Content-based Recommendation Model Research Articles

Research on the Recommendation System of Music e-learning Resources with Blockchain based on Hybrid Deep Learning Model

Learners are confronted with an ever-growing array of diverse and complex educational resources as music education increasingly moves to online platforms. Traditional resource curation methods, which rely heavily on educators, fall short of meeting the dynamic needs of modern students. To address this issue, we present a novel recommendation system for music e-learning resources that combines the power of blockchain technology with a hybrid deep learning model. Our model combines blockchain's robust security and transparency features with advanced deep learning algorithms, enhancing the personalization and efficiency of resource recommendations. A backpropagation neural network with K nearest neighbor classification, traditional collaborative filtering (CF), and an improved CF algorithm are used in the hybrid approach. For the back propagation neural network algorithm, K nearest neighbor classification algorithm, traditional collaborative filtering (CF) and improved CF algorithm, the accuracy rate of improved CF algorithm is higher, reaching 95%. Comparing the proposed model with the association rule-based recommendation model and the content-based recommendation model, the model constructed in this study received high evaluation from experts, with an average score of 98, and more than 97% of them gave a high score of 95 or more, and the evaluation of experts tended to be consistent. Overall, the model proposed in this study can make better recommendations for music education learning resources and bring users a good learning experience, so this study has some practical application value. This research demonstrates a highly effective, blockchain-enhanced recommendation system for music e-learning resources. Our model has significant practical value and potential for adoption in online music education platforms because it provides tailored educational content and an enhanced learning experience.

CBIR-DSS: Business Decision Oriented Content-Based Recommendation Model for E-Commerce

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.

Impact of Personalized Recommendation on Today’s News Communication through Algorithmic Mechanism in the New Media Era

In recent years, the continuous innovation of technology has greatly contributed to the great changes in the media industry. The rapid development of big data and artificial intelligence technologies has enabled people to transition from the era of new media to the era of intelligent media. While the automation of news production brings broad prospects for intelligent media, it also accelerates the challenge of information explosion. Facing the massive amount of news and information, how to get the information users want quickly has become a big problem. In order to solve the audience’s information anxiety, personalized news recommendation system is born. In fact, news gate-keeping is an important part of news distribution. In the era of smart media, algorithmic distribution has impacted the original distribution mode and brought challenges to news gate-keeping. Personalized news recommendation is one of the gate-keeping methods of intelligent media. At the same time, with the rapid development of the Internet and information technology, today’s society has entered a period of information explosion. In terms of news, the rapid development of the Internet has made it easier to publish and read news on the Internet. As a result, online news has become an important way for people to get information. However, the previous news websites had a large amount of news information, but only collected and consolidated the news. As a result, users were left to passively receive news information from news sites and find the content they needed. Consequently, although the Internet has a huge amount of complicated news information, it is unable to meet the diversified and personalized news needs of users. In order to solve this issue, researchers are constantly looking for solutions. The emergence of recommendation system is an effective measure to cope with the above problem. The mainstream models of recommendation systems are collaborative filtering model and content-based recommendation model. However, there are two essential problems with collaborative filtering. The first one is the cold start problem, and the second one is that the preference matrix of item users becomes sparse as the number of items and users grows. These two issues can seriously affect the recommendation accuracy of the recommendation system. As a result, a hybrid recommendation system is built by fusing common recommendation algorithms. This system can not only deliver personalized information to different users, but also compensate the shortcomings of a single algorithm to a certain extent. To be specific, the newly constructed hybrid recommendation system can push news of interest to users according to their demographic attributes, behavioral attributes, and interests, thus expanding the scope of news communication.

Research on Cosine Similarity and Pearson Correlation Based Recommendation Models

Recommendation models are based on data mining and machine learning techniques to suggest product and services to users. Advancement in the machine learning field results in the growth of more efficient recommendation models. Recommendation systems can contribute a lot to increase the business of internet based companies. Techniques for making recommendations generally fall into two categories namely collaborative filtering based models and content based recommendation models. The first techniques explores similarity based approaches like cosine similarity, Pearson correlation similarity etc. The second approaches uses predictions based on explicit and implicit ratings given by users. Recommendation systems try to suggest products to the users based on their previous purchases or general nature. A successful recommendation system is capable of improving the product sales to a large extend. This paper reviews about various recommendation models and performs a comparison of these models.

Exploiting Visual Content of Book Front Cover to Aggrandize the Content Based Book Recommendation System

In modern e-commerce world Recommendation Systems are playing a key role in supporting customers to take a decision. With this kind of services customers can choose comfortably the products as per their preferences from a long list of available products. It’s not only a boon for the customers; it will boost the sales for the organization and generate better revenues. Due to diverse domain characteristics, each domain requires different kinds of recommendation models. Content based recommendation model is one of the recommendation models which purely rely on product features and the current user preferences. This model is more effective for the domains like news, micro-blogs, books, movie plots and scientific papers etc. In this paper we propose a content-based filtering model for book recommender system by utilizing its overall textual features as well as visual features of its front cover. Numerous surveys have demonstrated that book readers are highly inclined to its covers that are visually attractive1. Book front cover is the first representative candidate of the book that will reveal the overall sense of the book; hence we considered book front cover as one of the book contents along with the text. Our experiment shows that augmenting the visual features to the existing content-based recommender models performed well.

Leveraging Image Visual Features in Content-Based Recommender System

Content-based (CB) and collaborative filtering (CF) recommendation algorithms are widely used in modern e-commerce recommender systems (RSs) to improve user experience of personalized services. Item content features and user-item rating data are primarily used to train the recommendation model. However, sparse data would lead such systems unreliable. To solve the data sparsity problem, we consider that more latent information would be imported to catch users’ potential preferences. Therefore, hybrid features which include all kinds of item features are used to excavate users’ interests. In particular, we find that the image visual features can catch more potential preferences of users. In this paper, we leverage the combination of user-item rating data and item hybrid features to propose a novel CB recommendation model, which is suitable for rating-based recommender scenarios. The experimental results show that the proposed model has better recommendation performance in sparse data scenarios than conventional approaches. Besides, training offline and recommendation online make the model has higher efficiency on large datasets.

MODELING WEB NAVIGATION USING GRAMMATICAL INFERENCE

In this article, a method that models user navigation on the web, as opposed to a single website, is presented, aiming to assist the user by recommending pages. User modeling is done through data mining of web usage logs, resulting in aggregate, rather than personal models. The proposed approach extends grammatical inference methods by introducing an extra merging criterion, which examines the semantic similarity of automaton states. The experimental results showed that the method does indeed facilitate the modeling of web navigation, which was not possible with the existing web usage mining methods. However, a content-based recommendation model is shown to still outperform the proposed method, which suggests that the knowledge of the navigation sequence does not contribute to the recommendation process. This is due to the thematic cohesion of navigation sessions, in comparison to the large thematic diversity of web usage data. Among three variants of the proposed method, the one based on Blue Fringe, that examines a larger space of possible merges, performs better.

A multigranular linguistic content-based recommendation model

Recommendation systems are a clear example of an e-service that helps the users to find the most suitable products they are looking for, according to their preferences, among a vast quantity of information. These preferences are usually related to human perceptions because the customers express their needs, taste, and so forth to find a suitable product. The perceptions are better modeled by means of linguistic information due to the uncertainty involved in this type of information. In this article, we propose a content-based recommendation model that will offer a more flexible context to improve the final recommendations where the preferences provided by the sources will be modeled by means of linguistic variables assessed in different linguistic term sets. The proposal consists of offering a multigranular linguistic context for expressing the preferences instead of forcing users to use a unique scale. Then the content-based recommendation model will look for the most suitable product(s), comparing them with the customer(s) information according to its resemblance. © 2007 Wiley Periodicals, Inc. Int J Int Syst 22: 419–434, 2007.