An exploratory data analysis on rating data using recommender system algorithms

Recommendation systems (RS) are software tools and methods designed to give recommendations to support customers in different decisions in terms of what items to buy, music to listen to, news to read, and so forth. Most recommender systems recommend items in terms of individual user likings and group recommender systems recommend items taking into consideration the likings and personalities of group members. To generate effective recommendations for a group, the system must satisfy, to the greatest extent possible, the individual interests of the group members. With the social networks, it is possible to recommend to a virtual group thus this study endeavors to develop a virtual group recommender system prototype using a model-based matrix factorization algorithm of collaborative filtering technique then popularity vote for virtual group. A publicly available dataset was used in this study. The results of the prototype showed the proposed collaborative filtering algorithm for prediction of user rating preferences demonstrated a good mean average error (MAE) of 0.70 and root mean square error (RMSE) of 0.89. Virtual groups of social networks user were then formed using the popularity vote algorithm and the results were plausible. This type of recommendation to a virtual group also enables members of the group to have something to talk about on the social network.KeywordsMean absolute error (MAE)Root mean squared error (RMSE)Recommendation system (RS)Collaborative-based filtering (CF)Content-based filtering (CBF)

Evaluation Of Recommendation System On A Movie Dataset

Recommender systems are used to generate meaningful recommendations to users based on their preferences, which will be determined following several approaches. This work targets Arab readers by providing accurate and reliable results that match their needs and desirability. Eventually, it will enhance the reading experience for any Arab readers. The main approach is to filter the recommendations, and this can be achieved either by Content-Based filtering or by Collaborative Filtering. The collaborative filtering techniques presented in this paper compute the similarity matrix between items and users' ratings, and then evaluate the recommendations for users. The techniques cover User-Based and Item-Based Collaborative Filtering, as well as Matrix Factorization through an SVD algorithm. A comparison between these techniques is presented in terms of the fitting and testing time, and accuracy. The KNN-based algorithms showed better performance than the matrix factorization method with respect to fitting and testing time. However, the matrix factorization (SVD) algorithm had the best results in terms of accuracy.

Collaborative and content-based filtering are the majorly used approaches towards implementing recommender systems which can successfully predict the item(s) to be recommended to users based upon their preferences. Each of the methods has its own advantages and disadvantages that are specific to the situation they are used in. The hybrid approach aims to combine both techniques in multiple ways to optimize results and overcome shortcomings. In this paper, we propose a hybrid approach based recommendation engine to improve accuracy. While content based recommendations are made on the similarity of items’ attributes, collaborative recommendations are based on similarity of users. Collaborative filtering is achieved by matrix factorization technique. One of the most effective algorithms for matrix factorization: Singular Value Decomposition (SVD) has been used to perform collaborative filtering and combined with content based model to predict the item ratings per user. The paper aims to achieve the final result with minimum possible errors as per user preferences and recommended items.

The necessity for sophisticated recommender systems in the movie recommendation sphere has become particularly pronounced, generating a more personalized movie recommendation due to people nowadays who like to watch movies online. Efficient recommender systems make use of advanced machine learning (ML) techniques in the pursuit of accurate and meaningful recommendations. This paper endeavours to give a comprehensive overview of technologies known as recommender systems, concentrating on ML methods found at the base. Different strategies have been applied in this work, which include collaborative filtering (CF), content-based filtering (CB), hybrid approaches, Generative AI and so on. The merits and demerits of each technique are listed and explained briefly. In addition, the actual application’s results are also presented in this paper. To evaluate the performance of the techniques, some of the important datasets that are used in evaluating recommender systems are also discussed along with measurement metrics to determine the effectiveness of technique. Example metrics used are Root Mean Square Error (RMSE), Mean Absolute Error (MAE) and so on. This paper synthesizes existing research to evaluate the advantages and limitations of diverse recommendation techniques, which aims to give suggestion on how to improve the design of movie recommendation systems so that the performance of the technique can be improved.

Introduction: Recommendation systems (RS) are pivotal in enhancing personalized user experiences, particularly in the educational domain, where tailored book recommendation systems (BRS) support individualized learning. Despite their importance, selecting an optimal recommendation algorithm remains challenging, especially in scenarios involving large and sparse datasets. Objectives: This study aims to evaluate the performance of four recommendation algorithms—Random Baseline, Popular, User-Based Collaborative Filtering (UBCF), and Singular Value Decomposition (SVD)—to identify their strengths and tradeoffs regarding accuracy and computational efficiency. The goal is to provide actionable insights for selecting appropriate algorithms for educational platforms and similar applications. Methods: The evaluation employed a dataset containing over 10,000 books, 53,424 users, and 981,756 ratings. The algorithms were assessed using Root Mean Square Error (RMSE) to measure predictive accuracy and training time to indicate computational efficiency. The analysis addressed challenges such as data sparsity and rating biases. Results: The results demonstrated that SVD achieved the highest accuracy (RMSE: 0.950), effectively uncovering latent relationships in sparse datasets. UBCF, with a slightly lower accuracy (RMSE: 1.020), offered a balance between accuracy and computational efficiency, making it suitable for real-time applications. Conversely, simpler algorithms like Random Baseline and Popular exhibited faster training times but significantly lower predictive accuracy, highlighting the limitations of non-personalized methods. Conclusions: This study underscores the tradeoffs between accuracy and efficiency among recommendation algorithms. While SVD is ideal for accuracy-driven applications, UBCF provides a practical alternative for scenarios requiring computational efficiency. The findings have substantial implications for educational platforms and e-commerce, where personalized recommendations enhance user satisfaction and engagement. Future research should focus on integrating deep learning models and expanding evaluation criteria, including user satisfaction and diversity, to further improve the performance of recommendation systems.

Recommender systems are useful tools to give personalized recommendations to users. One of the most popular techniques used in these systems is collaborative filtering. Recommender system algorithms get into trouble with data sparsity and scalability. These challenges cause lack of convergence in our algorithms. In this research, we propose a new method based on matrix factorization which alleviates data sparsity. We suggest a new method which can be performed as a preprocessing method for initial latent factor matrices of users and items. Initialized latent factors in matrix factorization lead to two advantages: (1) sparsity and scalability would be covered and (2) convergence of algorithms would be faster. We have shown that our method has improved the accuracy of optimization-based matrix factorization technique. Also it has increased the speed of matrix factorization convergence.

Purpose – Project ATHENA aims to develop an application to address information overload, primarily focused on Recommendation Systems (RSs) with the personalization and user experience design of a modern system. Method – Two machine learning (ML) algorithms were used: (1) TF-IDF for Content-based filtering (CBF); (2) Classification with Matrix Factorization- Singular Value Decomposition (SVD) applied with Collaborative filtering (CF) and mean (normalization) for prediction accuracy of the CF. Data sampling in academic Research and Development (R&D) of Philippine Council for Agriculture, Aquatic, and Natural Resources Research and Development (PCAARRD) e-Library and Project SARAI publications plus simulated data used as training sets to generate a recommendation of items that uses the three RS filtering (CF, CBF, and personalized version of item recommendations). Series of Testing and TAM performed and discussed. Results – Findings allow users to engage in online information and quickly evaluate retrieved items produced by the application. Compatibility-testing (CoT) shows the application is compatible with all major browsers and mobile-friendly. Performance-testing (PT) recommended v-parameter specs and TAM evaluations results indicate strongly associated with overall positive feedback, thoroughly enough to address the information-overload problem as the core of the paper. Conclusion – A modular architecture presented addressing the information overload, primarily focused on RSs with the personalization and design of modern systems. Developers utilized Two ML algorithms and prototyped a simplified version of the architecture. Series of testing (CoT and PT) and evaluations with TAM were performed and discussed. Project ATHENA added a UX feature design of a modern system. Recommendations – High-end hardware specs of v-Parameter are recommended, at least 8-cores of vCPU with 16GiB of memory and sufficient driver-type size to run the model and End-to-end jobs execution for continuously maintaining the application. Research Implications – Future Developers must use/integrate a large dataset. Other ML approaches can expand Hybrid-RS better. Implicit data and additional filtering methods may enhance the application in the future.

Recommender systems have become essential in the digital era to help users navigate overwhelming content. This study develops a book recommendation system using three collaborative filtering methods: user-based, item-based, and matrix factorization using singular value decomposition. We evaluate the system on a real-world dataset of 1,149,780 book ratings from 278,858 users across 271,360 books. A subset of 500 active users is used for experimental evaluation. The models are assessed using root mean square error and mean absolute error to measure rating prediction accuracy. The results show that the item-based collaborative filtering method achieves the best accuracy (root mean square error 7.362; mean absolute error 6.761), slightly outperforming the user-based approach (7.365; 6.809) and the matrix factorization method (7.643; 7.413). We analyze the results to understand the performance differences, noting the stability of item similarity as a key factor and the need for optimal tuning in the matrix factorization model. In conclusion, item-based collaborative filtering proved most effective for this context. This work provides insights into the comparative performance of foundational recommendation techniques and highlights practical considerations for improving book recommender systems.

Recommender system has a long history as a successful application in artificial intelligence. A growth in the number of products, which has been offered by different e-commerce platforms, leads to a technology which can help customers to choose and buy products. Collaborative filtering or recommender systems use a database about user preferences to predict additional topics or products a new user might like. This paper describes some algorithms designed for this task including cosine-based similarity algorithm and correlation-based similarity algorithm. The predictive accuracy of various methods in tourism recommender domains is compared. On the other hand, we have designed and implemented a recommender system in e-tourism in order to compare performance of these algorithms. Finally, we conclude that correlation based similarity algorithm acts better than Cosine based similarity algorithm.

The traditional matrix factorization algorithm has the problems of cold start, data sparsity and high predictive time complexity, which is not perfect in most recommendation systems. Especially in the sparse matrix problem, the recommendation accuracy cannot be guaranteed. This paper will be based on the matrix factorization algorithm modeling and create optimization algorithm, this method will be graded dataset preprocessing, the user and item's score matrix by embedding process at the same time and introducing BatchNorm sparse matrix algorithm for training normalization processing parameters do to speed up the convergence speed, increase the stability of the training, respectively add item bias that more able to show the user's true score, then the user matrix to join implicit feedback, the final score projections for the user. The optimization algorithm performs well in solving cold startup and data sparse problems of matrix factorization. The Results of the Movielens-1M experiment show that it has great advantages over traditional matrix factorization algorithms in terms of prediction accuracy, root mean square error and square absolute error.

Because of their mathematical backgrounds and coherent structures, Artificial Intelligent-based methods and techniques are often used to find solutions for different types of problems encountered. In the related context, nature-inspired algorithms are also important for providing more accurate solutions. Because of their nature-based, flexible process structures, the related algorithms can be applied to different types of problems. At this point, recommendation systems are one of the related problem areas where nature-inspired algorithms can be used to get better results. In the literature there are many research studies that are based on using nature-inspired algorithms within recommendation systems. This chapter aims to discuss usage of some newly developed nature-inspired algorithms in typical recommendation systems. In this aim, features and functions of some new nature-inspired algorithms will be explained first. Later, using the related algorithms in recommendation systems will be discussed. Following that, there will be a discussion on future of nature-inspired algorithms and also their roles in the recommendation approach or system-based applications.

Cooking your own food at home is a good step toward reducing fast food consumption. Fast food increases the risk of dangerous diseases. The diversity of recipe information available on the internet makes it difficult to choose recipes that match user preferences. Mobile technology can help with this by recommending recipes that better suit users' eating habits. This makes the transition to a healthier diet easier. Therefore, in this study, a recommendation system was developed that can recommend recipes based on the preferences of Android users. Two main recommendation methods are used in this study: content-based filtering and collaborative filtering. Using cosine similarity, a content-based recommendation system identifies the proximity between a recipe for food and its related context. The history of user comments on recipes serves as implicit feedback for the collaborative recommendation algorithm. This eliminates the need for explicit evaluations, such as ratings. This recommendation system generates recommendations in the form of the top ten food recipes with an evaluation matrix, referred to as NDCG@k and Hit-Ratio@k. The tests revealed that a content-based filtering technique may produce helpful recommendations, with the highest similarity score of 0.41 for the entry "chocolate cake that you can easily make at home." Meanwhile, in the collaborative filtering method using the Neural Collaborative Filtering (NCF) approach, the system shows consistent performance improvements, with the MAP@10 value increasing from 0.705 to 0.767 and the NDCG@10 from 0.78 to 0.83 after 10 training epochs. Keywords: Recommendation systems; content-based filtering; neural collaborative filtering; cosine similarity; implicit feedback

Recommender systems are powerful systems that give added value to business and corporation. They are relatively recent technology and they will only keep improving in the future. The most widely used algorithms for recommender systems are categorized into the traditional recommender and deep-based recommender system. The traditional recommendation algorithm suffers from sparse data that significantly degrades recommendation accuracy. The hybrid approaches are attempts to tackle recommendation challenges. This paper addresses the integration of deep learning into traditional recommendation approaches especially, Collaborative Filtering (CF) algorithms to get a significant accurate prediction. It proposes a hybrid deep CF recommender model called ConvSVD++ that tightly integrates Convolution Neural Network (CNN) and Singular Value Decomposition (SVD++). The proposed model incorporates items’ content, implicit user’s feedback along with explicit item-user interaction to enhance prediction accuracy and handle sparsity problem. The proposed model is evaluated and all baseline models based on Movielens- 1M datasets. The results are evaluated using Root Mean Squared Error (RMSE) metric and it is concluded that the proposed model ConvSVD++ outperforms the baselines models. Accordingly, it is concluded that integrating CNN with SVD++ algorithm improves rating prediction accuracy.

When we buy items in online stores, it is common to face recommended items that meet our interest. These recommendation system help users not only to find out related items, but also find new things that may interest users. Recommendation system has been widely studied and various models has been suggested such as, collaborative filtering and content-based filtering. Though collaborative filtering shows good performance for predicting users preference, there are some conditions where collaborative filtering cannot be applied. Sparsity in user data causes problems in comparing users. Systems which are newly starting or companies having small number of users are also hard to apply collaborative filtering. Content-based filtering should be used to support this conditions, but content-based filtering has some drawbacks and weakness which are tendency of recommending similar items, and keeping history of a user makes recommendation simple and not able to follow up users preference changes. To overcome this drawbacks and limitations, we suggest weighted window assisted user history based recommendation system, which captures user’s purchase patterns and applies them to window weight adjustment. The system is capable of following current preference of a user, removing useless recommendation and suggesting items which cannot be simply found by users. To examine the performance under user and data sparsity environment, we applied data from start-up trading company. Through the experiments, we evaluate the operation of the proposed recommendation system.