Book Recommendation System Research Articles

Enhancing Book Recommendation Accuracy through User Rating Analysis and Collaborative Filtering Techniques

Since online electronic books have become popular, book recommendation systems have been invented and challenged to handle the high demand from users in the digital era. This study aimed to develop and evaluate a book recommendation model using data mining techniques through RapidMiner Studio. The datasets used were comprised of 981,756 user ratings. Before conducting the data analytics, the data was pre-processed to eliminate duplicates and retain only the highest ratings. Collaborative Filtering (CF) techniques, particularly k-Nearest Neighbours (k-NN) and Matrix Factorization (KF), were employed to elicit insightful information for development and to highlight their capabilities in handling enormous datasets. Furthermore, statistical analysis, visualization, elementary modelling, and model combinations were investigated to compare their performance. To reinforce creditability, modelling techniques and parameter adjustments were integrated to optimize the performance of the algorithms, since the results indicated that different model settings and data partitions impacted the effectiveness of the recommendation system. Additionally, these results demonstrated the potential of hybrid models in improving the accuracy and efficiency of recommendation systems and highlighted the trade-off between algorithmic approaches and dataset characteristics that interplay in optimizing the performance of recommendation systems.

Personalized Mood-Centric Book Recommendation Integrating Machine Learning with Content Based Filtering

This paper introduces a novel personalized book recommendation system aimed at enhancing subjective well-being (SWB). Despite the vast array of books available on the internet, people often struggle to find literature that aligns with their current emotional state. The system dynamically detects users' emotional states and recommends books tailored to their mood. It utilizes a content-based filtering algorithm to suggest top-rated books in real-time based on the user's current emotional state. For users with low mood, uplifting and inspirational books are recommended, while a mix of happy and sad books is suggested for happy users to acknowledge the nuanced interplay of emotions. Neutral-themed books are proposed to users with a neutral mood for balance. The system employs an emotion classification algorithm for accurate mood detection, analyzing book summaries to extract emotional tones. Users' emotional states are evaluated using a Likert scale, adjusting recommendations accordingly. The methodology involves data collection, preprocessing, emotion classification, and mood meter operation. Results demonstrate the system's effectiveness in providing tailored book recommendations, enriching users' reading experiences.

Book Recommendation System with Integrated Chatbot

In today’s digital era, the overwhelming volume of available literature necessitates a more personalized approach to book recommendations. Our research aims to address this need by pioneering a solution that integrates hybrid filtering methodologies and advanced chatbot technology driven by machine learning algorithms. By leveraging these cutting-edge techniques, we aspire to redefine the book discovery experience, providing tailored recommendations that resonate with individual preferences. Through the fusion of popularized, collaborative and content-based alongside the capabilities of machine learning models for natural language processing, our system not only enhances recommendation accuracy but also fosters real-time interaction, ultimately enhancing user satisfaction and engagement in the realm of literature. Key Words- Personalized book recommendations, Hybrid filtering methodologies, chatbot technology, machine learning algorithms, Collaborative filtering, Content-based filtering, Natural language processing, Recommendation accuracy.

Book recommendation system: reviewing different techniques and approaches

Book recommendation system: reviewing different techniques and approaches

A Novel Framework on Book-Recommendation System

With so many books available these days, effective book recommendation engines are crucial to pointing readers in the direction of choices that suit their interests. This paper introduces a prediction algorithm that uses user ratings to improve book recommendations. The program attempts to precisely forecast and rank the top 50 books for specific users by examining user-provided book ratings. The research methodology utilized in this study generates tailored recommendations by combining machine learning algorithms with collaborative filtering techniques. In order to provide predictions, collaborative filtering looks for trends in user behavior and preferences and compares users and books. To be more precise, the model finds books that people with similar preferences have liked by using similarity measures and user-item matrices. The effectiveness of the predictive model is evaluated using a sizable dataset of user-rated novels. The model is guaranteed to be resilient and adaptable to a large range of literary interests due to the dataset's broad scope in terms of genres, authors, and publishing years. Recall, accuracy, and precision are among the performance metrics used to assess how well the model can recommend books to readers based on their interests. The predictive model's ability to produce individualized book recommendations based on user ratings is demonstrated by the results. The model's prediction of the top 50 books is highly relevant and aligned with users' likes, which improves readers' browsing and selecting experience. The concept also exhibits scalability and flexibility, making it possible to accommodate growing book catalogs and changing customer preferences. All things considered, this work advances book recommendation systems by introducing a predictive model that uses user ratings to produce tailored recommendations. The approach improves reading enjoyment and encourages readers to become more deeply involved with books by making it easier for them to find interesting and engaging literary content..

Book Recommendation System

In the digital age, the vast availability of books poses a significant challenge for readers to discover titles that match their interests and preferences. This report explores the use of machine learning for a Book Recommendation System (BRS) to help readers discover books suited to their interests in the digital age. It will utilize advanced algorithms and techniques, including collaborative filtering, content-based filtering, and hybrid models, to offer personalized recommendations based on users' reading history and behavior. The project aims to tackle challenges like data sparsity and the cold-start problem while ensuring scalability. Evaluation will be based on standard metrics like precision, recall, accuracy, and novelty, with a focus on ethical considerations such as fairness and user privacy. Ultimately, the goal is to develop a robust and user-centric system that enhances the book discovery process across digital platforms. A Graphical User Interface is also developed to discover information and display the recommended books. Keywords—Machine Learning, Book Recommendation System, K-Nearest Neighbor Algorithm

Bookshelf: A Book Recommendation System Using Collaborative Filtering

In the huge amount of literature, finding the perfect book to please your literary thirst can be a task. With countless genres, authors, and styles to choose from, it's easy to be lost in a sea of literacy. Fortunately, we live where technology and data can come to our rescue, guiding us toward our next great read. In this paper, we present the algorithms used to create Bookshelf and how we tried several algorithms before using collaborative filtering. Next, we discussed how the whole system is designed. The objective of a book recommender system is to create a personalized and engaging experience for book lovers, by providing them with relevant and interesting book recommendations that match their unique preferences and interests. Overall, the objective of bookshelf is to create a win-win situation by providing users with personalized recommendations that enhance their reading experience. Key Words: Recommender System, Collaborative Filtering, Cosine Similarity.

Exploring and mitigating gender bias in book recommender systems with explicit feedback

Exploring and mitigating gender bias in book recommender systems with explicit feedback

Comparison of machine learning-based book comments sentiment analysis for constructing recommendation system

The exponential growth in the volume of books available, along with the proliferation of online platforms, has made it increasingly challenging for readers to find books tailored to their interests. This research paper aims to address this challenge by developing an effective book recommendation system based on user reviews and ratings, primarily drawn from Amazons dataset covering the period from May 1996 to July 2014. Using a K-Nearest Neighbors (KNN) algorithm and a Random Forest baseline model, the study focuses on comparative analyses in terms of Mean Squared Error (MSE) and computational costs. The KNN model outperformed the baseline model with a lower MSE of 0.15 compared to 0.38 and proved to be computationally less exacting. While the KNN model is currently the more tenable option for deployment, the paper posits that an ensemble approach may offer a more robust solution. Future work aims to include sentiment analysis, explore other recommendation algorithms, and make use of more advanced evaluation metrics. This study provides a foundation for the advancement of book recommendation systems, offering insights into their efficiency and effectiveness.

Eliminating bias: enhancing children's book recommendation using a hybrid model of graph convolutional networks and neural matrix factorization.

Managing user bias in large-scale user review data is a significant challenge in optimizing children's book recommendation systems. To tackle this issue, this study introduces a novel hybrid model that combines graph convolutional networks (GCN) based on bipartite graphs and neural matrix factorization (NMF). This model aims to enhance the precision and efficiency of children's book recommendations by accurately capturing user biases. In this model, the complex interactions between users and books are modeled as a bipartite graph, with the users' book ratings serving as the weights of the edges. Through GCN and NMF, we can delve into the structure of the graph and the behavioral patterns of users, more accurately identify and address user biases, and predict their future behaviors. Compared to traditional recommendation systems, our hybrid model excels in handling large-scale user review data. Experimental results confirm that our model has significantly improved in terms of recommendation accuracy and scalability, positively contributing to the advancement of children's book recommendation systems.

An automatic college library book recommendation system using optimized Hidden Markov based weighted fuzzy ranking model

An automated book recommendation system has becoming a necessary role in increasing the efficacy and user experience of college library. The number of books in the library is very large, and it is difficult for students to choose the appropriate book from each department efficiently. The manual selection of books is time-consuming, and an automatic library book recommendation system is highly required. Therefore, this research proposes a novel ranking-based hybrid recommendation system for assisting each student in different department to select required books with minimal time. Initially, pre-processing is done to label each department name, which can effectively reduce the time complexity. Then, weighting is provided for each book using a timestamp based on the days the book was issued between the date of issue and return. After, Pearson correlation coefficient (PCC) is computed to find the similarity between the department and the corresponding books. Finally, the Hidden Markov-assisted Chaotic Artificial Humming Bird with Discriminant Analysis and weighted fuzzy ranking (HMCAHB_DA-WFR) model is proposed to classify the books according to the department accurately based on the generated ranks and provides better recommendation to the students. The proposed method is implemented in PYTHON software, and a real-time dataset is utilized from Bhilai Institute of Technology, Durg. The proposed method obtains a better accuracy of 99.2%, Kappa of 97.6%, MAE of 0.074, NMCC of 98.7%, FPR of 0.0085% and FNR of 0.034%. The attained simulation results prove the efficiency of proposed method over other existing methods.

Diversity Balancing in Two-Stage Collaborative Filtering for Book Recommendation Systems

A book recommender system is a system used to provide relevant book recommendations for readers. One approach that is often used in recommender systems is Collaborative Filtering (CF). CF provides book recommendations based on books liked by other similar users. However, CF only provides recommendations for items that are popular, so items that are less popular will be difficult to recommend. Therefore, we propose a book recommendation system based on Two-stages CF using the Diversity Balancing method. Diversity Balancing method in CF is used to balance diversity in the recommendation results by replacing popular items with less popular relevant items. System accuracy is measured using precision and recall, while diversity is measured using personal diversity and aggregate diversity. The test results show that the accuracy of the proposed system increases with the increasing number of recommended items. meanwhile, the diversity of recommended items continues to decrease as more items are included in the recommendation list. In consideration of the trade-off between accuracy and diversity, our system achieves a recall score of 0.301, a precision score of 0.282, a PD score of 0.048, and an AD score of 0.095 with a recommendation list size of 8 items.

Improving Collaborative Filter Using BERT

With the increasing number of books published and the difficulty of obtaining appropriate research attention, the recommendation systems can increase the affordability and availability of these books. In this work, we expand our work to enhance the accuracy of book collaborative filtering by applying semantic similarity to book summaries, in addition to that addressing major problems of the current work by applying effective techniques to handle the scalability and sparsity problems. The proposed approach consists of three stages: preprocessing, building the system, and evaluation. The technologies used in the pre-processing stage included reduction and normalization. The construction system is divided into two phases: semantic similarity and recommendation. The semantic similarity is done by using BERT for sentence embedding and cosine similarity to calculate the similarity between sentences. During the recommendation phase by using CF based on KNN. In the evaluation stage, classification accuracy metrics had been used. The proposed approach improved the accuracy of the book recommendation system and increased the accuracy to 0.89 compared to previous works on a dataset of 271,000 book summaries. The proposed approach yielded better results due to avoiding problems in previous work, such as scalability and sparsity, by using BERT with CF based KNN. Filtering the data using BERT and the KNN algorithm in the CF added strength to the recommendation, which led to an increase in the accuracy rate.

Implementasi Metode Collaborative Filtering Based Untuk Sistem Rekomendasi Buku Fiksi

A book is the result of someone's work in the form of a collection of papers containing articles intended for publication. One of the benefits of books is that they can open people's horizons and can educate reason, thoughts and faith. Indonesia is ranked 60 out of 61 countries with reading interest problems causing low public interest in reading. One of the factors that can be taken into consideration for interest in reading in Indonesia is the difficulty in finding books to read because the many kinds of books in circulation make it difficult for readers to decide which books to read, besides that readers only want to read books with the best reputation. The purpose of creating a book recommendation system is to make it easier to find fiction-type books to read. The data used in this design are book data and rating data from Kaggle. This design uses one of the recommendation system methods, namely collaborative filtering. Collaborative filtering is a recommendation method that calculates similarity between items by users to make choices. The system will recommend 5 books according to the book title that the user will input.

Book Recommender System Using Matrix Factorization with Alternating Least Square Method

In this digital age, we are faced with countless choices of books. Finding books that match our interests and desires becomes a complex challenge. However, the existence of a book recommender system is useful to help provide the best decision-making experience that users can have. This research develops a book recommender system using Collaborative Filtering (CF) Matrix Factorization with Alternating Least Squares method which is compared with Singular Value Decomposition method to see an accurate recommender system. This research uses datasets from Goodreads in the form of book data and rating data. This research uses several evaluation metrics, namely RMSE and MAE for regression metrics and F1-Score and Precision for classification metrics. Based on the research that has been done, SVD gets a better accuracy value with an RMSE value of around 0.86822, for MAE values around 0.6903, for F1-Score values around 0.827923 and for Precision values around 0.568347. Meanwhile, the ALS algorithm gets an RMSE value of around 1.09320, for MAE value of around 0.86479, for F1-Score value of around 0.000304 and for Precision value of around 0.000596.

Chatbot-Based Book Recommender System Using Singular Value Decomposition

In the era of information overload, finding the right book that matches one's preferences and interests has become a challenging task for users as many online book provider service websites such as Amazon, Goodreads, and Gramedia provide books of various types and choices. Recommender systems can be used in addressing such issues, it works by filtering information that provides predictions and suggests the best product or service to the user. Currently, various book recommender systems have been developed, but the systems do not provide interaction between the user and the system. Therefore, we propose a recommender system built with a conversational approach so that it can interact with natural language. Recommender system built using matrix factorization method with Singular Value Decomposition (SVD) algorithm, SVD is proven to have advantages for handling large datasets, extracting features, reducing noise and dimensionality so as to speed up computation. We performed two types of evaluation on the system. First, we tested the prediction accuracy using Root Mean Squared Error (RMSE) and Mean Absolute Error (MAE) metrics. Second, we use questionnaires to measure user satisfaction levels. The evaluation of the system shows that the results of the prediction accuracy obtain an MAE value of 0.6481 and an RMSE value of 0.8287. Then, the accuracy performance of the system found that 83.2% of users get recommendations according to their interests. The user satisfaction with the whole system is 87.9%. The system built can provide a fairly good recommendation performance, and the chatbot can interact well with users based on the evaluation results obtained.

초등학생을 위한 LightFM 기반의 개인별 맞춤 도서 추천 엔진 개발 및 성능평가

Objectives In this study, a personalized book system is designed for elementary school students based on machine learning technology, and a hybrid recommendation engine is developed. In addition, the purpose is to verify the performance through performance analysis of the developed recommended engine. Methods To achieve the above purpose, a toaping reading service with 9,000 members was used. 5,979,479 reading and reading activities were targeted, and a book recommendation system was developed using LightFM. The ranking for the activities was applied by applying the weight of each activity to the processing of Korean and various reading activities were applied. Results Performance analysis of the developed system was conducted. Performance analysis was conducted on AUC Score, Accuracy, Precison, Recall, and F1-Score, and as a result, 99.9%, 95.1%, 92.5%, 86.3%, and 89.3%, respectively. It was confirmed that the recommended book reflects the student's interest. Conclusions Through the results of the performance analysis, it was confirmed that the books selected by individual students and the books recommended by the developed recommendation engine were similar. It was confirmed that for elementary school students, book recommendations according to each student's area of interest should be made in addition to book recommendations by experts or related organizations. In addition, in order to improve the completeness of the system developed through this study, additional analysis and reflection of big data on book loans by public institutions are needed, and various additional studies are needed to recommend customized books.

BOOK RECOMMENDATION SYSTEM USING PYTHON

An innovative book recommendation tool called BookWise was created to help readers get individualized book suggestions based on their unique preferences. Effective recommendation systems are now more necessary than ever to guide readers through the overwhelming amount of options given the exponential expansion of the digital book market. Advanced machine learning algorithms and methodsfor natural language processing are used by BookWise to analyze a variety of features of books, including genre, author, narrative, and style, as well as user information like reading habits, ratings, and history. These elements are combined by BookWise to produce precise recommendationsthat are pertinent to the preferences of each user. BookWise's ability to learn from and adjust to the evolving preferences of its users over time is at the heart of the program. The system uses collaborative filtering and content-based filtering approaches to continuously gather user feedback on suggested books and take user interactions into account. By doing this, it is able to make increasingly accurate and interesting recommendations to users. By implementing a user review and rating system, BookWise also encourages social involvement and a sense of community among its members. Users can communicate with people who share their interests in literature, discussreviews, and offertheirthoughts. This community-driven strategy improves the quality of suggestions while also enhancing the reading experience by encouraging interactions and relationships amongst book lovers. Furthermore, BookWise is created to be very user- friendly, giving cross-platform compatibility, including online and mobile applications, to make sure users can get their tailored recommendations wherever they are and wheneverthey want. The system also offers a simple and user-friendly design that makes it simple to navigate and explore books of all genres, new releases, and popular works. Through thorough user testing and review, BookWise's efficacy has been confirmed, proving its capacity to provide precise and pleasing recommendations that are in line with consssumers' interests. BookWise represents a significant advancement in the field of book recommendation systems by providing a potent tool to improve reader experiences, promote literary research in the digital age, and ease discovery. Keywords—Personalize book recommendation; recommendation system; clustering; machine learning

Artificial Intelligence Algorithms for Collaborative Book Recommender Systems

Book recommender systems provide personalized recommendations of books to users based on their previous searches or purchases. As online trading of books has become increasingly important in recent years, artificial intelligence (AI) algorithms are needed to recommend suitable books to users and encourage them to make purchasing decisions in the short and the long run. In this paper, we consider AI algorithms for so called collaborative book recommender systems, especially the matrix factorization algorithm using the stochastic gradient descent method and the book-based k-nearest-neighbor algorithm. We perform a comprehensive case study based on the Book-Crossing benchmark data set, and implement various variants of both AI algorithms to predict unknown book ratings and to recommend books to individual users based on the highest predicted ratings. This study aims to evaluate the quality of the implemented methods in recommending books by using selected evaluation metrics for AI algorithms.

Integrated Personalized Book Recommendation using Social Media Analysis

Today, most e-commerce sites use product-specific recommendation systems to better user experience. The algorithm used by such sites is - item-toitem collaborative filtering. This matches each user who has purchased and rated items to similar items and then combines those similar items into a recommendation list. The solution proposed in this paper is an integrated book recommendation system that maps the user’s highly rated books with books of a similar genre, maps the interactions of said user on social media to assess the kind of books one is interested in, and considers the collaborative filtering or association mapping between the items. In this model, authors used datasets for the same Goodreads book collection, Amazon and Goodreads reviews, transaction histories, and Twitter data. The proposed solution shall use a weighted measure, k-means clustering, and sentiment analysis. The collaborative filtering will be done using the Apriori mechanism to develop an integrated book recommendation list. The result is a list of 10 books that are recommended for a particular user. The proposed model met 80 percent of the user’s expected recommendations, whereas the simple collaborative model only met 60 percent of the user’s expectations. The collaborative model consisted majority of books by the same author or of a complete contrast genre as the method only considers the choice of other similar users and not similar books. So, the proposed integrated recommendation system is more accurate in its recommendations than a simple collaborative system. This model helps firms recommend the best possible book for book lovers. It also helps book lovers to find the best content as per their interests.