Collaborative Filtering Research Articles

Hybrid self-attention BiLSTM and incentive learning-based collaborative filtering for e-commerce recommendation systems

Hybrid self-attention BiLSTM and incentive learning-based collaborative filtering for e-commerce recommendation systems

Application of artificial intelligence sensor and visual image technology in the analysis of hydrophilic space landscape characteristics

With the continuous development of intelligent sensor technology and artificial intelligence, the application of artificial intelligence feature recognition at the level of intelligent sensor information acquisition to urban landscape analysis has become a current research trend and hot spot, but also brings us new research directions and challenges. In this paper, the multi-sensor collaborative filtering algorithm is optimized, and a method based on evidence theory is proposed to deal with the fuzzy and unclear information generated in the process of multi-sensor collaboration. Then, the analysis and recognition of multi-sensor fusion information using AI technology are analyzed in detail. This paper discusses the problems in multi-sensor information fusion and related solutions. Combined with the key nodes of 3D object AI feature recognition, multi-sensor collaborative Dempster Shafer evidence theory and 3D convolutional neural network waterfront space landscape feature recognition sub-model are constructed, and the waterfront space landscape recognition analysis model is tested and analyzed. The results show that the multi-sensor information collection Kalman filtering fusion algorithm effectively realizes the recognition of landscape feature information and the filtering of irrelevant interference information combined with an artificial intelligence algorithm to build a landscape feature recognition model. Among all kinds of landscape recognition, the recognition effect of beach inflow is the best, and the recognition accuracy, recall, and F1 value are all above 95. However, the identification effect of hydrophilic plank roads and underwater breakwater is relatively poor. The recognition accuracy of the hydrophilic platform is the lowest at 83.16 % among the eight landscape types, and the recall rate of underwater breakwater is the lowest at 79.96 % among the eight landscape types. In general, the multi-sensor Kalman filtering algorithm information fusion model designed in this paper has higher recognition accuracy and better classification performance for the collection and classification of the entire landscape dataset. The work of this paper provides a new idea and direction for the design and application of waterfront landscape identification and analysis systems based on intelligent sensors and artificial intelligence technology and lays a certain foundation for the intelligent design of urban waterfront landscapes.

An interval-valued matrix factorization based trust-aware collaborative filtering algorithm for recommendation systems

In existing trust-aware collaborative filtering algorithms, each trust relationship between two users is usually represented by a real number, but such a number is neither sufficient to reflect the quantity of the trust relationship existing in the user’s mind nor easy to be given. This leads to the inaccuracy of the trust relationship and poor final recommendations. To solve this problem, we propose an approach to deduce interval-valued trust relationships from the given real-valued trust relationships, which enables the new trust relationships to optimally reflect the true trust relationships existing in users’ minds. The coming problem we face is how to fuse the interval-valued trust relationships and the real-valued ratings. Though most existing trust-aware collaborative filtering algorithms use matrix factorization to fuse the real-valued data, they are not capable of fusing interval-valued trust relationships and real-valued ratings. The reason is that the arithmetic operations on intervals and arithmetic operations on real numbers are different. Therefore, we proposed a novel interval-valued matrix factorization approach. After that, an interval-valued matrix factorization based trust-aware collaborative filtering (IMF_TCF) algorithm is designed. The experiments carried out with open datasets indicate that IMF_TCF achieves the best recommendation performance compared with the state-of-the-art algorithms.

CF-MGAN: Collaborative filtering with metadata-aware generative adversarial networks for top-N recommendation

Generative Adversarial Networks (GANs) have several advantages over latent variable and autoregressive models, such as directly computing the input data distribution, fast sampling, and best samples generated. Furthermore, the metadata information of users and items plays a crucial role in effectively learning the user-item latent representations. Existing neural network approaches are deterministic, incapable of accounting for user-item latent representations, and the rating prediction process is calculative rather than generative. However, only some papers have utilized GANs for information retrieval tasks. However, they have several limitations, such as the inability to abstract data effectively, the limitation of the model's ability to learn accurate latent representations, and the absence of metadata information. To address these issues, we propose a GAN-based recommendation framework named Collaborative Filtering with Metadata-awareGenerative Adversarial Networks (CF-MGAN) to learn complex rating distribution for collaborative filtering (CF) tasks. We use a negative item generator for the implicit user purchase vector to prevent GAN from generating biased ratings. We exploited metadata information to overcome data sparsity issues. We use Variational Autoencoder to abstract user-item metadata features, a regularizer for GAN-generated rating. Extensive experiments on datasets reveal the effectiveness of the proposed model, showing an improvement in the top-N recommendation task.

Music recommendation systems in music information retrieval: Leveraging machine learning and data mining techniques

Music Information Retrieval (MIR) has become a pivotal area of research with the rise of digital music platforms, enabling personalized music recommendations to enhance user experience. This paper explores the integration of machine learning and data mining techniques in music recommendation systems. We discuss user-based and item-based collaborative filtering, matrix factorization methods like Singular Value Decomposition (SVD) and Alternating Least Squares (ALS), and content-based filtering that incorporates audio feature analysis, metadata, and lyrics analysis. Additionally, we delve into hybrid recommendation systems, combining collaborative and content-based approaches using advanced models such as neural networks and hybrid autoencoders. Our finding show that, hybrid systems provide the most accurate and personalize recommendations, albeit requiring significant computational resources. Practical applications from platforms likes Spotify and Pandora illustrate the effectiveness of these approaches in real-world settings.

Leveraging ensemble clustering for privacy-preserving data fusion: Analysis of big social-media data in tourism

Discovering knowledge from social media becomes a trend in many domains such as tourism, where users' feedback and rating are the basis of recommendation systems. In this context, cluster analysis has been a major tool to disclose user groups by which the process of collaborative filtering can better determine a personalised suggestion. Matching this to the curse of big data is a challenge with previous studies either implementing conventional techniques on a distributed system or making use of data sampling. Specific to ensemble clustering, only a few aim to obtain both scalability and privacy preserving that are significant to handling social data. This paper presents a new bi-level framework of ensemble clustering in which an instance-segment based analysis is adopted to ensure data privacy and reduce the complexity of clustering the whole dataset. Unlike existing studies, instead of drawing a single clustering from each segment, multiple clusterings are selected to better represent instances therein. Based on published tourism datasets and different experimental settings, the new approach usually outperforms its baselines whilst being competitive to related methods found in the literature. Additional case studies on simulated big datasets and noisy variations are reported and discussed in addition to the analysis of algorithmic parameters.

Balancing Accuracy and Efficiency: A Comparative Analysis of Collaborative Filtering Algorithms for Job Recommendation Systems

Balancing Accuracy and Efficiency: A Comparative Analysis of Collaborative Filtering Algorithms for Job Recommendation Systems

A Bayesian deep recommender system for uncertainty-aware online physician recommendation

Online physician recommender systems alleviate information overload by automatically recommending the best-fit physicians to patients. In contrast to general recommendations, physicians with greater uncertainty (i.e., greater variance in patients’ feedback) may not be preferred as this could affect patients’ treatment. However, most existing recommender systems don't consider uncertainty, reducing systems’ reliability and patients’ readiness to trust. To address this concern, this study leverages Bayesian theory and develops an uncertainty-aware online physician recommender system, including a Bayesian deep collaborative filtering (BDCF) model and a novel uncertainty-aware ranking algorithm. Experiments on real-world data demonstrate the superiority of BDCF and the ranking algorithm.

Cluster-based Graph Collaborative Filtering

Graph Convolution Networks (GCNs) have significantly succeeded in learning user and item representations for recommendation systems. The core of their efficacy is the ability to explicitly exploit the collaborative signals from both the first- and high-order neighboring nodes. However, most existing GCN-based methods overlook the multiple interests of users while performing high-order graph convolution. Thus, the noisy information from unreliable neighbor nodes ( \(e.g.\) , users with dissimilar interests) negatively impacts the representation learning of the target node. Additionally, conducting graph convolution operations without differentiating high-order neighbors suffers the over-smoothing issue when stacking more layers, resulting in performance degradation. In this paper, we aim to capture more valuable information from high-order neighboring nodes while avoiding noise for better representation learning of the target node. To achieve this goal, we propose a novel GCN-based recommendation model, termed Cluster-based Graph Collaborative Filtering (ClusterGCF). This model performs high-order graph convolution on cluster-specific graphs, which are constructed by capturing the multiple interests of users and identifying the common interests among them. Specifically, we design an unsupervised and optimizable soft node clustering approach to classify user and item nodes into multiple clusters. Based on the soft node clustering results and the topology of the user-item interaction graph, we assign the nodes with probabilities for different clusters to construct the cluster-specific graphs. To evaluate the effectiveness of ClusterGCF, we conducted extensive experiments on four publicly available datasets. Experimental results demonstrate that our model can significantly improve recommendation performance.

Recommender System for Group of Users using Matrix Factorization for Tourism Domain (Case Study: Bali)

Choosing a product that suits a customer's needs requires a recommendation system to provide suggestions on a collection of items of interest to the user. Recommendations can be applied in various fields such as entertainment, shopping sites, social networking, job portals, discovery of relevant web pages, and so on. There are many circumstances where recommendations are needed for a group such as in tourism and entertainment purposes. The development of a Group Recommendation System (GRS) was carried out in response to the need to provide several recommendations to a group of users. We conducted this research to build a GRS that can provide item recommendations using the Collaborative Filtering (CF) method with Matrix Factorization Technique, as well as three approaches, namely After Factorization (AF), Before Factorization (BF), and Weighted Before Factorization (WBF). Determine the best approach for the three categories of groups formed, namely small groups (three members), medium groups (five members), and large groups (ten members). The focus of this research is the tourism destination domain in Bali. In the evaluation results of the precision calculation, the medium group obtained the highest score for the AF, BF, and WBF approaches of 0.944. Meanwhile, in calculating recall, the small group achieved the highest scores for the AF, BF, and WBF approaches of 0.294, 0.259, and 0.259, respectively. From the results of this study, it appears that small groups are suitable for using the BF approach, while the AF method is more effective for large groups, and the best approach for medium groups is the WBF.

DNN-Based Resource Recommendation for Ideology Theory Courses Online

Revamped IPE confronts static material constraints and outdated pedagogy, warranting integration of web resources and big data analytics for instructional innovation. Digital IPE adoption in vocational education optimizes online resource use, enhancing teaching effectiveness. Introducing CUPMF, a personalized learning model, we conduct empirical assessments on a large dataset (364,617+ entries) from Smart Classroom's cloud platform and public datasets, reflecting varied IPE scenarios. Comparative experiments against association rule, content-, tag-based, and collaborative filtering algorithms show CUPMF's superiority. It achieves a 11.61% F1 score boost over four alternatives for basic recommendations and outperforms Que Rec by 1.975%. Complexity-wise, CUPMF registers an 11.52% mean F1 score increment over four methods and 1.875% over Que Rec. Proven, CUPMF markedly improves IPE resource recommendation accuracy and efficacy, poised to transform personalized online vocational learning.

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 article, 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.

Feedback Collection and Nearest-Neighbor Profiling for Recommendation Systems in Healthcare Scenarios

The rise in the dimension and complexity of information generated in the clinical field has motivated research on the automation of tasks in personalized healthcare. Recommendation systems are a filtering method that utilizes patterns and data relationships to generate items of interest for a particular user. In healthcare, these systems can be used to potentiate physical therapy by providing the user with specific exercises for rehabilitation, albeit facing issues pertaining to low accuracy in earlier iterations (cold-start) and a lack of gradual optimization. In this study, we propose a physical activity recommendation system that utilizes a K-nearest neighbor (KNN) sampling strategy and feedback collection modules to improve the adequacy of recommendations at different stages of a rehabilitation period when compared to traditional collaborative filtering (CF) or human-constrained methods. The results from a trial show significant improvements in the quality of initial recommendations, achieving 81.2% accuracy before optimization. Moreover, the introduction of short-term adjustments based on frequent player feedback can be an efficient manner of improving recommendation accuracy over time, achieving overall better convergence periods than those of human-based systems, topping at a measured 98.1% accuracy at K = 7 cycles.

Stereo Matching Method with Cost Volume Collaborative Filtering

Aiming at the problem of matching ambiguity and low disparity accuracy at the object boundary in stereo matching, a novel stereo matching algorithm with cost volume collaborative filtering is proposed. Firstly, for each pixel, two support windows are built, namely a local cross- support window as well as a global support window for the whole image. Secondly, a new adaptive weighted guide filter with a cross-support window as a kernel window is derived, and it is used to locally filter the cost volume. In addition, a minimum spanning tree is constructed in the whole image window, and then the minimum spanning tree filter is used to globally filter the cost volume. The collaborative filtering of cost volume is realized by fusing the filtering results of the local filter and global filter, so that each pixel can not only receive the support of the neighboring pixels in the local adaptive window, but can also receive the effective support of other pixels in the whole image, thus effectively eliminating the matching ambiguity in different texture regions while maintaining the disparity edges. The experimental results show that the average matching error rate of our method on the Middlebury stereo images is 3.17%. Compared with the other state-of-the-art methods, our method has higher robustness and matching accuracy, the generated disparity maps are smoother, and the disparity edges are better preserved.

A recommendation attack detection approach integrating CNN with Bagging

Due to their open architecture, collaborative filtering recommender systems are susceptible to recommendation attacks, in which attackers inject fake rating data into the system to affect the accuracy of recommendation results. To detect these attacks, numerous detection methods have been designed and proven effective. However, in recent years, deep learning-based recommendation attack models such as GSA-GAN have shown higher concealment, posing new challenges to existing detection methods. Motivated by the need for improved detection, in this paper we propose a new approach called CNN-BAG, which integrates convolutional neural network (CNN) and Bagging (BAG) techniques. CNN-BAG can enhance the detection performance by simultaneously leveraging the deep learning capabilities of CNN and the ensemble learning strengths of Bagging. Firstly, we construct a deep neural network based on CNN as the base learner to automatically extract and learn features of recommendation attacks. Secondly, we use the Bagging algorithm to perform bootstrap sampling on the training data to generate multiple diverse training subsets. The above constructed base learners are then trained on these generated training subsets to produce multiple base classifiers for classifying recommendation attacks. Finally, we combine the base classifiers’ outputs using a majority voting method to obtain the final detection results. To assess the performance of CNN-BAG in detecting recommendation attacks, we compared it against several well-established detection methods on the Movielens-10M and Amazon datasets. Our experiments revealed that CNN-BAG is adept at identifying various attack types, including the deep learning-based recommendation attack models.

Collaborative Filtering for the Imputation of Patient Reported Outcomes.

This study addresses the prevalent issue of missing data in patient-reported outcome datasets, particularly focusing on head and neck cancer patient symptom ratings sourced from the MD Anderson Symptom Inventory. Given that many data mining and machine learning algorithms necessitate complete datasets, the accurate imputation of missing data as an initial step becomes crucial. In this study we propose, for the first time, the use of collaborative filtering for imputing missing head and neck cancer patient symptom ratings. Two configurations of collaborative filtering, namely patient-based and symptom-based, leverage known ratings to infer the missing ones. Additionally, this study compares the performance of collaborative filtering with alternative imputation methods such as Multiple Imputation by Chained Equations, Nearest Neighbor Imputation, and Linear interpolation. Performance is compared using Root Mean Squared Error and Mean Absolute Error metrics. Findings demonstrate that collaborative filtering is a viable and comparatively superior approach for imputing missing patient symptom data.

A denoising method for power equipment images based on block-matching and 3D filtering.

A substation is important equipment of the power system, and there are many power equipment components in the substation. In order to better detect the working status of power equipment components, it is necessary to preprocess these components. In the actual application, the power equipment images may be noisy due to external environmental interference. Therefore, it should denoise these images in order to improve system detection performance. This paper uses the acquired power equipment images and adds noise intensity of 10, 15, 20, 25, and 30, respectively. Then, the Block-Matching and 3D Filtering (BM3D) method is used to denoise these images. BM3D includes three steps such as block combination, collaborative filtering, and integration, which has strong denoising ability. The experimental results show that the proposed method outperforms other methods in terms of denoising visual effects and evaluation indicators. Especially in terms of preserving details and textures of the denoised image, there is a significant advantage in suppressing strong noise. In summary, the proposed method can achieve encouraging denoising results, which is an effective denoising method for power equipment images.

Job Recommendations: Benchmarking of Collaborative Filtering Methods for Classifieds

Job Recommendations: Benchmarking of Collaborative Filtering Methods for Classifieds

Personalized Recommendation Systems Powered By Large Language Models: Integrating Semantic Understanding and User Preferences

This study proposes a novel personalized recommendation system leveraging Large Language Models (LLMs) to integrate semantic understanding with user preferences [1]. The system addresses critical challenges in traditional recommendation approaches by harnessing LLMs' advanced natural language processing capabilities. We introduce a framework combining a fine-tuned Roberta semantic analysis model with a multi-modal user preference extraction mechanism.The LLM component undergoes domain adaptation using Masked Language Modeling on a corpus of 112,000 user reviews from the MyAnimeList dataset, followed by task-specific fine-tuning using contrastive learning. User preferences are modeled through a weighted combination of explicit ratings, review sentiment, and implicit feedback, incorporating temporal dynamics through a time-decay function. Experimental results demonstrate significant improvements over state-of-the-art baselines, including Matrix Factorization, Neural Collaborative Filtering, BERT4Rec, and LightGCN. Our LLM-powered system achieves an 8.6%increase in NDCG@10 and a 10.5% improvement in Mean Reciprocal Rank compared to the best-performing baseline. Ablation studies reveal the synergistic effect of integrating LLM-based semantic understanding with user preference modeling. Case studies highlight the system's ability to recommend long-tail items and provide cross-genre suggestions, showcasing its capacity for nuanced content understanding. Scalability analysis indicates that while the LLM-based approach has higher initial computational costs, its performance scales comparably to other deep learning models for larger datasets. This research contributes to the field by demonstrating the effectiveness of LLMs in enhancing recommendation accuracy and diversity. Future work will explore advanced LLM architectures, multi-modal data integration, and techniques to improve computational efficiency and interpretability of recommendations.

Neural Collaborative Learning for User Preference Discovery From Biased Behavior Sequences

The rapid increase of the data of user behaviors on the Internet brings a promising chance to better discover user preferences. Recommender systems have become a popular tool for the discovery of user preferences. One key issue is how to employ user behavior sequences to develop effective sequential recommendations, especially when behavior sequences are biased. The current sequential recommendation methods either can only mine data dependencies but ignores bias or only can learn bias but cannot mine data dependencies. To solve these problems, in this article, we propose a neural collaborative sequential learning mechanism, which learns sequential information from user behavior sequences that contain bias. We propose a neural collaborative filtering (NCF) model that fully takes advantage of all data dependencies among users, items, and biased sequential behaviors. Our sequential learning mechanism employs a self-attention mechanism to learn sequential features into an embedding space and inputs this sequential embedding into the generalized matrix factorization (GMF) model and the multilayer perceptron (MLP) model. We performed experiments on two real-world datasets and compared our model with many well-known baselines. The experimental results demonstrate that our model achieves superior performance. We also give a thorough analysis through ablation experiments and sensitivity experiments.