User-based Collaborative Filtering Research Articles

Research on the application of bilateral collaborative algorithm in hotel tourism consumption preference recommendation

SummaryThe continuous expansion of the domestic tourism market has driven the development of the hotel related industry. It is a key focus of the hotel industry to provide accurate supporting services to tourists based on their preferences. However, the current recommendation model mainly considers tourist factors and lacks consideration for hotel factors. In order to address the concerns of recommendation services, a bilateral collaborative recommendation model is proposed, which first analyzes tourist feature data through comprehensive similarity calculation. Firstly, the comprehensive similarity calculation is used to analyze tourist feature data, and the K‐means Clustering Algorithm (K‐means) is introduced to improve the clustering effect between data. And to better distinguish tourist preferences, the Gale Shapley (G‐S) bilateral matching model is introduced to achieve preference matching of consumer features. In the Mean Absolute Error (MAE) analysis of the model, the User‐based Collaborative Filtering (UBCF) model performs the worst. When the k value is 90, the minimum MAE value is 0.7896. The Item‐based Collaborative Filtering (IBCF) model is 0.7776, with the best performance being the improved G‐S model of 0.7325. In the performance testing of three models' preference recommendations, the improved G‐S model achieves an accuracy of over 90% in all three types of feature data recommendations, with the lowest accuracy of 62.5% for UBCF and 71.3% for IBCF. It can be seen that the proposed improved G‐S model can accurately identify tourist and hotel preference data and accurately recommend corresponding projects. The research content provides important technical references for the digital transformation and development of the tourism industry.

Point of Interest (POI) Recommendation System using Implicit Feedback Based on K-Means+ Clustering and User-Based Collaborative Filtering

Recommendation system always involves huge volumes of data, therefore it causes the scalability issues that do not only increase the processing time but also reduce the accuracy. In addition, the type of data used also greatly affects the result of the recommendations. In the recommendation system, there are two common types of data namely implicit (binary) rating and explicit (scalar) rating. Binary rating produces lower accuracy when it is not handled with the properly. Thus, optimized K-Means+ clustering and user-based collaborative filtering are proposed in this research. The K-Means clustering is optimized by selecting the K value using the Davies-Bouldin Index (DBI) method. The experimental result shows that the optimization of the K values produces better clustering than Elbow Method. The K-Means+ and User-Based Collaborative Filtering (UBCF) produce precision of 8.6% and f-measure of 7.2%, respectively. The proposed method was compared to DBSCAN algorithm with UBCF, and had better accuracy of 1% increase in precision value. This result proves that K-Means+ with UBCF can handle implicit feedback datasets and improve precision.

Mining and Application of Digital Health Elements in Higher Education Student Management and Education

Abstract This study explores the mining and application of digital health elements in higher education student management and education, refining student user profiles through data mining techniques and applying them to student management and education to improve the accuracy and effectiveness of education management. The basic process of data mining, including data cleaning, integration, selection, transformation, mining, pattern evaluation and knowledge representation, was first carried out in the study. Then, a clustering recommendation algorithm based on user characteristics was designed to construct a clustering model of user interest preferences by calculating the distance between user attributes and filling the rating matrix using rating time and item type. Then, the study constructed a student user profiling system and analyzed techniques such as fuzzy C-mean clustering, association rule algorithm and user-based collaborative filtering. The study results show that applying digital health elements in student management and education helps identify potential association patterns of students’ mental health problems. For example, in the W vocational school case study, the association rule algorithm found that the sense of learning stress and compulsion were the most frequent combinations of psychological problems among students, with support levels of 0.7451 and 0.6518, respectively. In addition, the study evaluated the effects of educational management interventions on the mental health of higher vocational students, and found that, after adopting student user profiles for educational management interventions, the experimental class students’ mental health scores for each variable were 0.602 to 1.113 points higher than those of the control class, which significantly improved the students’ psychological quality.

Deep learning methods used in movie recommendation systems

As the amount of internet movie data grows rapidly, traditional movie recommendation systems face increasing challenges. They typically rely on statistical algorithms such as item-based or user-based collaborative filtering. However, these algorithms struggle to handle large-scale data and often fail to capture the complexity and contextual information of user behavior. Therefore, deep learning techniques have been widely applied to movie recommendation systems. This paper reviews movie recommendation algorithms based on traditional statistical models and introduces three main deep learning techniques: Artificial Neural Networks (ANN), Convolutional Neural Networks (CNN), and Recurrent Neural Networks (RNN). ANN can extract features at different levels of users and movies; CNN can capture features of movie posters and movie data to recommend similar movies; RNN can consider user historical behavior and contextual information to better understand user interests and demands. The application of these deep learning techniques can enhance the accuracy and user experience of movie recommendation systems. This paper also demonstrates the advantages and disadvantages of these models and their specific application methods in movie recommendation systems, and points out the direction for further development and improvement of deep learning models in this field.

A Hybrid Deep Ranking Weighted Multi-Hashing Recommender System

In countries where there is a low availability of resources for language, businesses face the challenge of overcoming language barriers to reach their customers. One possible solution is to use collaborative filtering-based recommendation systems in their native languages. These systems employ algorithms that understand the customers’ preferences and suggest products or services in their native language. Collaborative filtering (CF) is a popular recommendation technique that simulates word-of-mouth phenomena. However, the accuracy of a CF recommendation can be affected by sparse data. In this research paper, we present a novel hybrid weighted multi-deep ranking supervised hashing (HWMDRH) approach. Our method leverages both user-based and item-based CF by merging the item-based deep ranking weighted multi-hash recommender system prediction with the user-based deep ranking weighted multi-hash recommender system prediction to generate Top-N prediction. We conducted extensive experiments on the MovieLens 1M dataset, and our results show that the proposed HWMDRH model outperforms existing models and achieves state-of-the-art performance across recall, precision, RMSE, and F1-score metrics.

AT-GCN: A DDoS attack path tracing system based on attack traceability knowledge base and GCN

The rapid development of the Internet provides sufficient attack entrances for massive malicious terminals, which makes it extremely challenging to effectively trace the path of DDoS attacks. Traditional solutions mainly trace distributed denial of service (DDoS) attacks by adding tags to data packet headers or querying logs, which increases the cost of tagging. There are also some works that implement attack tracking based on the network-wide perspective and centralized control of SDN, but these methods are difficult to deploy on a large scale. In order to solve this problem, we propose a DDoS attack path tracing system (AT-GCN) based on attack traceability knowledge base and graph convolutional network (GCN). We first propose the construction process of the attack traceability knowledge base, and design the intra-domain attack graph and the traceability algorithm recommendation graph to solve the problems of DDoS attack path traceability and optimal traceability solution recommendation. On this basis, we propose a GCN-based intra-domain attack traceability scheme, and design a subgraph sampling algorithm Tracing-Sample adapted to intra-domain DDoS attack traceability, aiming to efficiently use the graph structure in the knowledge base to reproduce DDoS attack paths. Additionally, we recommend the traceability algorithm based on user-based collaborative filtering (UBCF), and dynamically recommend the best traceability algorithm according to the different requirements of administrators for traceability performance. Compared with other GCN algorithms, the results show that the recall of the AT-GCN system is increased by 7.3% on average, and the FPR is reduced by 5.7% on average at the expense of the memory usage rate. Under different scale topologies, the recall of the AT-GCN system can be stabilized at 95%.

CFNCM: Collaborative filtering neighborhood-based model for predicting miRNA-disease associations

MicroRNAs have a significant role in the emergence of various human disorders. Consequently, it is essential to understand the existing interactions between miRNAs and diseases, as this will help scientists better study and comprehend the diseases’ biological mechanisms. Findings can be employed as biomarkers or drug targets to advance the detection, diagnosis, and treatment of complex human disorders by foretelling possible disease-related miRNAs. This study proposed a computational model for predicting potential miRNA-disease associations called the Collaborative Filtering Neighborhood-based Classification Model (CFNCM), in light of the shortcomings of conventional and biological experiments, which are expensive and time-consuming. The model generated integrated miRNA and disease similarity matrices using the validated associations and miRNA and disease similarity information and used them as the input features for CFNCM. To produce class labels, we first determined the association scores for brand-new pairs using user-based collaborative filtering. With zero as the threshold, the associations with scores >0 were labelled 1, indicating a potential positive association, otherwise, it is marked as 0. Then, we developed classification models using various machine-learning algorithms. By comparison, we discovered that the support vector machine (SVM) produced the best AUC of 0.96 with 10-fold cross-validation through the GridSearchCV technique for identifying optimal parameter values. In addition, the models were evaluated and verified by analyzing the top 50 breast and lung neoplasms-related miRNAs, of which 46 and 47 associations were verified in two authoritative databases, dbDEMC and miR2Disease.

DaGzang: a synthetic data generator for cross-domain recommendation services.

Research on cross-domain recommendation systems (CDRS) has shown efficiency by leveraging the overlapping associations between domains in order to generate more encompassing user models and better recommendations. Nonetheless, if there is no dataset belonging to a specific domain, it is a challenge to generate recommendations in CDRS. In addition, finding these overlapping associations in the real world is generally tricky, and it makes its application to actual services hard. Considering these issues, this study aims to present a synthetic data generation platform (called DaGzang) for cross-domain recommendation systems. The DaGzang platform works according to the complete loop, and it consists of the following three steps: (i) detecting the overlap association (data distribution pattern) between the real-world datasets, (ii) generating synthetic datasets based on these overlap associations, and (iii) evaluating the quality of the generated synthetic datasets. The real-world datasets in our experiments were collected from Amazon's e-commercial website. To validate the usefulness of the synthetic datasets generated from DaGzang, we embed these datasets into our cross-domain recommender system, called DakGalBi. We then evaluate the recommendations generated from DakGalBi with collaborative filtering (CF) algorithms, user-based CF, and item-based CF. Mean absolute error (MAE) and root mean square error (RMSE) metrics are measured to evaluate the performance of collaborative filtering (CF) CDRS. In particular, the highest performance of the three recommendation methods is user-based CF when using 10 synthetic datasets generated from DaGzang (0.437 at MAE and 0.465 at RMSE).

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.

Towards personal learning environment by enhancing adaptive access to digital library using ontology-supported collaborative filtering

PurposeThe purpose of this paper is to provide adaptive access to learning resources in the digital library.Design/methodology/approachA novel method using ontology-based multi-attribute collaborative filtering is proposed. Digital libraries are those which are fully automated and all resources are in digital form and access to the information available is provided to a remote user as well as a conventional user electronically. To satisfy users' information needs, a humongous amount of newly created information is published electronically in digital libraries. While search applications are improving, it is still difficult for the majority of users to find relevant information. For better service, the framework should also be able to adapt queries to search domains and target learners.FindingsThis paper improves the accuracy and efficiency of predicting and recommending personalized learning resources in digital libraries. To facilitate a personalized digital learning environment, the authors propose a novel method using ontology-supported collaborative filtering (CF) recommendation system. The objective is to provide adaptive access to learning resources in the digital library. The proposed model is based on user-based CF which suggests learning resources for students based on their course registration, preferences for topics and digital libraries. Using ontological framework knowledge for semantic similarity and considering multiple attributes apart from learners' preferences for the learning resources improve the accuracy of the proposed model.Research limitations/implicationsThe results of this work majorly rely on the developed ontology. More experiments are to be conducted with other domain ontologies.Practical implicationsThe proposed approach is integrated into Nucleus, a Learning Management System (https://nucleus.amcspsgtech.in). The results are of interest to learners, academicians, researchers and developers of digital libraries. This work also provides insights into the ontology for e-learning to improve personalized learning environments.Originality/valueThis paper computes learner similarity and learning resources similarity based on ontological knowledge, feedback and ratings on the learning resources. The predictions for the target learner are calculated and top N learning resources are generated by the recommendation engine using CF.

Movie Recommendation System Based on Traditional Recommendation Algorithm and CNN Model

As streaming services have expanded in recent years, an excellent recommendation algorithm, as one of the core technologies in movie service, brings huge benefits. Although the current application research of recommendation algorithms is mature, most systems or products usually rely on only one main algorithm. This makes it difficult for the system to overcome the shortcomings of various algorithms and cannot benefit from the combination of multiple recommendation algorithms. In addition, the widely used recommendation models are found to be unable to extract the finer features of users and movies, and the calculation time is very long. Meanwhile, the recommendation results are inaccurate, and they are not user-friendly. In this research, we design and construct a system to recommend movies which is consisted by a model based on a convolutional neural network consisting. By extracting the features of users and movies, we can calculate the direct similarity of different users' movies and then predict movie ratings to give recommendations. When extracting features of users and movies, we refer to traditional algorithms based on content and content. Through the "cosine similarity" and "user movie score matrix", the Item-Based Collaborative Filtering and User-Based Collaborative Filtering can be well implemented. To sum up, our movie recommendation system is based on Convolutional Neural Network (CNN) model and the performance of the system is improved by adopting multiple recommendation algorithms such as content-based recommendation and system filtering. We efficiently trained the neural network and finally built a movie recommendation system with a faster operation speed, although some parts are not perfect.

Multimodal Movie Recommendation System Using Deep Learning

Recommendation systems, the best way to deal with information overload, are widely utilized to provide users with personalized content and services with high efficiency. Many recommendation algorithms have been researched and deployed extensively in various e-commerce applications, including the movie streaming services over the last decade. However, sparse data cold-start problems are often encountered in many movie recommendation systems. In this paper, we reported a personalized multimodal movie recommendation system based on multimodal data analysis and deep learning. The real-world MovieLens datasets were selected to test the effectiveness of our new recommendation algorithm. With the input information, the hidden features of the movies and the users were mined using deep learning to build a deep-learning network algorithm model for training to further predict movie scores. With a learning rate of 0.001, the root mean squared error (RMSE) scores achieved 0.9908 and 0.9096 for test sets of MovieLens 100 K and 1 M datasets, respectively. The scoring prediction results show improved accuracy after incorporating the potential features and connections in multimodal data with deep-learning technology. Compared with the traditional collaborative filtering algorithms, such as user-based collaborative filtering (User-CF), item-based content-based filtering (Item-CF), and singular-value decomposition (SVD) approaches, the multimodal movie recommendation system using deep learning could provide better personalized recommendation results. Meanwhile, the sparse data problem was alleviated to a certain degree. We suggest that the recommendation system can be improved through the combination of the deep-learning technology and the multimodal data analysis.

A comparative analysis of different recommender systems for university major and career domain guidance

Academic advising is inhibited at most of the high schools to help students identify appropriate academic pathways. The choice of a career domain is significantly influenced by the complexity of life and the volatility of the labor market. Thus, high school students feel confused during the shift period from high school to university, especially with the enormous amounts of data available on the Web. In this paper, an extensive comparative study is conducted to investigate five approaches of recommender systems for university study field and career domain guidance. A novel ontology is constructed to include all the needed information for this purpose. The developed approaches considered user-based and item-based collaborative filtering, demographic-based recommendation, knowledge base supported by case-based reasoning, ontology, as well as different hybridizations of them. A case study on Lebanese high school students is analyzed to evaluate the effectiveness and efficiency of the implemented approaches. The experimental results indicate that the knowledge-based hybrid recommender system, combined with the user-based collaborative filtering and braced with case-based reasoning as well as ontology, generated 98% of similar cases, 95% of them are personalized based on the interests of the high school students. The average usefulness feedback and satisfaction level of the students concerning this proposed hybrid approach reached 95% and 92.5% respectively, which could be a solution to similar problems, regardless of the application domain. Besides, the constructed ontology could be reused in other systems in the educational domain.

Enhancing item-based collaborative filtering by users’ similarities injection and low-quality data handling

With the increasing amount of the commercial items (movies, music, books, cars, etc.) produced each day by companies, it becomes very difficult for customers to find the suitable products satisfying their needs. Generally, Recommendation Systems (RSs) were used to fit this necessary requirement by solving the problem of information overload, specially on the web. Indeed, RS are designed to provide relevant resources to a client using certain information about users and resources. To the best of our knowledge, RS remains providing modest performances in many domains. In this context, we proposed a new model named CSWMC that combines two different techniques: item-based and user-based Collaborative Filtering. In fact, our proposed algorithm starts with the estimation of the suitable number of the user’s neighbors that offers to the Recommender System the optimal efficiency. Then, the system integrates this knowledge about users in the ‘Mean Centered’ aggregation method. Also, we proposed a simple method for handling the cold start and data sparsity problems that used mean value of the training datasets. The proposed models were validated through an experimental study on three standards datasets and compared with six well-known models. The obtained results demonstrated that our proposed model (in its two versions: with and without cold start handling) outperforms all the other models in terms of three evaluation metrics: RMSE, MAE and R2.

Recommender System Based on Collaborative Filtering for Personalized Dietary Advice: A Cross-Sectional Analysis of the ELSA-Brasil Study.

This study aimed to predict dietary recommendations and compare the performance of algorithms based on collaborative filtering for making predictions of personalized dietary recommendations. We analyzed the baseline cross-sectional data (2008-2010) of 12,667 participants of the Brazilian Longitudinal Study of Adult Health (ELSA-Brasil). The participants were public employees of teaching and research institutions, aged 35-74 years, and 59% female. A semiquantitative Food Frequency Questionnaire (FFQ) was used for dietary assessment. The predictions of dietary recommendations were based on two machine learning (ML) algorithms-user-based collaborative filtering (UBCF) and item-based collaborative filtering (IBCF). The ML algorithms had similar precision (88-91%). The error metrics were lower for UBCF than for IBCF: with a root mean square error (RMSE) of 1.49 vs. 1.67 and a mean square error (MSE) of 2.21 vs. 2.78. Although all food groups were used as input in the system, the items eligible as recommendations included whole cereals, tubers and roots, beans and other legumes, oilseeds, fruits, vegetables, white meats and fish, and low-fat dairy products and milk. The algorithms' performances were similar in making predictions for dietary recommendations. The models presented can provide support for health professionals in interventions that promote healthier habits and improve adherence to this personalized dietary advice.

Item Selection With Collaborative Filtering in On-The-Fly Multistage Adaptive Testing

An important design feature in the implementation of both computerized adaptive testing and multistage adaptive testing is the use of an appropriate method for item selection. The item selection method is expected to select the most optimal items depending on the examinees’ ability level while considering other design features (e.g., item exposure and item bank utilization). This study introduced collaborative filtering (CF) as a new method for item selection in the on-the-fly assembled multistage adaptive testing framework. The user-based CF (UBCF) and item-based CF (IBCF) methods were compared to the maximum Fisher information method based on the accuracy of ability estimation, item exposure rates, and item bank utilization under different test conditions (e.g., item bank size, test length, and the sparseness of training data). The simulation results indicated that the UBCF method outperformed the traditional item selection methods regarding measurement accuracy. Also, the IBCF method showed the most superior performance in terms of item bank utilization. Limitations of the current study and the directions for future research are discussed.

A hybrid recommendation technique using topic embedding for rating prediction and to handle cold-start problem

Recommender systems aim to estimate item ratings and recommend items based on the users’ interests. The traditional recommender systems generally consider user–item rating information for rating prediction, but they suffer from various limitations, such as data sparsity, black-box recommendation, and cold-start problems. As a result, researchers have proposed amalgamating contextual information with rating data to provide effective recommendations. Although user-generated data in the form of reviews are a rich source of contextual information, they are rarely utilized in recommender algorithms. This study presents a hybrid recommendation technique, called RecTE, using rating data and topic embedding, which is an amalgamation of word embedding and topic modeling techniques. The novelty of RecTE lies in predicting item ratings using topic embeddings learned by incorporating local and global contextual information and integrating them with user-based collaborative filtering. RecTE is empirically evaluated over three real-world datasets – YelpNYC, YelpZip and TripAdvisor. This technique performs significantly better in comparison to nine baselines and five state-of-the-art recommendation techniques. On empirical analysis, we found that incorporating topic embedding in RecTE makes it capable of performing significantly better and handle cold-start problems effectively in comparison to the existing recommendation approaches.

Rating-Based Recommender System Based on Textual Reviews Using IoT Smart Devices

Recommender system (RS) is a unique type of information clarification system that anticipates the user's evaluation of items from a large pool based on the expectations of a single stakeholder. The proposed system is highly useful for getting expected meaning suggestions and guidance for choosing the proper product using artificial intelligence and IoT (Internet of Things) such as chatbot. The current proposed technique makes it easier for stakeholders to make context-based decisions that are optimal rather than reactive, such as which product to buy, news classification based on high filtering views, highly recommended wanted music to choose, and desired product to choose. Recommendation systems are a critical tool for obtaining verified information and making accurate decisions. As a result, operational efficiency would skyrocket, and the risk to the company that uses a recommender system would plummet. This proposed solution can be used in a variety of applications such as commercial hotels OYO and other hotels, hospitals (GYAN), public administrative applications banks HDFC, and ICICI to address potential questions on the spot using intelligence computing as a recommendation system. The existing RS is considering a few factors such as buying records, classification or clustering items, and user's geographic location. Collaborative filtering algorithms (CFAs) are much more common approaches for cooperating to mesh the respective documents they retrieved from the historical data. CFAs are distinguished in plenty of features that are uncommon from other algorithms. In this existing system classification, precision and efficiency and error rate are statistical measurements that need to be enhanced according to the current need to fit for global requirements. The proposed work deals with enhancing accuracy levels of text reviews with the recommender system while interacting by the numerous users for their domains. The authors implemented the recommender system using a user-based CF method and presented the significance of collaborative filtering on the movie domain with a recommender system. This whole experiment has been implanted using the RapidMiner Java-based tool. Results have been compared with existing algorithms to differentiate the efficiency of the current proposed approach.

Point of Interest (POI) Recommendation System using Implicit Feedback Based on K-Means+ Clustering and User-Based Collaborative Filtering

Recommendation system always involves huge volumes of data, therefore it causes the scalability issues that do not only increase the processing time but also reduce the accuracy. In addition, the type of data used also greatly affects the result of the recommendations. In the recommendation system, there are two common types of data namely implicit (binary) rating and explicit (scalar) rating. Binary rating produces lower accuracy when it is not handled with the properly. Thus, optimized K-Means+ clustering and user-based collaborative filtering are proposed in this research. The K-Means clustering is optimized by selecting the K value using the Davies-Bouldin Index (DBI) method. The experimental result shows that the optimization of the K values produces better clustering than Elbow Method. The K-Means+ and User-Based Collaborative Filtering (UBCF) produce precision of 8.6% and f-measure of 7.2%, respectively. The proposed method was compared to DBSCAN algorithm with UBCF, and had better accuracy of 1% increase in precision value. This result proves that K-Means+ with UBCF can handle implicit feedback datasets and improve precision.

Developing Self-Advocacy Skills through Machine Learning Education: The Case of Ad Recommendation on Facebook

Facebook users interact with algorithms every day. These algorithms can perpetuate harm via incongruent targeted ads, echo chambers, or "rabbit hole" recommendations. Education around the machine learning (ML) behind Facebook (FB) can help users to point out algorithmic bias and harm, and advocate for themselves effectively when things go wrong. One algorithm that FB users interact with regularly is User-Based Collaborative Filtering (UB-CF) which provides the basis for ad recommendation. We contribute a novel research approach for teaching users about a commonly used algorithm in machine learning in real-world context -- an instructive web application using real examples built from the user's own FB data on ad interests. The instruction also prompts users to reflect on their interactions with ML systems, specifically Facebook. In a between-subjects design, we tested both Data Science Novices and Experts on the efficacy of the UB-CF instruction. Taking care to highlight the voices of marginalized users, we use the application as a prompt for surfacing potential harms perpetuated by FB ad recommendations, and qualitatively analyze themes of harm and proposed solutions provided by users themselves. The instruction increased comprehension of UB-CF for both groups, and we show that comprehension is associated with mentioning the mechanisms of the algorithm more in advocacy statements, a crucial component of a successful argument. We provide recommendations for increased algorithmic transparency on social media and for including marginalized voices in the conversation of algorithmic harm that are of interest both to social media researchers and ML educators.