Recommender System Model Research Articles

Construction of Internet TV Industry Ecosystem Based on Data Mining Technology

While the Internet provides people with convenience, it also comes with security concerns. Users can more easily form groups, distort facts, and contribute to some sensitive topics on the internet. As a result, identifying and analyzing users’ online behaviors are critical. This paper creates a new Internet TV industry ecosystem using DM (data mining) technology. The recommendation system model is established based on data from users’ on-demand viewing behavior across the entire network, and the functions of various system modules and their coordination ability are described in detail. The evolution of users’ online time is examined, providing data to support and explain the prediction analysis of users’ click behavior and the analysis of users’ search intent. The type of web page that the user clicks on can reveal the user’s behavior tendency.

Developing a Location-Based Recommender System Using Collaborative Filtering Technique in the Tourism Industry

The rapid growth of new information and products in the virtual environment has made it time consuming to acquire relevant information and knowledge amidst a vast amount of information. Therefore, an intelligent system that can offer the most appropriate and desirable among the large amount of information and products by following the conditions and features selected by each user should be essentially efficient. Systems that perform this task are called recommendation systems. Given the volume of social network data, challenges such as short-term processing and increased accuracy of recommendations are discussed in this type of system. Hence, it can perform processes faster with less error and can be effective in improving the performance of social recommending systems in improving the classification and clustering of information with the help of collaboration filtering methods. This study first develops an innovative conceptual model of a social network-based tourism recommendation system using Flicker network data. This model is based on 9 key components. The comparison show that the proposed method has an accuracy of 0.3% and a lower error rate.

Design and Application of Handicraft Recommendation System Based on Improved Hybrid Algorithm

With the rapid development of online e-commerce, traditional collaborative filtering algorithms have the disadvantages of data set reduction and sparse matrix filling cannot meet the requirements of users. This paper takes handicrafts as an example to propose the design and application of handicraft recommendation system based on an improved hybrid algorithm. Based on the theory of e-commerce system, through the traditional collaborative filtering algorithm of users, the personalized e-commerce system of hybrid algorithm is designed and analyzed. The personalized e-commerce system based on hybrid algorithm is further proposed. The component model of the business recommendation system and the specific steps of the improved hybrid algorithm based on user information are given. Finally, an experimental analysis of the improved hybrid algorithm is carried out. The results show that the algorithm can effectively improve the effectiveness and exemption of recommending handicrafts. What’s more, it can reduce the user item ratings of candidate set and improve accuracy of the forecast recommendation.

A Survey of Recommendation Systems: Recommendation Models, Techniques, and Application Fields

This paper reviews the research trends that link the advanced technical aspects of recommendation systems that are used in various service areas and the business aspects of these services. First, for a reliable analysis of recommendation models for recommendation systems, data mining technology, and related research by application service, more than 135 top-ranking articles and top-tier conferences published in Google Scholar between 2010 and 2021 were collected and reviewed. Based on this, studies on recommendation system models and the technology used in recommendation systems were systematized, and research trends by year were analyzed. In addition, the application service fields where recommendation systems were used were classified, and research on the recommendation system model and recommendation technique used in each field was analyzed. Furthermore, vast amounts of application service-related data used by recommendation systems were collected from 2010 to 2021 without taking the journal ranking into consideration and reviewed along with various recommendation system studies, as well as applied service field industry data. As a result of this study, it was found that the flow and quantitative growth of various detailed studies of recommendation systems interact with the business growth of the actual applied service field. While providing a comprehensive summary of recommendation systems, this study provides insight to many researchers interested in recommendation systems through the analysis of its various technologies and trends in the service field to which recommendation systems are applied.

Sistem Rekomendasi Tugas Akhir Mahasiswa pada AMIK Indonesia untuk Mendukung Merdeka Belajar-Kampus Merdeka Menggunakan Metode Collaborative Filtering (CF)

The purpose of this study was to develop a model of a student's final project recommendation system by applying the Collaborative Filtering (CF) method. Agile Development Methods (ASD) is used in software development where this method is widely used for software development because it can produce system requirements data that is more descriptive and easy to implement into software. After going through the process of completing this research, the results of this study; 1) In knowing the topic or research theme in the final project, students with an expert system can be seen from the themes and sub-themes and valid calculations are carried out in helping students to find out the research title for the final project quickly, 2) By applying the Collaborative Filtering (CF) method ) in detecting research topics or themes at AMIK Indonesia, it can produce valid calculations that are the same as manual calculations so that the selection process can be carried out quickly and accurately, 3) In designing this application, it consists of the main menu page, a list of research topics, FAQs and about applications with using the programming language PHP, ReachJS and MySQL as data processing in the recommendation of the student's final project, and 4) The test is carried out using Mean Absolute Error (MAE), root-mean-square error (RMSE), Recall Value, and F-Measure. The results of the test evaluation show that the Collaborative Filtering (CF) method is quite accurate with the resulting recommendations.

A Community-Driven Deep Collaborative Approach for Recommender Systems

Recommender systems (RS) are increasingly leveraging the power of graphs to enhance accuracy. However, we stipulate that existing methods don’t take into consideration the inherent behavior of communities and the interaction between all the sub-groups of the network. In this work, we develop a Deep Graph-based Collaborative Filtering recommender system (DGCF), which incorporates the concept of community profiling and leverages the power of Graph Neural Networks. DGCF utilizes multiple graphs to exploit all types of information from the different user interactions. It extracts the overlapping communities from the homophily user-user graph and also integrates the high-order information from the user-item bipartite graph. We conduct experiments and evaluate the DGCF on the MovieLens datasets(ML-100K and ML-1M), and Douban dataset. Our experiments reveal significant improvements over a number of the latest deep learning models for recommender systems. Results also support that DGCF has the potential to render better recommendations as it extracts deep relationships using the community structure.

A Review-based Context-Aware Recommender Systems: Using Custom NER and Factorization Machines

Recommender Systems depend fundamentally on user feedback to provide recommendation. Classical Recom-menders are based only on historical data and also suffer from several problems linked to the lack of data such as sparsity. Users’ reviews represent a massive amount of valuable and rich knowledge information, but they are still ignored by most of current recommender systems. Information such as users’ preferences and contextual data could be extracted from reviews and integrated into Recommender Systems to provide more accurate recommendations. In this paper, we present a Context Aware Recommender System model, based on a Bidirectional Encoder Representations from Transformers (BERT) pretrained model to customize Named Entity Recognition (NER). The model allows to automatically extract contextual information from reviews then insert extracted data into a Contextual Machine Factorization to compte and predict ratings. Empirical results show that our model improves the quality of recommendation and outperforms existing Recommender Systems.

Iterative Collaborative Filtering for Sparse Matrix Estimation

Matrix estimation or completion has served as a canonical mathematical model for recommendation systems. More recently, it has emerged as a fundamental building block for data analysis as a first step to denoise the observations and predict missing values. Since the dawn of e-commerce, similarity-based collaborative filtering has been used as a heuristic for matrix etimation. At its core, it encodes typical human behavior: you ask your friends to recommend what you may like or dislike. Algorithmically, friends are similar “rows” or “columns” of the underlying matrix. The traditional heuristic for computing similarities between rows has costly requirements on the density of observed entries. In “Iterative Collaborative Filtering for Sparse Matrix Estimation” by Christian Borgs, Jennifer T. Chayes, Devavrat Shah, and Christina Lee Yu, the authors introduce an algorithm that computes similarities in sparse datasets by comparing expanded local neighborhoods in the associated data graph: in effect, you ask friends of your friends to recommend what you may like or dislike. This work provides bounds on the max entry-wise error of their estimate for low rank and approximately low rank matrices, which is stronger than the aggregate mean squared error bounds found in classical works. The algorithm is also interpretable, scalable, and amenable to distributed implementation.

A Review for Recommender System Models and Deep Learning

A Review for Recommender System Models and Deep Learning

E-Learning Recommendation System for Big Data Based on Cloud Computing

In educational institutions, E-learning has been known as a successful technology for enhancing performance, concentration, and thus providing higher academic success. Nevertheless, the conventional system for executing research work and selecting courses is a time-consuming and unexciting practice, that not only directly impacts the students ’ academic achievement but also impacts the learning experience of students. In addition to that, there is an enormous number of various kinds of data in the E-Learning domain both structured and unstructured, and the academic establishments attempt to manage and understand big complicated data sets. To fix this problem, this paper proposes a model of an E-learning recommendation system that will suggest and encourage the learner in choosing the courses according to their needs. This system used big data tools such as Hadoop and Spark to enhance data collection, storage, analysis, processing, optimization, and visualization, furthermore based on cloud computing infrastructure and especially Google cloud services.

A Correlation Based Recommendation System for Large Data Sets

Correlation determination brings out relationships in data that had not been seen before and it is imperative to successfully use the power of correlations for data mining. In this paper, we have used the concepts of correlations to cluster data, and merged it with recommendation algorithms. We have proposed two correlation clustering algorithms (RBACC and LGBACC), that are based on finding Spearman’s rank correlation coefficient among data points, and using dimensionality reduction approach (PCA) along with graph theory respectively, to produce high quality hierarchical clusters. Both these algorithms have been tested on real life data (New York yellow cabs dataset taken from http://www.nyc.gov), using distributed and parallel computing (Spark and R). They are found to be scalable and perform better than the existing hierarchical clustering algorithms. These two approaches have been used to replace similarity measures in recommendation algorithms and generate a correlation clustering based recommendation system model. We have combined the power of correlation analysis with that of prediction analysis to propose a better recommendation system. It is found that this model makes better quality recommendations as compared to the random recommendation model. This model has been validated using a real time, large data set (MovieLens dataset, taken from http://grouplens.org/datasets/movielens/latest). The results show that combining correlated points with the predictive power of recommendation algorithms, produce better quality recommendations which are faster to compute. LGBACC has approximately 25% better prediction capability but at the same time takes significantly more prediction time compared to RBACC.

A Hybrid Movie Recommender System and Rating Prediction Model

In the current era, a rapid increase in data volume produces redundant information on the internet. This predicts the appropriate items for users a great challenge in information systems. As a result, recommender systems have emerged in this decade to resolve such problems. Various e-commerce platforms such as Amazon and Netflix prefer using some decent systems to recommend their items to users. In literature, multiple methods such as matrix factorization and collaborative filtering exist and have been implemented for a long time, however recent studies show that some other approaches, especially using artificial neural networks, have promising improvements in this area of research. In this research, we propose a new hybrid recommender system that results in better performance. In the proposed system, the users are divided into two main categories, namely average users, and non-average users. Then, various machine learning and deep learning methods are applied within these categories to achieve better results. Some methods such as decision trees, support vector regression, and random forest are applied to the average users. On the other side, matrix factorization, collaborative filtering, and some deep learning methods are implemented for non-average users. This approach achieves better compared to the traditional methods.

Interpretable deep recommender system model for prediction of kinase inhibitor efficacy across cancer cell lines

Computational models for drug sensitivity prediction have the potential to significantly improve personalized cancer medicine. Drug sensitivity assays, combined with profiling of cancer cell lines and drugs become increasingly available for training such models. Multiple methods were proposed for predicting drug sensitivity from cancer cell line features, some in a multi-task fashion. So far, no such model leveraged drug inhibition profiles. Importantly, multi-task models require a tailored approach to model interpretability. In this work, we develop DEERS, a neural network recommender system for kinase inhibitor sensitivity prediction. The model utilizes molecular features of the cancer cell lines and kinase inhibition profiles of the drugs. DEERS incorporates two autoencoders to project cell line and drug features into 10-dimensional hidden representations and a feed-forward neural network to combine them into response prediction. We propose a novel interpretability approach, which in addition to the set of modeled features considers also the genes and processes outside of this set. Our approach outperforms simpler matrix factorization models, achieving R = 0.82 correlation between true and predicted response for the unseen cell lines. The interpretability analysis identifies 67 biological processes that drive the cell line sensitivity to particular compounds. Detailed case studies are shown for PHA-793887, XMD14-99 and Dabrafenib.

New Vector-Space Embeddings for Recommender Systems

In this work, we propose a novel recommender system model based on a technology commonly used in natural language processing called word vector embedding. In this technology, a word is represented by a vector that is embedded in an n-dimensional space. The distance between two vectors expresses the level of similarity/dissimilarity of their underlying words. Since item similarities and user similarities are the basis of designing a successful collaborative filtering, vector embedding seems to be a good candidate. As opposed to words, we propose a vector embedding approach for learning vectors for items and users. There have been very few recent applications of vector embeddings in recommender systems, but they have limitations in the type of formulations that are applicable. We propose a novel vector embedding that is versatile, in the sense that it is applicable for the prediction of ratings and for the recommendation of top items that are likely to appeal to users. It could also possibly take into account content-based features and demographic information. The approach is a simple relaxation algorithm that optimizes an objective function, defined based on target users’, items’ or joint user–item’s similarities in their respective vector spaces. The proposed approach is evaluated using real life datasets such as “MovieLens”, “ModCloth”, “Amazon: Magazine_Subscriptions” and “Online Retail”. The obtained results are compared with some of the leading benchmark methods, and they show a competitive performance.

Hybrid model for movie recommendation system using content K-nearest neighbors and restricted Boltzmann machine

<span>One of the most commonly used techniques in the recommendation framework is collaborative filtering (CF). It performs better with sufficient records of user rating but is not good in sparse data. Content-based filtering works well in the sparse dataset as it finds the similarity between movies by using attributes of the movies. RBM is an energy-based model serving as a backbone of deep learning and performs well in rating prediction. However, the rating prediction is not preferable by a single model. The hybrid model achieves better results by integrating the results of more than one model. This paper analyses the weighted hybrid CF system by integrating content K-nearest neighbors (KNN) with restricted Boltzmann machine (RBM). Movies are recommended to the active user in the proposed system by integrating the effects of both content-based and collaborative filtering. Model efficacy was tested with MovieLens benchmark datasets.</span>

Optimization of the Hybrid Movie Recommendation System Based on Weighted Classification and User Collaborative Filtering Algorithm

Aiming at the problem that the single model of the traditional recommendation system cannot accurately capture user preferences, this paper proposes a hybrid movie recommendation system and optimization method based on weighted classification and user collaborative filtering algorithm. The sparse linear model is used as the basic recommendation model, and the local recommendation model is trained based on user clustering, and the top-N personalized recommendation of movies is realized by fusion with the weighted classification model. According to the item category preference, the scoring matrix is converted into a low-dimensional, dense item category preference matrix, multiple cluster centers are obtained, the distance between the target user and each cluster center is calculated, and the target user is classified into the closest cluster. Finally, the collaborative filtering algorithm is used to predict the scores for the unrated items of the target user to form a recommendation list. The items are clustered through the item category preference, and the high-dimensional rating matrix is converted into a low-dimensional item category preference matrix, which further reduces the sparsity of the data. Experiments based on the Douban movie dataset verify that the recommendation algorithm proposed in this article solves the shortcomings of a single algorithm model to a certain extent and improves the recommendation effect.

Preventing Overfitting via Sample Reweighting for Recommender System Incremental Update (Student Abstract)

Incremental update of recommender system models using only newly arrived data may easily cause the model to overfit to the current data. To address this issue without relying on historical data, we propose a sample reweighting method based on prediction performance of previous model on current data. The proposed method effectively alleviates the problem of overfitting and improves the performance of incremental update.

Logistic Regression Algorithm-Based Product Recommendation System Model

It is very rare to see that the product development process is smoothly carried out with no trials and errors. In the process of producing new products, many difficulties are frequently occurring contrary to the expectation. In order to efficiently cope with such difficulties, it would be required to have the procedure for reflecting consumers’ needs by introducing and applying the recent Opinion Mining, Sentiment Analysis, and Logistic Regression to the product development process. This study used the linear regression and logistic regression models to predict a product with the highest purchase possibility, which was used for the newest marketing technique for the next best action or product recommendation, by analyzing the massive unstructured consumer data of portal sites and online markets, positiveness/negativeness, and predicting/analyzing the causes. Even though the concept itself of those models is simple, they are the most successful models in the logistic regression field. Some people may be opposed to call these models logistic regression. However, the concept itself is the same, and they are achieving such excellent results in the actual logistic regression. The logistic regression model is used for predicting a product with the highest purchase possibility in the newest marketing technique for the next best action or product recommendation. To conquer the new product market, the planning and development of new products are playing huge roles in the corporate performance and growth. The logistic regression-based product recommendation system model proposed by this thesis is the system for acquiring useful and accurate information to plan the best product. In the results of analyzing total 11,200 data sets of a specific product in portal sites and online markets, the customer satisfaction was 92% and there were some product defective issues in case of frequency analysis, so that it would be necessary to have the urgent improvement. According to the results of a survey on the use of the proposed model targeting the planners of products related to system operation, the items related to the system use satisfaction, system efficiency, and system effectiveness showed the satisfactory results higher than expected.

A Federated Learning Approach for Privacy Protection in Context-Aware Recommender Systems

Abstract Privacy protection is one of the key concerns of users in recommender system-based consumer markets. Popular recommendation frameworks such as collaborative filtering (CF) suffer from several privacy issues. Federated learning has emerged as an optimistic approach for collaborative and privacy-preserved learning. Users in a federated learning environment train a local model on a self-maintained item log and collaboratively train a global model by exchanging model parameters instead of personalized preferences. In this research, we proposed a federated learning-based privacy-preserving CF model for context-aware recommender systems that work with a user-defined collaboration protocol to ensure users’ privacy. Instead of crawling users’ personal information into a central server, the whole data are divided into two disjoint parts, i.e. user data and sharable item information. The inbuilt power of federated architecture ensures the users’ privacy concerns while providing considerably accurate recommendations. We evaluated the performance of the proposed algorithm with two publicly available datasets through both the prediction and ranking perspectives. Despite the federated cost and lack of open collaboration, the overall performance achieved through the proposed technique is comparable with popular recommendation models and satisfactory while providing significant privacy guarantees.

Regret Bounds and Regimes of Optimality for User-User and Item-Item Collaborative Filtering

We consider an online model for recommendation systems, with each user being recommended an item at each time-step and providing `like' or `dislike' feedback. Each user may be recommended a given item at most once. A latent variable model specifies the user preferences: both users and items are clustered into types. All users of a given type have identical preferences for the items, and similarly, items of a given type are either all liked or all disliked by a given user. We assume that the matrix encoding the preferences of each user type for each item type is randomly generated; in this way, the model captures structure in both the item and user spaces, the amount of structure depending on the number of each of the types. The measure of performance of the recommendation system is the expected number of disliked recommendations per user, defined as expected regret. We propose two algorithms inspired by user-user and item-item collaborative filtering (CF), modified to explicitly make exploratory recommendations, and prove performance guarantees in terms of their expected regret. For two regimes of model parameters, with structure only in item space or only in user space, we prove information-theoretic lower bounds on regret that match our upper bounds up to logarithmic factors. Our analysis elucidates system operating regimes in which existing CF algorithms are nearly optimal.