algorithms and Web Mining”. We aim to collect recent advances in web mining addressing the main challenges of recommendation algorithms and leveraging user activities to improve the efficiency and effectiveness of recommendation systems.

Dr. Ida Mele
Guest Editor

Manuscript Submission Information

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Keywords

  • Content-based recommendation systems
  • Collaborative-filtering approaches for recommendation systems
  • Personalized recommendation systems and user profiling
  • Data mining algorithms for discovering usage profiles
  • Recommending products in e-commerce websites
  • Recommendation algorithms for web textual content (e.g., web pages, news, blog articles)
  • Recommendation algorithms for multimedia files (e.g., songs, videos)
  • Social network analysis for improving recommendations
  • Recommendation algorithms for social networks (e.g., recommending posts, people to follow)’
  • Suggesting venues and POIs in location-based social networks
  • Scalable algorithms for web recommendations

Published Papers (9 papers)

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Research

20 pages, 3467 KiB  
Article
Analyzing Social Media Data Using Sentiment Mining and Bigram Analysis for the Recommendation of YouTube Videos
by Ken McGarry
Information 2023, 14(7), 408; https://doi.org/10.3390/info14070408 - 16 Jul 2023
Viewed by 2230
Abstract
In this work we combine sentiment analysis with graph theory to analyze user posts, likes/dislikes on a variety of social media to provide recommendations for YouTube videos. We focus on the topic of climate change/global warming, which has caused much alarm and controversy [...] Read more.
In this work we combine sentiment analysis with graph theory to analyze user posts, likes/dislikes on a variety of social media to provide recommendations for YouTube videos. We focus on the topic of climate change/global warming, which has caused much alarm and controversy over recent years. Our intention is to recommend informative YouTube videos to those seeking a balanced viewpoint of this area and the key arguments/issues. To this end we analyze Twitter data; Reddit comments and posts; user comments, view statistics and likes/dislikes of YouTube videos. The combination of sentiment analysis with raw statistics and linking users with their posts gives deeper insights into their needs and quest for quality information. Sentiment analysis provides the insights into user likes and dislikes, graph theory provides the linkage patterns and relationships between users, posts, and sentiment. Full article
(This article belongs to the Special Issue Recommendation Algorithms and Web Mining)
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31 pages, 3322 KiB  
Article
CBIR-DSS: Business Decision Oriented Content-Based Recommendation Model for E-Commerce
by Ashish Bagwari, Anurag Sinha, N. K. Singh, Namit Garg and Jyotshana Kanti
Information 2022, 13(10), 479; https://doi.org/10.3390/info13100479 - 5 Oct 2022
Cited by 6 | Viewed by 2796
Abstract
Business-based decision support systems have been proposed for a few decades in the e-commerce and textile industries. However, these Decision Support Systems (DSS) have not been so productive in terms of business decision delivery. In our proposed model, we introduce a content-based image [...] Read more.
Business-based decision support systems have been proposed for a few decades in the e-commerce and textile industries. However, these Decision Support Systems (DSS) have not been so productive in terms of business decision delivery. In our proposed model, we introduce a content-based image retrieval model based on a DSS and recommendations system for the textile industry, either offline or online. We used the Fashion MNIST dataset developed by Zalando to train our deep learning model. Our proposed hybrid model can demonstrate how a DSS can be integrated with a system that can separate customers based on their personal characteristics in order to tailor recommendations of products using behavioral analytics, which is trained based on MBTI personality data and Deap EEG data containing numerous pre-trained EEG brain waves. With this hybrid, a DSS can also show product usage analytics. Our proposed model has achieved the maximum accuracy compared to other proposed state-of-the-art models due to its qualitative analysis. In the first section of our analysis, we used a deep learning algorithm to train our CBIR model based on different classifiers such as VGG-net, Inception-Net, and U-net which have achieved an accuracy of 98.2% with a 2% of minimized error rate. The result was validated using different performance metrics such as F-score, F-weight, Precision, and Recall. The second part of our model has been tested on different machine learning algorithms with an accuracy rate of 89.9%. Thus, the entire model has been trained, validated, and tested separately to gain maximum efficiency. Our proposal for a DSS system, which integrates several subsystems with distinct functional sets and several model subsystems, is what makes this study special. Customer preference is one of the major problems facing merchants in the textile industry. Additionally, it can be extremely difficult for retailers to predict customer interests and preferences to create products that fulfill those needs. The three innovations presented in this work are a conceptual model for personality characterization, utilizing an amalgamation of an ECG classification model, a suggestion for a textile image retrieval model using Denoising Auto-Encoder, and a language model based on the MBTI for customer rating. Additionally, we have proposed a section showing how blockchain integration in data pre-processing can enhance its security and AI-based software quality assurance in a multi-model system. Full article
(This article belongs to the Special Issue Recommendation Algorithms and Web Mining)
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13 pages, 1088 KiB  
Article
On Exploiting Rating Prediction Accuracy Features in Dense Collaborative Filtering Datasets
by Dimitris Spiliotopoulos, Dionisis Margaris and Costas Vassilakis
Information 2022, 13(9), 428; https://doi.org/10.3390/info13090428 - 11 Sep 2022
Cited by 4 | Viewed by 1603
Abstract
One of the typical goals of collaborative filtering algorithms is to produce rating predictions with values very close to what real users would give to an item. Afterward, the items having the largest rating prediction values will be recommended to the users by [...] Read more.
One of the typical goals of collaborative filtering algorithms is to produce rating predictions with values very close to what real users would give to an item. Afterward, the items having the largest rating prediction values will be recommended to the users by the recommender system. Collaborative filtering algorithms can be applied to both sparse and dense datasets, and each of these dataset categories involves different kinds of risks. As far as the dense collaborative filtering datasets are concerned, where the rating prediction coverage is, most of the time, very high, we usually face large rating prediction times, issues concerning the selection of a user’s near neighbours, etc. Although collaborative filtering algorithms usually achieve better results when applied to dense datasets, there is still room for improvement, since in many cases, the rating prediction error is relatively high, which leads to unsuccessful recommendations and hence to recommender system unreliability. In this work, we explore rating prediction accuracy features, although in a broader context, in dense collaborative filtering datasets. We conduct an extensive evaluation, using dense datasets, widely used in collaborative filtering research, in order to find the associations between these features and the rating prediction accuracy. Full article
(This article belongs to the Special Issue Recommendation Algorithms and Web Mining)
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22 pages, 4319 KiB  
Communication
Study of the Yahoo-Yahoo Hash-Tag Tweets Using Sentiment Analysis and Opinion Mining Algorithms
by Adebayo Abayomi-Alli, Olusola Abayomi-Alli, Sanjay Misra and Luis Fernandez-Sanz
Information 2022, 13(3), 152; https://doi.org/10.3390/info13030152 - 15 Mar 2022
Cited by 17 | Viewed by 3911
Abstract
Mining opinion on social media microblogs presents opportunities to extract meaningful insight from the public from trending issues like the “yahoo-yahoo” which in Nigeria, is synonymous to cybercrime. In this study, content analysis of selected historical tweets from “yahoo-yahoo” hash-tag was conducted for [...] Read more.
Mining opinion on social media microblogs presents opportunities to extract meaningful insight from the public from trending issues like the “yahoo-yahoo” which in Nigeria, is synonymous to cybercrime. In this study, content analysis of selected historical tweets from “yahoo-yahoo” hash-tag was conducted for sentiment and topic modelling. A corpus of 5500 tweets was obtained and pre-processed using a pre-trained tweet tokenizer while Valence Aware Dictionary for Sentiment Reasoning (VADER), Liu Hu method, Latent Dirichlet Allocation (LDA), Latent Semantic Indexing (LSI) and Multidimensional Scaling (MDS) graphs were used for sentiment analysis, topic modelling and topic visualization. Results showed the corpus had 173 unique tweet clusters, 5327 duplicates tweets and a frequency of 9555 for “yahoo”. Further validation using the mean sentiment scores of ten volunteers returned R and R2 of 0.8038 and 0.6402; 0.5994 and 0.3463; 0.5999 and 0.3586 for Human and VADER; Human and Liu Hu; Liu Hu and VADER sentiment scores, respectively. While VADER outperforms Liu Hu in sentiment analysis, LDA and LSI returned similar results in the topic modelling. The study confirms VADER’s performance on unstructured social media data containing non-English slangs, conjunctions, emoticons, etc. and proved that emojis are more representative of sentiments in tweets than the texts. Full article
(This article belongs to the Special Issue Recommendation Algorithms and Web Mining)
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17 pages, 1989 KiB  
Article
A Rating Prediction Recommendation Model Combined with the Optimizing Allocation for Information Granularity of Attributes
by Jianfei Li, Yongbin Wang and Zhulin Tao
Information 2022, 13(1), 21; https://doi.org/10.3390/info13010021 - 5 Jan 2022
Cited by 6 | Viewed by 1724
Abstract
In recent years, graph neural networks (GNNS) have been demonstrated to be a powerful way to learn graph data. The existing recommender systems based on the implicit factor models mainly use the interactive information between users and items for training and learning. A [...] Read more.
In recent years, graph neural networks (GNNS) have been demonstrated to be a powerful way to learn graph data. The existing recommender systems based on the implicit factor models mainly use the interactive information between users and items for training and learning. A user–item graph, a user–attribute graph, and an item–attribute graph are constructed according to the interactions between users and items. The latent factors of users and items can be learned in these graph structure data. There are many methods for learning the latent factors of users and items. Still, they do not fully consider the influence of node attribute information on the representation of the latent factors of users and items. We propose a rating prediction recommendation model, short for LNNSR, utilizing the level of information granularity allocated on each attribute by developing a granular neural network. The different granularity distribution proportion weights of each attribute can be learned in the granular neural network. The learned granularity allocation proportion weights are integrated into the latent factor representation of users and items. Thus, we can capture user-embedding representations and item-embedding representations more accurately, and it can also provide a reasonable explanation for the recommendation results. Finally, we concatenate the user latent factor-embedding and the item latent factor-embedding and then feed it into a multi-layer perceptron for rating prediction. Extensive experiments on two real-world datasets demonstrate the effectiveness of the proposed framework. Full article
(This article belongs to the Special Issue Recommendation Algorithms and Web Mining)
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17 pages, 569 KiB  
Article
A Closer-to-Reality Model for Comparing Relevant Dimensions of Recommender Systems, with Application to Novelty
by François Fouss and Elora Fernandes
Information 2021, 12(12), 500; https://doi.org/10.3390/info12120500 - 1 Dec 2021
Cited by 2 | Viewed by 2431
Abstract
Providing fair and convenient comparisons between recommendation algorithms—where algorithms could focus on a traditional dimension (accuracy) and/or less traditional ones (e.g., novelty, diversity, serendipity, etc.)—is a key challenge in the recent developments of recommender systems. This paper focuses on novelty and presents a [...] Read more.
Providing fair and convenient comparisons between recommendation algorithms—where algorithms could focus on a traditional dimension (accuracy) and/or less traditional ones (e.g., novelty, diversity, serendipity, etc.)—is a key challenge in the recent developments of recommender systems. This paper focuses on novelty and presents a new, closer-to-reality model for evaluating the quality of a recommendation algorithm by reducing the popularity bias inherent in traditional training/test set evaluation frameworks, which are biased by the dominance of popular items and their inherent features. In the suggested model, each interaction has a probability of being included in the test set that randomly depends on a specific feature related to the focused dimension (novelty in this work). The goal of this paper is to reconcile, in terms of evaluation (and therefore comparison), the accuracy and novelty dimensions of recommendation algorithms, leading to a more realistic comparison of their performance. The results obtained from two well-known datasets show the evolution of the behavior of state-of-the-art ranking algorithms when novelty is progressively, and fairly, given more importance in the evaluation procedure, and could lead to potential changes in the decision processes of organizations involving recommender systems. Full article
(This article belongs to the Special Issue Recommendation Algorithms and Web Mining)
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22 pages, 1009 KiB  
Article
SC-Political ResNet: Hashtag Recommendation from Tweets Using Hybrid Optimization-Based Deep Residual Network
by Santosh Kumar Banbhrani, Bo Xu, Haifeng Liu and Hongfei Lin
Information 2021, 12(10), 389; https://doi.org/10.3390/info12100389 - 22 Sep 2021
Cited by 4 | Viewed by 2468
Abstract
Hashtags are considered important in various real-world applications, including tweet mining, query expansion, and sentiment analysis. Hence, recommending hashtags from tagged tweets has been considered significant by the research community. However, while many hashtag recommendation methods have been developed, finding the features from [...] Read more.
Hashtags are considered important in various real-world applications, including tweet mining, query expansion, and sentiment analysis. Hence, recommending hashtags from tagged tweets has been considered significant by the research community. However, while many hashtag recommendation methods have been developed, finding the features from dictionary and thematic words has not yet been effectively achieved. Therefore, we developed an effective method to perform hashtag recommendations, using the proposed Sine Cosine Political Optimization-based Deep Residual Network (SC-Political ResNet) classifier. The developed SCPO is designed by integrating the Sine Cosine Algorithm (SCA) with the Political Optimizer (PO) algorithm. Employing the parametric features from both, optimization can enable the acquisition of the global best solution, by training the weights of classifier. The hybrid features acquired from the keyword set can effectively find the information of words associated with dictionary, thematic, and more relevant keywords. Extensive experiments are conducted on the Apple Twitter Sentiment and Twitter datasets. Our empirical results demonstrate that the proposed model can significantly outperform state-of-the-art methods in hashtag recommendation tasks. Full article
(This article belongs to the Special Issue Recommendation Algorithms and Web Mining)
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14 pages, 1149 KiB  
Article
A Joint Summarization and Pre-Trained Model for Review-Based Recommendation
by Yi Bai, Yang Li and Letian Wang
Information 2021, 12(6), 223; https://doi.org/10.3390/info12060223 - 24 May 2021
Cited by 4 | Viewed by 2753
Abstract
Currently, reviews on the Internet contain abundant information about users and products, and this information is of great value to recommendation systems. As a result, review-based recommendations have begun to show their effectiveness and research value. Due to the accumulation of a large [...] Read more.
Currently, reviews on the Internet contain abundant information about users and products, and this information is of great value to recommendation systems. As a result, review-based recommendations have begun to show their effectiveness and research value. Due to the accumulation of a large number of reviews, it has become very important to extract useful information from reviews. Automatic summarization can capture important information from a set of documents and present it in the form of a brief summary. Therefore, integrating automatic summarization into recommendation systems is a potential approach for solving this problem. Based on this idea, we propose a joint summarization and pre-trained recommendation model for review-based rate prediction. Through automatic summarization and a pre-trained language model, the overall recommendation model learns a fine-grained summary representation of the key content as well as the relationships between words and sentences in each review. The review summary representations of users and items are finally incorporated into a neural collaborative filtering (CF) framework with interactive attention mechanisms to predict the rating scores. We perform experiments on the Amazon dataset and compare our method with several competitive baselines. Experimental results show that the performance of the proposed model is obviously better than that of the baselines. Relative to the current best results, the average improvements obtained on four sub-datasets randomly selected from the Amazon dataset are approximately 3.29%. Full article
(This article belongs to the Special Issue Recommendation Algorithms and Web Mining)
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14 pages, 874 KiB  
Article
FSCR: A Deep Social Recommendation Model for Misleading Information
by Depeng Zhang, Hongchen Wu and Feng Yang
Information 2021, 12(1), 37; https://doi.org/10.3390/info12010037 - 17 Jan 2021
Cited by 2 | Viewed by 2359
Abstract
The popularity of intelligent terminals and a variety of applications have led to the explosive growth of information on the Internet. Some of the information is real, some is not real, and may mislead people’s behaviors. Misleading information refers to false information made [...] Read more.
The popularity of intelligent terminals and a variety of applications have led to the explosive growth of information on the Internet. Some of the information is real, some is not real, and may mislead people’s behaviors. Misleading information refers to false information made up by some malicious marketer to create panic and seek benefits. In particular, when emergency events break out, many users may be misled by the misleading information on the Internet, which further leads them to buy things that are not in line with their actual needs. We call this kind of human activity ‘emergency consumption’, which not only fails to reflect users’ true interests but also causes the phenomenon of user preference deviation, and thus lowers the accuracy of the personal recommender system. Although traditional recommendation models have proven useful in capturing users’ general interests from user–item interaction records, learning to predict user interest accurately is still a challenging problem due to the uncertainty inherent in user behavior and the limited information provided by user–item interaction records. In addition, to deal with the misleading information, we divide user information into two types, namely explicit preference information (explicit comments or ratings) and user side information (which can show users’ real interests and will not be easily affected by misleading information), and then we create a deep social recommendation model which fuses user side information called FSCR. The FSCR model is significantly better than existing baseline models in terms of rating prediction and system robustness, especially in the face of misleading information; it can effectively identify the misleading users and complete the task of rating prediction well. Full article
(This article belongs to the Special Issue Recommendation Algorithms and Web Mining)
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