Collaborative Filtering Techniques Research Articles

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..

Personalised Outfit Recommendation System Using Deep Learning

Abstract: Fashion engineering presents a significant challenge in efficiently coordinating outfits to suit personal preferences. In this study, we introduce a Fashion Coordination Assistant featuring a hybrid recommendation system. Our approach combines content-based and collaborative filtering techniques, utilizing Singular Value Decomposition (SVD) and Non-negative Matrix Factorization (NMF) algorithms. By thoroughly analyzing clothing characteristics and user behavior, we address the technical complexities of fashion coordination. Leveraging the latent features identified by SVD and the non-linear relationships captured by NMF, our system generates tailored outfit suggestions based on individual preferences and style profiles. Extensive testing validates the system's ability to provide diverse and efficient recommendations. By integrating innovative recommendation methods, the Fashion Coordination Assistant adapts to evolving fashion trends and user preferences, enhancing its practical utility in real-world scenarios

Design and Application of Intelligent Assisted Learning System for Higher Education Students

The Intelligent Assisted Learning System for Higher Education Students is an innovative educational platform designed to enhance the learning experience for college and university students. Leveraging artificial intelligence, machine learning, and data analytics, this system provides personalized learning pathways, adaptive tutoring, and real-time feedback to students based on their learning styles, preferences, and performance. By analyzing student interactions, engagement levels, and academic progress, the system dynamically adjusts course materials, assignments, and assessments to optimize learning outcomes. This paper explores the integration of Cooperative User Knowledge Modelling (CUKN) into Intelligent Assisted Learning Systems (IALS) tailored for higher education. Through a systematic investigation of CUKN's role within IALS, we elucidate its potential to enhance personalized learning experiences for students. By leveraging user data, collaborative filtering techniques, and adaptive learning strategies, CUKN empowers IALS to dynamically tailor content recommendations, learning paths, and collaborative tools to individual student needs, preferences, and proficiency levels. Simulation results suggest significant improvements in student engagement and performance, with metrics such as a 30% increase in learning efficacy and an 85% satisfaction rate with feedback mechanisms. While promising, challenges remain in refining algorithms and ensuring seamless integration into educational practices.

A Collaborative Filtering-Based Recommender Systems approach for Multifarious applications

Recommender systems are crucial in today's IT landscape, enhancing user experiences in various industries. Collaborative filtering (CF) is a key approach, using historical interaction data to predict user preferences. This paper presents CF advancements, with a focus on latent factor models that represent users and items in a compact feature space. It also addresses sparsity issues with techniques like neighborhood-based approaches and content augmentation. Contextual CF, which incorporates temporal and contextual dynamics, is explored through methods like matrix factorization with side information and session-based recommendation. Evaluation metrics such as MAE and RMSE, along with novel ranking-based metrics, provide a comprehensive assessment of recommendation quality. In this paper we outline cutting-edge CF techniques, showcasing their mechanisms and applications and were able to achieve accurate recommendations of almost 90% using MAE and RMSE metrics. By integrating latent factor modeling, sparsity mitigation, contextual enrichment, and advanced evaluation, it paves the way for the next generation of personalized recommendation systems, tailored to meet evolving demands in modern information environments.

UniRecSys: A unified framework for personalized, group, package, and package-to-group recommendations

Recommender systems aim to enhance the overall user experience by providing tailored recommendations for a variety of products and services. These systems help users make more informed decisions, leading to greater user engagement with the platform. However, the implementation of these systems largely depends on the context, which can vary from recommending an item or package to a user or a group. This requires careful exploration of several models during the deployment, as there is no comprehensive and unified approach that deals with recommendations at different levels. Furthermore, these individual models must be closely attuned to their generated recommendations depending on the context to prevent significant variation in their generated recommendations. In this paper, we propose a novel unified recommendation framework that addresses all four recommendation tasks, namely, personalized, group, package, and package-to-group recommendation, filling the gap in the current research landscape. The proposed framework can be integrated with most of the traditional matrix factorization-based collaborative filtering (CF) models. This research underscores the significance of including group and package information while learning latent representations of users and items for personalized recommendations. These components help in exploiting a rich latent representation of the user/item by enforcing them to align closely with their corresponding group/package representation. We consider two prominent CF techniques, namely Regularized Matrix Factorization and Maximum Margin Matrix factorization, as the baseline models and demonstrate their customization to various recommendation tasks. Experimental results on two publicly available datasets are reported, comparing them to other baseline approaches for various recommendation tasks.

Devoloping a Robust and Accurate Food Recommendation System Using a Hybrid Filtering Approach

With the increase of fame for online food platforms as well as a broad range of culinary choices, there has been a need for stronger and more correct food recommendation systems that can help users in discovering new and fascinating foods that are tailored to their individual tastes. This paper presents an innovative design of constructing a recommendation system by utilizing both content-based approach and collaborative filtering techniques. Our system applies machine learning algorithms to examine user preferences as well as dish attributes with personalized recommendations based on it thereby increasing satisfaction levels and overall engagement rates. The experimental results we provide herein demonstrate the efficacy and accuracy of our hybrid filtering method and prove its ability to transform how individuals find pleasure in eating.

Hybrid collaborative filtering using matrix factorization and XGBoost for movie recommendation

Nowadays, e-commerce platforms, such as Amazon, Flipkart, Netflix and YouTube, extensively use recommender systems (RS) techniques. Collaborative filtering (CF) is used widely among all RS techniques. A CF analyzes the user’s preference from past data, like ratings, and then suggests actual items to the intended user. The existing techniques compute the similarity between users/items and predict the ratings. However, most of them indicate the user’s preference for the items using a single technique, which may produce poor results. This paper proposes a hybrid CF technique to enhance the movie recommendation (HCFMR). The HCFMR consists of two modules. The first module finds the prediction score with the help of matrix factorization (MF) and passes the prediction score as input to the prediction algorithm, i.e., extreme gradient boosting (XGBoost). The second module generates handcrafted features, such as similar users and movies, along with the user, item and global average. Finally, these features are supplied to the XGBoost to predict the rating score of the movie and recommend the topmost movie to the user. We conduct various simulations on real-world datasets to verify the effectiveness of the proposed technique against the baseline techniques. The exploratory outcomes signify that the HCFMR technique outperforms the baselines and provides a better prediction on the benchmark datasets.

Deployment of Advanced and Intelligent Logistics Vehicles with Enhanced Tracking and Security Features

This study focuses on the implementation of modern and intelligent logistics vehicles equipped with advanced tracking and security features. In response to the evolving landscape of logistics management, the proposed system integrates cutting-edge technologies to enhance efficiency and ensure the security of the entire logistics process. The core component of this implementation is the incorporation of state-of-the-art tracking mechanisms, enabling real-time monitoring of vehicle locations and movements. Furthermore, the system addresses the paramount concern of security by introducing advanced security measures. Through the utilization of sophisticated tracking technologies and security protocols, the proposed logistics vehicles aim to safeguard both customer and provider data. The implementation includes the integration of QR code concepts, creating a binary image system that conceals sensitive information and ensures access only to authorized users. In addition to tracking and security, the study delves into the realm of information mining, employing techniques such as classification, clustering, and recommendation to extract meaningful patterns from vast datasets. Collaborative filtering techniques are incorporated to enhance customer experience by recommending services based on user preferences and historical data. This abstract encapsulates the comprehensive approach of deploying modern logistics vehicles, emphasizing their intelligence through advanced tracking, robust security measures, and data-driven insights. The proposed system aims to revolutionize logistics management, providing a seamless and secure experience for both customers and service providers in the dynamic logistics landscape.

Time-Aware Based Recommendation System using Gower’s Coefficients: Enhancing Personalized Recommendation

To give users personalised recommendations, the collaborative filtering technique is widely used. In this technique, similarity computation plays a crucial role. Once the similarities among users or items are determined, the system can predict how a user might rate or interact with items that they have not yet encountered. The traditional approaches or measures mainly considered the user-item historical ratings to compute the similarity, while user preferences may change with time. Considering this, the objective of this research is to create an efficient recommendation system that utilized the temporal data. For this, few time decay functions, i.e., exponential, linear, logistic, and power applied to the ratings to give more weightage to the most recent ratings. Since, Gower’s coefficient is suitable for handling the missing data, it is applied to calculate the similarity and compared its results with popular traditional similarity measures i.e., Cosine and Pearson correlation coefficient. Experimental findings on the ML-100k dataset in terms of Root Mean Squared Error (RMSE) and Mean Average Error (MAE) demonstrate that performance of RS improved when we applied a power function on ratings. With comparison to most recent methods PIP, NHSM, RJaccard etc., proposed approach gives almost 10% better results in MAE and 5% better results in RMSE comparison.

Data augmentation and refinement for recommender system: A semi-supervised approach using maximum margin matrix factorization

Collaborative filtering (CF) has become a popular method for developing recommender systems (RSs) where ratings of a user for new items are predicted based on her past preferences and available preference information of other users. Despite the popularity of CF-based methods, their performance is often greatly limited by the sparsity of observed entries. In this study, we explore the data augmentation and refinement aspects of Maximum Margin Matrix Factorization (MMMF), a widely accepted CF technique for rating predictions, which has not been investigated before. We exploit the inherent characteristics of MMMF to assess the confidence level of algorithm’s prediction of individual rating and propose a semi-supervised approach for rating augmentation based on self-training. We hypothesize that any CF algorithm’s predictions with low confidence are due to some deficiency in the training data and hence, the performance of the algorithm can be improved by adopting a systematic data augmentation strategy. We iteratively use some of the ratings predicted with high confidence to augment the training data and remove low-confidence entries through a refinement process. By repeating this process, the system learns to improve prediction accuracy. Our method is experimentally evaluated on several state-of-the-art CF algorithms and leads to informative rating augmentation, improving the performance of the baseline approaches.

Single User-Item Profile Matrix in Recommendations System

Recommendation systems play a vital role in assisting users across various domains, including movies, music, books, and products, by providing personalized and relevant item suggestions. One popular approach employed in recommendation systems is the utilization of a utility matrix. This.0 matrix captures user-item interactions, with each cell representing a user's rating or preference for a particular item. By analyzing the utility matrix, recommendation systems can uncover patterns and resemblance between items and users, enabling accurate predictions and personalized recommendations. Collaborative filtering techniques, such as user-based and item-based approaches, are commonly applied to leverage the utility matrix. These techniques exploit collective user preferences similarities to suggest relevant items to users. Furthermore, the incorporation of additional data, such as item attributes or contextual information, and the adoption of hybrid approaches can further enhance recommendation system performance and effectiveness. By harnessing utility matrices and collaborative filtering, recommendation systems deliver tailored recommendations that enrich users' experiences and facilitate the discovery of items aligned with their individual preferences.
 Single User-Item Profile Matrix (SUIPM) is an algorithm which predicts missing feature value like ratings, scores, rankings etc. It uses linear process to predict the missing feature value (like, ratings, scores etc.) of each user within a user cluster or a group of user clusters according to the user’s activity and the preferences. Single User-Item Profile Matrix (SUIPM) algorithm predicts much faster than the Utility Matrix due to its low time complexity. The SUIPM algorithm mainly focuses on the improvement and the optimization of the prediction quality of the Utility Matrix in the recommendations system.

Analysis of media content recommendation in the new media era considering scene clustering algorithm

Abstract With the advent of the new media era, we face the problem of information overload every day, based on which this paper proposes a scenario-conscious clustering algorithm for recommending media content. The improved K-means algorithm is used to cluster the media content, an initial cluster center is randomly selected, and the remaining initial cluster centers are obtained by executing the improved algorithm to reduce the number of iterations and avoid neighboring situations. The clustering algorithm is then compared with content-based recommendation techniques, KNN recommendation algorithm, and collaborative filtering recommendation algorithm in terms of accuracy, recall, MAE value, and execution time. The clustering algorithm is significantly better than the other algorithms regarding accuracy and recall. The accuracy of the clustering algorithm is 0.4 when the recommendation sequence is 30, which is 0.4 higher than the collaborative filtering technique, 0.1 higher than the KNN, and 0.12 higher than the content recommendation method. In terms of MAE value, the clustering algorithm outperforms the other algorithms when the number of nearest neighbors is selected to be above 20. In terms of execution time, the longer the amount of data, the more obvious the advantage of the clustering algorithm. Therefore, the applicability and reliability of the model proposed in this paper for media content recommendation are verified in terms of accuracy, recall and execution time, which meet the design requirements.

A novel model based collaborative filtering recommender system via truncated ULV decomposition

Collaborative filtering is a technique that takes into account the common characteristics of users and items in recommender systems. Matrix decompositions are one of the most used techniques in collaborative filtering based recommendation systems. Singular Value Decomposition (SVD) and Non-negative Matrix Factorization (NMF) based approaches are widely used. Although they are quite good at dealing with the scalability problem, their complexities are high. In this study, the Truncated-ULV decomposition (T-ULVD) technique was used as an alternative technique to improve the accuracy and quality of recommendations. The proposed method has been tested with Movielens 100 k, Movielens 1 M, Filmtrust, and Netflix datasets, which are widely used in recommender system researches. In order to assess the performance of the proposed model, standart metrics (MAE, RMSE, precision, recall, and F1 score) were used. It is seen that while progress was achieved in all experiments with the T-ULVD compared to the NMF, very close or better results were obtained compared to the SVD. Moreover, this study may guide T-ULVD based future studies on solving the cold-start problem and reducing the sparsity in collaborative filtering based recommender systems.

Interpretable Machine Learning for Personalized Medical Recommendations: A LIME-Based Approach.

Chronic diseases are increasingly major threats to older persons, seriously affecting their physical health and well-being. Hospitals have accumulated a wealth of health-related data, including patients' test reports, treatment histories, and diagnostic records, to better understand patients' health, safety, and disease progression. Extracting relevant information from this data enables physicians to provide personalized patient-treatment recommendations. While collaborative filtering techniques and classical algorithms such as naive Bayes, logistic regression, and decision trees have had notable success in health-recommendation systems, most current systems primarily inform users of their likely preferences without providing explanations. This paper proposes an approach of deep learning with a local interpretable model-agnostic explanations (LIME)-based interpretable recommendation system to solve this problem. Specifically, we apply the proposed approach to two chronic diseases common in older adults: heart disease and diabetes. After data preprocessing, we use six deep-learning algorithms to form interpretations. In the heart-disease data set, the actual model recommendation of multi-layer perceptron and gradient-boosting algorithm differs from the local model's recommendation of LIME, which can be used as its approximate prediction. From the feature importance of these two algorithms, it can be seen that the CholCheck, GenHith, and HighBP features are the most important for predicting heart disease. In the diabetes data set, the actual model predictions of the multi-layer perceptron and logistic-regression algorithm were little different from the local model's prediction of LIME, which can be used as its approximate recommendation. Moreover, from the feature importance of the two algorithms, it can be seen that the three features of glucose, BMI, and age were the most important for predicting heart disease. Next, LIME is used to determine the importance of each feature that affected the results of the calculated model. Subsequently, we present the contribution coefficients of these features to the final recommendation. By analyzing the impact of different patient characteristics on the recommendations, our proposed system elucidates the underlying reasons behind these recommendations and enhances patient trust. This approach has important implications for medical recommendation systems and encourages informed decision-making in healthcare.

The new e-learning adaptation technique based on learner’s learning style and motivation

E-learning has increased in popularity, especially during the COVID-19, due to its numerous advantages that allow learners to study anywhere and anytime. Therefore, recommending a list of the most appropriate learning objects for learners according to their specific needs is a great challenge for adaptive e-learning systems. In an e-learning environment, the optimum adaptive e-learning system is one that can adapt dynamically to the profile of each learner. Within that particular context, various approaches were proposed. In this article, we propose a new adaptation technique based on learner’s learning style and motivation score by using collaborative filtering technique, constrained Pearson correlation coefficient, adjusted cosine measure, and K-nearest neighbor algorithms. The proposed approach is focused on how to develop and construct an effective customized pedagogical learning scenario for learning resources, and improve the accuracy of the adaptation by choosing the most suitable learning objects for learners. Therefore, we used the dataset MovieLens100K containing 943 learners and 1,682 learning objects. Additionally, a few experiments have been conducted to validate the performance of our technique. The results indicate that taking into account the learner’s learning style and motivation score can completely satisfy the customized needs of learners and improves the quality of learning.

Facilitating innovation in the API economy: Privacy-enhanced and novelty-aware API recommendation for enterprises

Web APIs provide enterprises with a new way of driving innovations of new technology with limited resources. API recommendations greatly alleviate the selection burdens of enterprises in identifying potential useful APIs to meet their business demands. However, these approaches disregard the privacy leakage risk in cross-platform collaboration and the popularity bias in recommendation. To address these issues, first, we introduce MinHash, an instance of locality-sensitive hashing, into a collaborative filtering technique and propose a novel, privacy-enhanced, API recommendation approach. Second, we present a simulation algorithm to analyze the popularity bias in API recommendation. Third, we mitigate popularity bias by improving the novelty of recommendation results with an adaptive reweighting mechanism. Last, comprehensive experiments are conducted on a real-world dataset collected from ProgrammableWeb. Experimental results show that our proposed approach can effectively preserve usage data privacy and mitigate popularity bias at a minimum cost in accuracy.

INTELLIGENT MOVIE RECOMMENDER FRAMEWORK BASED ON CONTENT-BASED & COLLABORATIVE FILTERING ASSISTED WITH SENTIMENT ANALYSIS

Recommendation systems for rating movies and forming opinion have grown exponentially in popularity, they make it very convenient for consumers to select films that suit their tastes. However, traditional recommendation systems often rely solely on user ratings or reviews, which may not accurately reflect the user's true feelings about a movie. To address this issue, sentiment analysis has been proposed as a more reliable method for capturing emotional information about movies. In this research paper, we propose a novel movie recommendation system that combines sentiment analysis with collaborative filtering and content-based methods. Our system is designed to provide accurate and timely recommendations to mobile users based on their preferences, reviews, and emotions. We evaluate the performance of our system using real-world data and demonstrate its effectiveness in improving the accuracy and timeliness of movie recommendations. In this research paper, we propose a comparative study of three popular movie recommendation techniques: content-based filtering, collaborative filtering, and sentiment analysis. We aim to evaluate the effectiveness of each approach in providing accurate and personalized movie recommendations. Work done has utilized MovieLens dataset for this study, which is a popular benchmark dataset for assessing movie recommendation systems. The dataset includes almost 100,000 ratings, ranging from 1 to 5, from 943 individuals on 1,682 films. Each rating has a timestamp, a special user ID, and a movie ID. The collection also contains details about each movie's genre. To prepare the dataset for our experiments, we first performed some data cleaning and pre-processing. We removed any duplicate ratings, and any movies or users with a low number of ratings were also removed. We also performed some feature engineering to extract relevant features from the raw data, such as movie genres, user demographics, and movie release dates. Our tests on the Movielens dataset show that the system we've suggested works well. Precision, recall, and F1-score were some of the measures we used to assess the system's performance. We discovered that it performed better than conventional recommendation systems that just use content-based or collaborative filtering techniques. A better user experience was produced with the use of sentiment analysis, which improved the accuracy and promptness of suggestions. Algorithm designed achieves a precision of 0.875, Recall of 0.0435, F1 Score of 0.0828, Accuracy of 0.9889, RMSE of 0.407, and F measure of 0.1813.

Course Recommendation System based on Natural Language Processing

Abstract—Computers are actually capable of understand hu- man languages thanks to the era referred to as NLP (Natural Language Processing). Deep grammatical and semantic analysis frequently makes use of words as the primary unit. The principle goal of NLP is regularly word segmentation. This undertaking can be used to deal with the sensible problem of widespread structural variations between conventional and multi-modal envi- ronments and diverse statistics modalities. The challenge presents the basics of the multi-modal function extraction approach that uses deep gaining knowledge of. A advice device primarily based on content material is also contributed by the undertaking. Based on the person’s previous behaviour or express feedback, it recommends additional items which might be just like what they already like the usage of object functions. A course recommenda-tion gadget, in a nutshell, is a device that suggests the subsequent piece of content based on what got already seen and loved. Advice structures are utilized by services like Spotify, Netflix, and Youtube to signify the subsequent movie or track you need to watch primarily based on what you’ve got already watched or heard. Primarily based at the filtering records, the advice gadget foresees gadgets which can realise and provide excessive- potential content that has been chosen by the consumer. Primarily based at the customers’ various searches, a recommendation machine become evolved to indicate courses to them. Users now have an easier time finding the proper guides based on their searches thanks to this machine. To determine availability, the system employs the TF-IDF algorithm and content material-based totally filtering. Suggestions for customers who use the collaborative filtering technique had been researched in earlier studies. Index Terms—Recommender System, Feature Extraction, Nat-ural Language Processing, Collaborative Filtering

Explaining the Performance of Collaborative Filtering Methods with Optimal Data Characteristics

The performance of a Collaborative Filtering (CF) method is based on the properties of a User-Item Rating Matrix (URM). And the properties or Rating Data Characteristics (RDC) of a URM are constantly changing. Recent studies significantly explained the variation in the performances of CF methods resulted due to the change in URM using six or more RDC. Here, we found that the significant proportion of variation in the performances of different CF techniques can be accounted to two RDC only. The two RDC are the number of ratings per user or Information per User (IpU) and the number of ratings per item or Information per Item (IpI). And the performances of CF algorithms are quadratic to IpU (or IpI) for a square URM. The findings of this study are based on seven well-established CF methods and three popular public recommender datasets: 1M MovieLens, 25M MovieLens, and Yahoo! Music Rating datasets.

A Comparative Analysis of Collaborative Filtering Similarity Measurements for Recommendation Systems

Collaborative Filtering (CF) is a widely used technique in recommendation systems to suggest items to users based on their previous interactions with the system. CF involves finding correlations between the preferences of different users and using those correlations to provide recommendations. This technique can be divided into user-based and item-based CF, both of which utilize similarity metrics to generate recommendations. Content-based filtering is another commonly used recommendation technique that analyzes the attributes of items to suggest similar items. To enhance the accuracy of recommendation systems, hybrid algorithms that combine CF and content-based filtering techniques have been developed. These hybrid systems leverage the strengths of both approaches to provide more accurate and personalized recommendations. In conclusion, collaborative filtering is an essential technique in recommendation systems, and the use of various similarity metrics and hybrid techniques can enhance the quality of recommendations.