Traditional Collaborative Filtering Research Articles

KGCFRec: Improving Collaborative Filtering Recommendation with Knowledge Graph

Traditional collaborative filtering (CF)-based recommendation systems are often challenged by data sparsity. The recent research has recognized the potential of integrating new information sources, such as knowledge graphs, to address this issue. However, a common drawback is the neglect of the interplay between user–item interaction data and knowledge graph information, resulting in insufficient model performance due to coarse-grained feature fusion. To bridge this gap, in this paper, we propose a novel graph neural network (GNN) model called KGCFRec, which leverages both Knowledge Graph and user–item Collaborative Filtering information for an enhanced Recommender system. KGCFRec employs a dual-channel information propagation and aggregation mechanism to generate distinct representations for the collaborative knowledge graph and the user–item interaction graph. This is followed by an attention mechanism that adaptively fuses the knowledge graph with collaborative information, thereby refining the representations and narrowing the gap between them. The experiments conducted on three real-world datasets demonstrate that KGCFRec outperforms state-of-the-art methods. These promising results underscore the capability of KGCFRec to enhance recommendation accuracy by integrating knowledge graph information.

Research on the Recommendation System of Music e-learning Resources with Blockchain based on Hybrid Deep Learning Model

Learners are confronted with an ever-growing array of diverse and complex educational resources as music education increasingly moves to online platforms. Traditional resource curation methods, which rely heavily on educators, fall short of meeting the dynamic needs of modern students. To address this issue, we present a novel recommendation system for music e-learning resources that combines the power of blockchain technology with a hybrid deep learning model. Our model combines blockchain's robust security and transparency features with advanced deep learning algorithms, enhancing the personalization and efficiency of resource recommendations. A backpropagation neural network with K nearest neighbor classification, traditional collaborative filtering (CF), and an improved CF algorithm are used in the hybrid approach. For the back propagation neural network algorithm, K nearest neighbor classification algorithm, traditional collaborative filtering (CF) and improved CF algorithm, the accuracy rate of improved CF algorithm is higher, reaching 95%. Comparing the proposed model with the association rule-based recommendation model and the content-based recommendation model, the model constructed in this study received high evaluation from experts, with an average score of 98, and more than 97% of them gave a high score of 95 or more, and the evaluation of experts tended to be consistent. Overall, the model proposed in this study can make better recommendations for music education learning resources and bring users a good learning experience, so this study has some practical application value. This research demonstrates a highly effective, blockchain-enhanced recommendation system for music e-learning resources. Our model has significant practical value and potential for adoption in online music education platforms because it provides tailored educational content and an enhanced learning experience.

AXCF: Aspect‐based collaborative filtering for explainable recommendations

AbstractWith the rapid growth of the e‐commerce market facilitated, users are often overwhelmed by the excessive online information, making item selection challenging. While recommendation services have significantly enhanced user experience and sales, these traditional models often overlook the complexity of user‐item interactions and user preferences based on various item aspects. The proposed AXCF framework innovatively combines graph‐based collaborative filtering (CF), which captures high‐order connectivity between users and items, with aspect‐based sentiment analysis (ABSA) to extract detailed user preferences from online reviews. This approach addresses the limitations of linear relationships in traditional CF models by incorporating deep neural networks and introduces a method to overcome the cold‐start problem using online reviews as auxiliary information. By focusing on main aspects such as food, ambiance, and service derived from restaurant reviews, AXCF provides personalized recommendations with improved accuracy and explanatory power, demonstrating its superiority over existing models through experimental results. This study not only presents a significant advancement in recommender systems but also highlights the importance of high‐order connectivity and aspect‐based preferences in understanding and catering to user needs in the e‐commerce platform.

The Growth of Contemporary Music Subject and the Reform of Music Teaching in Universities

In order to promote the growth of contemporary music and the reform of music, this article designs an improved collaborative filtering (CF) algorithm to solve the problem of sparse matrix in traditional recommendation algorithms. The data matrix is dimensionally reduced to find the nearest neighbor, so as to realize personalized recommendation of music teaching resources in colleges and universities. The test results show that the accuracy of the proposed teaching resource recommendation algorithm is improved by 22.56% compared with the traditional CF algorithm. The improved CF algorithm can provide more accurate prediction, and the recommendation effect of the improved algorithm is better than the original algorithm, which can effectively avoid the sparse matrix problem faced by the CF algorithm, and provide technical support for the development of contemporary music discipline and the reform of music discipline.

Integration of Deep Reinforcement Learning with Collaborative Filtering for Movie Recommendation Systems

In the era of big data, effective recommendation systems are essential for providing users with personalized content and reducing search time on online platforms. Traditional collaborative filtering (CF) methods face challenges like data sparsity and the new-user or cold-start issue, primarily due to their reliance on limited user–item interactions. This paper proposes an innovative movie recommendation system that integrates deep reinforcement learning (DRL) with CF, employing the actor–critic method and the Deep Deterministic Policy Gradient (DDPG) algorithm. This integration enhances the system’s ability to navigate the recommendation space effectively, especially for new users with less interaction data. The system utilizes DRL for making initial recommendations to new users and to generate optimal recommendation as more data becomes available. Additionally, singular value decomposition (SVD) is used for matrix factorization in CF, improving the extraction of detailed embeddings that capture the latent features of users and movies. This approach significantly increases recommendation precision and personalization. Our model’s performance is evaluated using the MovieLens dataset with metrics like Precision, Recall, and F1 Score and demonstrates its effectiveness compared with existing recommendation benchmarks, particularly in addressing sparsity and new-user challenges. Several benchmarks of existing recommendation models are selected for the purpose of model comparison.

Less is More: Removing Redundancy of Graph Convolutional Networks for Recommendation

While Graph Convolutional Networks (GCNs) have shown great potential in recommender systems and collaborative filtering (CF), they suffer from expensive computational complexity and poor scalability. On top of that, recent works mostly combine GCNs with other advanced algorithms which further sacrifice model efficiency and scalability. In this work, we unveil the redundancy of existing GCN-based methods in three aspects: (1) Feature redundancy . By reviewing GCNs from a spectral perspective, we show that most spectral graph features are noisy for recommendation, while stacking graph convolution layers can suppress but cannot completely remove the noisy features, which we mostly summarize from our previous work; (2) Structure redundancy . By providing a deep insight into how user/item representations are generated, we show that what makes them distinctive lies in the spectral graph features, while the core idea of GCNs (i.e., neighborhood aggregation) is not the reason making GCNs effective; and (3) Distribution redundancy . Following observations from (1), we further show that the number of required spectral features is closely related to the spectral distribution, where important information tends to be concentrated in more (fewer) spectral features on a flatter (sharper) distribution. To make important information be concentrated in as few features as possible, we sharpen the spectral distribution by increasing the node similarity without changing the original data, thereby reducing the computational cost. To remove these three kinds of redundancies, we propose a Simplified Graph Denoising Encoder (SGDE) only exploiting the top- K singular vectors without explicitly aggregating neighborhood, which significantly reduces the complexity of GCN-based methods. We further propose a scalable contrastive learning framework to alleviate data sparsity and to boost model robustness and generalization, leading to significant improvement. Extensive experiments on three real-world datasets show that our proposed SGDE not only achieves state-of-the-art but also shows higher scalability and efficiency than our previously proposed GDE as well as traditional and GCN-based CF methods.

Personalized Recommendation Method of E-Commerce Products Based on In-Depth User Interest Portraits

In dynamic e-commerce environments, researchers strive to understand users' interests and behaviors to enhance personalized product recommendations. Traditional collaborative filtering (CF) algorithms have encountered computational challenges such as similarity errors and user rating habits. This research addresses these issues by emphasizing user profiling techniques. This article proposes an innovative user profile updating technique that explores the key components of user profile (basic information, behavior, and domain knowledge). An enhanced kernel fuzzy mean clustering algorithm constructs a dynamic user portrait based on domain knowledge mapping. This dynamic portrait is combined with e-commerce personalized recommendation to improve the accuracy of inferring user interests, thus facilitating accurate recommendation on the platform. The method proposed in this article greatly improves the overall performance and provides strong support for developing smarter and more personalized e-commerce product recommendation systems.

Enhancing Recommendation System using Ontology-based Similarity and Incremental SVD Prediction

Background: With the explosion of data in recent years, recommender systems have become increasingly important for personalized services and enhancing user engagement in various industries, including e-commerce and entertainment. Collaborative filtering (CF) is a widely used approach for generating recommendations, but it has limitations in addressing issues such as sparsity, scalability, and prediction errors. Methods: To address these challenges, this study proposes a novel hybrid CF method for movie recommendations that combines an incremental singular value decomposition approach with an item-based ontological semantic filtering approach in both online and offline phases. The ontology-based technique improves the accuracy of predictions and recommendations. The proposed method is evaluated on a real-world movie recommendation dataset using several performance metrics, including precision, F1 scores, and MAE. Results: The results demonstrate that the proposed method outperforms existing methods in terms of accuracy while also addressing sparsity and scalability issues in recommender systems. Additionally, our approach has the advantage of reduced running time, making it a promising solution for practical applications. Conclusion: The proposed method offers a promising solution to the challenges faced by traditional CF methods in recommender systems. By combining incremental SVD and ontological semantic filtering, the proposed method not only improves the accuracy of predictions and recommendations but also addresses issues related to scalability and sparsity. Overall, the proposed method has the potential to contribute to the development of more accurate and efficient recommendation systems in various industries, including e-commerce and entertainment.

A recommending system for mobile games using the dynamic nonparametric model

We contribute to the literature on recommendation systems by proposing the Dynamic Nonparametric (DNP) model that incorporates a fully dynamic Bayesian approach into the traditional collaborative filtering and hence captures the change of customer preferences in the mobile game industry with higher flexibility. Using the extensive log data of mobile gamers, we demonstrate that our DNP model closely predicts game usage patterns with higher accuracy than other widely used benchmarks. We also present various applications that reveal the managerial relevance of our model. Personalized recommendation lists of games generated by the DNP model outperform those by benchmarks in terms of adoption rates and repeated usage. Our model can also be used to improve the prediction performance of future in-app purchases and to decide the optimal size of recommendation sets based on types of gamers.

SelfCF: A Simple Framework for Self-supervised Collaborative Filtering

Collaborative filtering (CF) is widely used to learn informative latent representations of users and items from observed interactions. Existing CF-based methods commonly adopt negative sampling to discriminate different items. That is, observed user-item pairs are treated as positive instances; unobserved pairs are considered as negative instances and are sampled under a defined distribution for training. Training with negative sampling on large datasets is computationally expensive. Further, negative items should be carefully sampled under the defined distribution, in order to avoid selecting an observed positive item in the training dataset. Unavoidably, some negative items sampled from the training dataset could be positive in the test set. Recently, self-supervised learning (SSL) , has emerged as a powerful tool to learn a model without negative samples. In this paper, we propose a self-supervised collaborative filtering framework (SelfCF) , that is specially designed for recommender scenario with implicit feedback. The proposed SelfCF framework simplifies Siamese networks and can be easily applied to existing deep-learning based CF models, which we refer to as backbone networks. The main idea of SelfCF is to augment the latent embeddings generated by backbone networks instead of the raw input of user/item ids. We propose and study three embedding perturbation techniques that can be applied to different types of backbone networks including both traditional CF models and graph-based models. The framework enables learning informative representations of users and items without negative samples, and is agnostic to the encapsulated backbones. We conduct experimental comparisons on four datasets, one self-supervised framework, and eight baselines to show that our framework may achieve even better recommendation accuracy than the encapsulated supervised counterpart with a 2×–4× faster training speed. The results also demonstrate that SelfCF can boost up the accuracy of a self-supervised framework BUIR by 17.79% on average and shows competitive performance with baselines.

Improved collaborative filtering personalized recommendation algorithm based on k-means clustering and weighted similarity on the reduced item space

<abstract><p>Collaborative filtering (CF) algorithm is one of the most widely used recommendation algorithms in recommender systems. However, there is a data sparsity problem in the traditional CF algorithm, which may reduce the recommended efficiency of recommender systems. This paper proposes an improved collaborative filtering personalized recommendation (ICF) algorithm, which can effectively improve the data sparsity problem by reducing item space. By using the k-means clustering method to secondarily extract the similarity information, ICF algorithm can obtain the similarity information of users more accurately, thus improving the accuracy of recommender systems. The experiments using MovieLens and Netflix data set show that the ICF algorithm has a significant improvement in the accuracy and quality of recommendation.</p></abstract>

Multi-Level Knowledge-Aware Contrastive Learning Network for Personalized Recipe Recommendation

Personalized recipe recommendation is attracting more and more attention, which can help people make choices from the exploding growth of online food information. Unlike other recommendation tasks, the target of recipe recommendation is a non-atomic item, so attribute information is especially important for the representation of recipes. However, traditional collaborative filtering or content-based recipe recommendation methods tend to focus more on user–recipe interaction information and ignore higher-order semantic and structural information. Recently, graph neural networks (GNNs)-based recommendation methods provided new ideas for recipe recommendation, but there was a problem of sparsity of supervised signals caused by the long-tailed distribution of heterogeneous graph entities. How to construct high-quality representations of users and recipes becomes a new challenge for personalized recipe recommendation. In this paper, we propose a new method, a multi-level knowledge-aware contrastive learning network (MKCLN) for personalized recipe recommendation. Compared with traditional comparative learning, we design a multi-level view to satisfy the requirement of fine-grained representation of users and recipes, and use multiple knowledge-aware aggregation methods for node fusion to finally make recommendations. Specifically, the local-level includes two views, interaction view and semantic view, which mine collaborative information and semantic information for high-quality representation of nodes. The global-level learns node embedding by capturing higher-order structural information and semantic information through a network structure view. Then, a kind of self-supervised cross-view contrastive learning is invoked to make the information of multiple views collaboratively supervise each other to learn fine-grained node embeddings. Finally, the recipes that satisfy personalized preferences are recommended to users by joint training and model prediction functions. In this study, we conduct experiments on two real recipe datasets, and the experimental results demonstrate the effectiveness and advancement of MKCLN.

An Integrated Collaborative Filtering Framework With Location-Aware Graph Embedding in Intelligent Internet of Things Systems

Reliable Internet of Things (IoT) service discovery is a significant task in intelligent service-oriented IoT systems. Collaborative filtering (CF) turns out to be an effective solution to IoT service discovery. However, the traditional CF framework is facing the following challenges: inefficiency in learning the high-order interactions between users and IoT services and ineffectively making use of geographical location information. Moreover, deploying a CF framework in real-world distributed IoT systems poses another significant challenge in terms of reliability assurance and privacy protection. For bridging this gap, we propose an innovative integrated collaborative filtering framework (ICF) with incorporating the location-aware quality of IoT services into a heterogeneous graph embedding model. Meanwhile, a Geohash-based privacy-preservation mechanism is introduced to encoding the location information into a short string for protecting the sensitive location information. The proposed ICF framework is an integrated architecture combining advanced graph embedding learning and heterogeneous side information. And a joint objective optimization function is designed in the graph embedding learning to qualify automatic IoT service features. In this way, the hidden user-service interaction information and location-aware quality semantic features can be explored in an effective and reliable way. Furthermore, the learned location-aware quality embedding vector is incorporated to discover the reliable services among all IoT services with the mini-batch online clustering-based CF algorithm. The experimental findings illustrate the significant efficacy and reliability of our proposed ICF framework in large scale IoT service discovery scenarios.

Contextualized Recommendation of Aviation Ancillary Services Based on Passenger Portraits

Ancillary services are crucial to Chinese domestic airlines’ profits. Personalized recommendations for ancillary aviation services must consider massive amounts of contextualized online and offline data. The issue of sparsity is more serious in massive data, which makes traditional collaborative filtering (CF) algorithms less accurate. This article adds instant contextualized travel-related factors to the modeling of air passengers’ ancillary services, thus achieving dynamic recommendations. It also proposes a four-tuple to construct a contextual ontology as a conceptual model of ancillary aviation services. The article establishes a context-aware air passenger portrait model based on four tag sets, including passenger attribute tags, ancillary service attribute tags, passenger interaction behavior tags, and contextual attribute tags. Further, it proposes a multidimensional method for calculating contextual portrait similarity; the method recommends a list of the top- <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">N</i> items to the target passenger according to the similarity calculation. Compared with the traditional CF recommendation method, the proposed algorithm has better performance, improving the accuracy rate by 4.3% and the recall rate by 2.62%. The final experimental result demonstrates that the contextual recommendation method achieves higher passenger satisfaction, which can provide consolidated data and decision support for the management of aviation ancillary services.

Personalized tourist route recommendation model with a trajectory understanding via neural networks

ABSTRACT Travel recommendations form a major part of tourism service. Traditional collaborative filtering and Markov model are not appropriate for expressing the trajectory features, for travel preferences of tourists are dynamic and affected by previous behaviors. Inspired by the success of deep learning in sequence learning, a personalized recurrent neural network (P-RecN) is proposed for tourist route recommendation. It is data-driven and adaptively learns the unknown mapping of historical trajectory input to recommended route output. Specifically, a trajectory encoding module is designed to mine the semantic information of trajectory data, and LSTM neural networks are used to capture the sequence travel patterns of tourists. In particular, a temporal attention mechanism is integrated to emphasize the main behavioral intention of tourists. We retrieve a geotagged photo dataset in Shanghai, and evaluate our model in terms of accuracy and ranking ability. Experimental results illustrated that P-RecN outperforms other baseline approaches and can effectively understand the travel patterns of tourists.

Content-Based Collaborative Filtering With Predictive Error Reduction-Based CNN Using IPU Model

Recommender systems (RS) are strong tools for addressing the internet networking overload problems by considering past user ratings on multiple items with auxiliary data and suggests the better item to the end user. Traditional collaborative filtering (CF) and content-based methods were identified the interaction or correlation between users and the items. But they have failed to identify the join user-item interactions and suffering from incomplete cold start (ICS) and complete cold start (CCS) issues. To address the deficiencies of CF-based approaches, this article offers a novel deep learning based error predictions method along with CF based user-item interactions. Initially, incentivized/penalized user-based content-based collaborative filtering (IPU-CBCF) method is introduced for learning low-dimensional vectors of users and items, separately. The simulation results shows that IPU-CBCF using PER-CNN resulted in better performance as compared to the conventional approaches for all performance metrics like F1-score, recall, and precision, respectively.

Development of Ecological Health Tourism Products under the Background of Internet+

With the development of domestic economy and the improvement of people’s living standard, tourism has become more and more popular as a leisure lifestyle. The explosive growth of the mobile Internet has caused the problem of “information overload”. The travel recommendation system can help tourists obtain the travel information that users are interested in from the massive data. Ecological health tourism is a special tourism product with ecological environment as the background and leisure health activities as the theme. With the development of China’s urbanization and the intensification of population aging, the Chinese people’s demand for health tourism products and ecological health tourism market is becoming stronger and stronger, and the development prospect is extremely broad, but there is not much research in this field in the academic circles at present. This paper applies the Collaborative Filtering (CF) to travel recommendation to provide users with accurate travel recommendation services. However, because the traditional CF only relies on a single user’s rating data, and has its own defects, it cannot meet the complex needs of users in the tourism industry. This paper improves the traditional CF and designs and implements a tourism recommendation system on this basis. Combine Spark cloud computing platform technology and TC-Personal Rank algorithm to achieve a breakthrough in the algorithm. Through experiments, it can be found that the accuracy of product recommendation can be improved by 75.3% for the algorithm designed in this paper. Overall, the recall rate can reach 65.7%. And it can also achieve good results in recommendation satisfaction and recommendation coverage.

Web Service Recommendation Based on Word Embedding and Node Embedding

Thanks to the rapid development of service-oriented computing (SOC) technologies, the number of Web services, such as Web API, is increasing rapidly. However, this brings some difficulties for mashup (a kind of Web API composition) developers to choose appropriate Web Services to build their projects. Finding required Web APIs from a large number of candidates and recommending them to developers has become a vital issue in mashup development. The traditional collaborative filtering algorithm has the problems of cold start and sparse data. In order to solve the deficiency of the collaborative filtering algorithm, we propose an improved hybrid method that combines the two kinds of information to generate word embedding and node embedding, avoiding the cold start problem and data sparsity problem. Experiments on real-world data sets show that our proposed approach is better than five state-of-the-art approaches, which verifies the effectiveness of our approach.

Analysis of the Influence of Hybrid Personalized Recommendation Algorithm on the Employment Tendency of Hotel Management Major

As the pillar industry of tourism, the hotel industry plays an important role in the present and future development of tourism. Therefore, the industry’s demand for hotel management professionals is increasing. In this paper, the mixed personalized recommendation algorithm is used to carry out descriptive statistical analysis on the results, and the characteristics of the employment tendency of college students majoring in hotel management are preliminarily understood. In addition, the influence of human capital and social capital on college students’ employment tendency is discussed. The results show that, through experiments, the hybrid personalized recommendation algorithm proposed in this paper is compared with two traditional collaborative filtering recommendation algorithms based on users, Pearson similarity and cosine similarity. The algorithm in this paper has smaller MAE, which shows that the algorithm proposed in this paper has higher recommendation quality. Through this research, it is helpful to have a more comprehensive understanding of the actual situation and characteristics of the employment tendency of hotel management major college students, and it also provides the policy basis for the government and relevant departments to make employment decisions.

Enhancing Grade Prediction via Course Attribute-Aware Student Similarity in Collaborative Filtering

Collaborative filtering has been used in predicting personalized students’ grades in courses at higher educational institutions. The traditional collaborative filtering through student-to-student correlation is based on similarity between students with respect to their grades in all courses that they have taken. However, courses are not completely independent each other. Given a course, the grades of its prerequisite, corequisite and similar courses will affect the grade of the course more than the grades of other unrelated courses will.&#x0D; We propose a course attribute-aware student-to-student collaborative filtering-based grade prediction method that incorporates course-relevant attributes such as prerequisite courses, corequisite courses and similar courses for enhanced grade prediction. To predict the grade of a target course C for a target student S, we identify all direct and indirect prerequisite and corequisite courses of C and compute two types of similarity values between C and other courses, i.e., a) the collaborative course-to-course-based similarity values via the Pearson correlation coefficient (PCC) and b) the semantic textual course content description-based similarity values via the Cosine similarity on the numeric vector representations of the course content descriptions via Doc2Vec. The course attribute-aware student similarity between S and other students for C is computed as a weighted similarity measure such as the Weighted Pearson correlation coefficient (WPCC) by assigning appropriate weights on each direct/indirect prerequisite course of C, each direct/indirect corequisite course of C, and each course based on two types of similarity values between C and the course. The weights can be adjustable and tuned for optimization.