Improve Recommendation Accuracy Research Articles

Enhancing machine learning recommendation systems with CNN architectures: Applications and innovations in computer vision and speech recognition

Abstract. This paper provides an in-depth analysis of enhancing machine learning recommendation systems using Convolutional Neural Network (CNN) architectures, with a focus on their applications in computer vision and speech recognition. Traditional recommendation systems often struggle with scalability and the complexity of high-dimensional data. By integrating CNNs, these systems can utilize advanced feature extraction and representation learning to more effectively process and analyze diverse data sources. Our study demonstrates significant improvements in recommendation accuracy and personalization through CNN-enhanced systems. We discuss the architecture, design principles, and advantages of CNNs, supported by case studies across various domains. Our findings illustrate the potential of CNNs to revolutionize recommendation systems by addressing existing limitations and offering innovative solutions for real-time, high-quality recommendations. This research emphasizes the importance of advanced machine learning techniques in creating robust and scalable recommendation systems, paving the way for future advancements and applications in multiple fields.

Implementation and Evaluation of a Hybrid Recommendation System for the Real Estate Market

Introduction: The real estate market has been transformed by digital technologies, especially Industry 4.0, which has made property searching and evaluation more efficient, improving its accuracy with the use of advanced algorithms. Traditional methods have been replaced by online platforms using modern machine learning (ML) algorithms, leading to the need for personalized recommendation systems to improve user experiences. Methodology: This study designed and implemented a hybrid recommendation system that combines collaborative and content-based filtering techniques. The development process involved four phases: literature review, technology selection, prototype implementation, and system deployment. Findings: The proposed hybrid model effectively addressed challenges such as data sparsity and the cold start problem, improving recommendation accuracy. In the evaluation, users indicated high satisfaction with the system’s ability to offer personalized property recommendations. Conclusion: Thus, hybrid recommendation systems can significantly improve the property search experience by offering personalized recommendations. However, further research into the applicability of the system in different domains remains a need for further exploration.

Can Optimized Genetic Algorithms Improve the Effectiveness of Homestay Recommendation Systems in Smart Villages? A Case of Thailand

ABSTRACTThis paper introduces a novel approach to optimize genetic algorithms (GAs) for homestay recommendation systems, specifically designed for smart village tourism destinations. Researchers developed an advanced GA focused on maximizing user satisfaction, the main quality metric. The algorithm was tailored to address the dynamic nature of homestay offerings and the varied preferences of travelers, using users' reviews, listing attributes, and historical booking data. The GA framework included a custom encoding scheme, fitness function, and parameters. Validation occurred through a case study in a smart village, with the algorithm's effectiveness tested via user surveys and ratings. Results showed that GA‐driven recommendations surpassed traditional methods, enhancing user satisfaction, trust, and booking rates while benefiting hosts with positive reviews. The optimized GA improved recommendation accuracy and efficiency, boosting economic benefits for local communities and contributing significantly to recommendation system research.

AI-empowered consumer behavior analysis for trustworthy track recommendation over musical dance electronic products

—In the digital music era, accurate and trustworthy track recommendations for musical dance electronic products are becoming increasingly important to improve user experiences and attract more consumers. Consumer behavior modeling is critical in user interest learning and has been extensively used in recommender systems to improve recommendation accuracy. This paper proposes a novel AI-empowered consumer behavior analysis method for trustworthy track recommendations over musical dance electronic products. Specifically, we first model consumer behavior by integrating collaborative filtering and a hidden Markov model to capture the key interactive patterns between consumers and musical dance electronic products. Then, we develop a trustworthy track recommendation method based on multi-layer attention representation learning, which leverages scattering transform for audio preprocessing and attention-based independent recurrent neural networks for encoding user preferences and product features. Extensive experiments on real-world datasets demonstrate the superiority of our proposed method in terms of recommendation accuracy and trustworthiness.

Spatiotemporal context transition model based on graph convolutional network and its implementation

PurposeThe purpose of this paper is to present a method that addresses the data sparsity problem in points of interest (POI) recommendation by introducing spatiotemporal context features based on location-based social network (LBSN) data. The objective is to improve the accuracy and effectiveness of POI recommendations by considering both spatial and temporal aspects.Design/methodology/approachTo achieve this, the paper introduces a model that integrates the spatiotemporal context of POI records and spatiotemporal transition learning. The model uses graph convolutional embedding to embed spatiotemporal context information into feature vectors. Additionally, a recurrent neural network is used to represent the transitions of spatiotemporal context, effectively capturing the user’s spatiotemporal context and its changing trends. The proposed method combines long-term user preferences modeling with spatiotemporal context modeling to achieve POI recommendations based on a joint representation and transition of spatiotemporal context.FindingsExperimental results demonstrate that the proposed method outperforms existing methods. By incorporating spatiotemporal context features, the approach addresses the issue of incomplete modeling of spatiotemporal context features in POI recommendations. This leads to improved recommendation accuracy and alleviation of the data sparsity problem.Practical implicationsThe research has practical implications for enhancing the recommendation systems used in various location-based applications. By incorporating spatiotemporal context, the proposed method can provide more relevant and personalized recommendations, improving the user experience and satisfaction.Originality/valueThe paper’s contribution lies in the incorporation of spatiotemporal context features into POI records, considering the joint representation and transition of spatiotemporal context. This novel approach fills the gap left by existing methods that typically separate spatial and temporal modeling. The research provides valuable insights into improving the effectiveness of POI recommendation systems by leveraging spatiotemporal information.

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.

Diversity and Serendipity Preference-Aware Recommender System

Diversity and novelty are essential objectives in recommender systems to improve stakeholders' benefits by reducing user's discovery efforts and improving business operators’ sales and revenue. Existing diversity and novelty-based methods indifferently increase diversity or novelty for every user, which inevitably induces the trade-off dilemma between relevance and accuracy. Moreover, different users have different preferences for recommendation diversity and novelty. Such preference should be considered by a recommendation algorithm, thereby avoiding the trade-off dilemma and increasing the prediction accuracy. To address this research gap, we propose a new Diversity and Serendipity-Aware Recommender System (DSPA-RS) problem and its solution method. The MovieLens-2k data are used to evaluate our proposed DSPA-RS method against seven widely used recommendation methods in recommender systems as benchmarks. The test results demonstrate our method shows a superior performance than the benchmarks by a range of 34.30% to 108.27%, indicating that the movies recommended by our method best satisfy users’ diversity and serendipity preference. For recommendation accuracy, our DSPA-RS method outperforms the most accurate method by 34.62% in Precision, 7.71% in Recall, and 24.37% in F1 score. The improvement in recommendation accuracy indicates that DSPA-RS’s consideration and utilization of diversity preference and novelty momentum greatly improves recommendation quality.

Emerging synergies between large language models and machine learning in e-commerce recommendations

This paper explores the integration of large language models (LLMs) into collaborative filtering algorithms to enhance recommendation systems in the e-commerce domain. The proposed approach combines user-based and item-based collaborative filtering with LLMs to improve recommendation accuracy and personalization. Specifically, the study introduces a novel framework called PALR, which leverages LLMs to refine user-item interactions and enrich item representations. PALR utilizes historical user behavior data, such as clicks, purchases, and ratings, to guide candidate retrieval and generate recommended items. This study highlights the importance of integrating LLMs into recommendation systems to deliver more accurate and personalized suggestions, ultimately improving user satisfaction and driving sales in e-commerce platforms.

Collaborative Sequential Recommendations via Multi-view GNN-transformers

Sequential recommendation systems aim to exploit users’ sequential behavior patterns to capture their interaction intentions and improve recommendation accuracy. Existing sequential recommendation methods mainly focus on modeling the items’ chronological relationships in each individual user behavior sequence, which may not be effective in making accurate and robust recommendations. On the one hand, the performance of existing sequential recommendation methods is usually sensitive to the length of a user’s behavior sequence (i.e., the list of a user’s historically interacted items). On the other hand, besides the context information in each individual user behavior sequence, the collaborative information among different users’ behavior sequences is also crucial to make accurate recommendations. However, this kind of information is usually ignored by existing sequential recommendation methods. In this work, we propose a new sequential recommendation framework, which encodes the context information in each individual user behavior sequence as well as the collaborative information among the behavior sequences of different users, through building a local dependency graph for each item. We conduct extensive experiments to compare the proposed model with state-of-the-art sequential recommendation methods on five benchmark datasets. The experimental results demonstrate that the proposed model is able to achieve better recommendation performance than existing methods, by incorporating collaborative information.

CrossGCL: Cross-pairwise graph contrastive learning for unbiased recommendation

Popularity bias is commonly observed in recommendation results. Directly fitting biased data can significantly affect the quality of recommendations for long-tail items. To eliminate popularity bias, we propose a novel recommendation method called CrossGCL. Unlike existing Bayesian Personalized Ranking (BPR) based unbiased recommendation methods, CrossGCL employs Cross Pairwise Ranking (CPR) as its primary task, in an attempt to accurately predict user preferences and promote niche items. Furthermore, to better utilize unsupervised data for debiasing, CrossGCL introduces an innovative layer-wise graph contrastive learning approach based on odd-numbered and even-numbered layers and incorporates noise augmentation to construct pretext tasks. Then CrossGCL jointly trains pretext task with primary task for Top-K recommendation. Experimental results on MovieLens-10M, Netflix, and Alibaba-iFashion demonstrate that, compared to baselines, CrossGCL effectively reduces the proportion of popular items in recommendation lists while improving recommendation accuracy. The code is available at https://github.com/RYoto116/CrossGCL.

Personalized multi-attribute recommendation method based on partial order set

With the rapid development of big data and continuous optimization of online shopping platforms, personalized recommendation has become a standard feature of recommendation methods. In order to effectively provide personalized recommendations to customers, improve recommendation accuracy, and customer satisfaction, it is necessary to consider customers’ preferences for multiple product attributes when making product recommendations. However, existing recommendation methods require precise calculation of product attribute weights, which is computationally expensive, complex, and often results in unstable weight values. This paper proposes a multi-attribute recommendation method based on consumer decision preference information that overcomes the need for weights and reflects personalized customer preferences. Based on the acquisition of customer product attribute preference sequences, a partial order relation for recommended products is constructed using partial order set theory. Finally, the recommended products are determined through the partial order Hasse diagram, where the top layer elements of the Hasse diagram represent the recommended product set. This method addresses challenges that traditional content-based recommendations cannot overcome. The experiment in this paper uses a dataset of 30,000 records from Beeradvocate beer reviews. The experimental results show that, compared to traditional multi-attribute recommendation methods, this method only requires decision-maker preference information to complete product recommendations, requiring less information and having lower computational costs, resulting in more robust results.

SiReN: Sign-Aware Recommendation Using Graph Neural Networks.

In recent years, many recommender systems using network embedding (NE) such as graph neural networks (GNNs) have been extensively studied in the sense of improving recommendation accuracy. However, such attempts have focused mostly on utilizing only the information of positive user-item interactions with high ratings. Thus, there is a challenge on how to make use of low rating scores for representing users' preferences since low ratings can be still informative in designing NE-based recommender systems. In this study, we present SiReN, a new Sign-aware Recommender system based on GNN models. Specifically, SiReN has three key components: 1) constructing a signed bipartite graph for more precisely representing users' preferences, which is split into two edge-disjoint graphs with positive and negative edges each; 2) generating two embeddings for the partitioned graphs with positive and negative edges via a GNN model and a multilayer perceptron (MLP), respectively, and then using an attention model to obtain the final embeddings; and 3) establishing a sign-aware Bayesian personalized ranking (BPR) loss function in the process of optimization. Through comprehensive experiments, we empirically demonstrate that SiReN consistently outperforms state-of-the-art NE-aided recommendation methods.

Intelligent Talent Recommendation Algorithm for College Students for the Future Job Market

An intelligent talent recommendation algorithm is a sophisticated system that utilizes advanced data analysis techniques and machine learning algorithms to match individuals with suitable job opportunities or educational programs. By analyzing a wide range of data points such as skills, qualifications, work experience, and preferences, these algorithms can generate personalized recommendations tailored to each individual's unique profile and career goals. Moreover, intelligent talent recommendation algorithms continuously learn and improve over time, refining their recommendations based on user feedback and performance data. This paper presents an intelligent talent recommendation algorithm tailored for college students preparing for the future job market, utilizing Ranking Hidden Chain Deep Learning (RHC-DL). The algorithm aims to provide personalized recommendations by analysing students' skills, qualifications, interests, and career aspirations. Through simulated experiments and empirical validations, the efficacy of the RHC-DL-enhanced recommendation algorithm is evaluated. Results demonstrate significant improvements in recommendation accuracy and relevance compared to traditional methods. For instance, students using the RHC-DL algorithm reported a 40% increase in job offer acceptance rates and a 30% improvement in job satisfaction levels. Additionally, the algorithm adapts and learns from user interactions, continuously refining its recommendations based on real-time feedback.

Evolving Knowledge Graph Representation Learning with Multiple Attention Strategies for Citation Recommendation System

The growing number of publications in the field of artificial intelligence highlights the need for researchers to enhance their efficiency in searching for relevant articles. Most paper recommendation models either rely on simplistic citation relationships among papers or focus on content-based approaches, both of which overlook interactions within academic networks. To address the aforementioned problem, knowledge graph embedding (KGE) methods have been used for citation recommendations because recent research proves that graph representations can effectively improve recommendation model accuracy. However, academic networks are dynamic, leading to changes in the representations of users and items over time. The majority of KGE-based citation recommendations are primarily designed for static graphs, thus failing to capture the evolution of dynamic knowledge graph (DKG) structures. To address these challenges, we introduced the evolving knowledge graph embedding (EKGE) method. In this methodology, evolving knowledge graphs are input into time-series models to learn the patterns of structural evolution. The model has the capability to generate embeddings for each entity at various time points, thereby overcoming limitation of static models that require retraining to acquire embeddings at each specific time point. To enhance the efficiency of feature extraction, we employed a multiple attention strategy. This helped the model find recommendation lists that are closely related to a user’s needs, leading to improved recommendation accuracy. Various experiments conducted on a citation recommendation dataset revealed that the EKGE model exhibits a 1.13% increase in prediction accuracy compared to other KGE methods. Moreover, the model’s accuracy can be further increased by an additional 0.84% through the incorporation of an attention mechanism.

A long-tail alleviation post-processing framework based on personalized diversity of session recommendation

Session-based recommendation leverages the short-term interaction sequence to predict the next item a user is most likely to click on. Generally, in real applications, users often click on different types of items in the same session, which makes the items in the sequence present diversity, and degree of diversity vary with different sequences, that is, there is a phenomenon of personalized diversity. However, the majority of existing session recommendation models primarily focus on improving recommendation accuracy and overlooking the importance of personalized diversity, which leads to the issue of filtering bubbles. In order to alleviate the long-tail effect caused by the filtering bubble, the recommendation system should not only consider the accuracy of recommended items, but also the diversity of users’ demands for recommended items. To address this issue, this paper proposes a model, named as LAP-SR, a Long-tail Alleviation Post-processing framework based on personalized diversity of Session Recommendation. After obtaining the initial recommendation list through the selected initial session model, LAP-SR model generates the long-tail item set and item graph according to session contents. Besides, from the item graph, the model calculates the embedding representation of items, from which the diversity of all sessions is calculated. By combining the obtained diversity and the long-tail item set, the initial recommendation list is soft processed. On this basis, the model yields the final recommendation list, which alleviates the overall long-tail effect. We verified that as a post-processing framework, the proposed model can be applied to various session recommendation models. Extensive experiments on the three datasets of Diginetica, Tmall and Yoochoose1_64 demonstrate that the LAP-SR model has a competitive advantage over the baseline models in mitigating the long-tail effect.

Hybrid knowledge-infused collaborative filtering for enhanced movie clustering and recommendation

This article proposes an enhanced knowledge-based collaborative filtering model for movie recommendation services to address the limitations of collaborative filtering in capturing the diverse preferences and specific characteristics of movies. The proposed model integrates external knowledge sources, such as movie plots and reviews, to enrich the recommendation process. By leveraging this additional information, the model can better understand movies’ unique features and attributes, improving recommendation accuracy and relevance. The knowledge-based features are extracted and incorporated into the collaborative filtering framework, enhancing the model’s ability to match user preferences with movie characteristics. Experiments are conducted using the MovieLens dataset to evaluate the proposed model. The MAE and RMSE metrics are employed to assess the quality of recommendations. Comparative analyses are conducted against various baseline approaches, including popularity-based, CF-based, content-based, and hybrid recommendation models. The experimental results demonstrate the effectiveness of the proposed knowledge-based collaborative filtering model. The proposed model consistently outperforms the baselines, providing more accurate and personalized recommendations.

A qualitative analysis of knowledge graphs in recommendation scenarios through semantics-aware autoencoders

Knowledge Graphs (KGs) have already proven their strength as a source of high-quality information for different tasks such as data integration, search, text summarization, and personalization. Another prominent research field that has been benefiting from the adoption of KGs is that of Recommender Systems (RSs). Feeding a RS with data coming from a KG improves recommendation accuracy, diversity, and novelty, and paves the way to the creation of interpretable models that can be used for explanations. This possibility of combining a KG with a RS raises the question whether such an addition can be performed in a plug-and-play fashion – also with respect to the recommendation domain – or whether each combination needs a careful evaluation. To investigate such a question, we consider all possible combinations of (i) three recommendation tasks (books, music, movies); (ii) three recommendation models fed with data from a KG (and in particular, a semantics-aware deep learning model, that we discuss in detail), compared with three baseline models without KG addition; (iii) two main encyclopedic KGs freely available on the Web: DBpedia and Wikidata. Supported by an extensive experimental evaluation, we show the final results in terms of accuracy and diversity of the various combinations, highlighting that the injection of knowledge does not always pay off. Moreover, we show how the choice of the KG, and the form of data in it, affect the results, depending on the recommendation domain and the learning model.

A recommendation model for e-commerce platforms oriented to explicit information compensation and hidden information mining

In an e-commerce platform recommendation system, accurately mining users’ preferences and hidden information has always been an important initiative. In this study, based on the potential preferences and hidden characteristics of users, we propose a recommendation model for e-commerce platforms oriented to explicit information compensation and hidden information mining. First, considering the advantages of the VGAE (variational Graph Auto-Encoder) in reconstructing data samples, we propose an EI-VGAE (Explicit Information-Variational Graph Auto-Encoder) to compensate for the lack of explicit feedback. Second, to address the problem that hidden information in e-commerce platforms is difficult to mine, an xDeepFM (eXtremely Deep Factorization Machine) is used to jointly learn higher-order interaction features explicitly and implicitly. A time decay function is also introduced to reduce the impact of higher-order interaction features on the representation of hidden information. Finally, considering the advantages of the attention mechanism for obtaining implicit relational attention and the quantification of the user’s degree of preference in local representation, a GAT (Graph Attention Network)-based recommendation model for the e-commerce platform EI-GAT (Explicit and Implicit Graph Attention Network) is proposed. Our experiments show that the proposed method effectively alleviates the problem of low recommendation accuracy owing to insufficient explicit feedback from consumers and further improves recommendation accuracy by quantifying user preferences.

Research of online courses recommendation based on deep learning

This paper delves into leveraging deep learning techniques, such as graph neural networks (GNNs), Transformer, and techniques in Large Language Models (LLMs), to enhance course recommendation systems in e-learning platforms. Recommendation methods have some short-comes in the case of online course with less information and choic less logic. Our research proposes novel algorithms that use graph collaborative filtering and sequential recommendation to improve recommendation accuracy and personalization. By analyzing user behavior patterns and course attributes, our approach aims to provide smarter and more efficient course recommendation services, ultimately enhancing learning outcomes and experiences in e-learning environments. This research not only contributes to the advancement of e-learning technology but also provides valuable insights for the broader application of deep learning in smart education.

Research on System Development of Spatial Clustering in Tourism Recommendation

Abstract This paper investigates the application of exploring spatial clustering techniques in travel recommendation systems to improve recommendation accuracy and user satisfaction. A system that can provide personalized travel information has been designed by analyzing the spatial data and behavioral patterns of travelers. With this approach, the travel experience can be optimized by considering geographic location, user preferences, and tourist attraction characteristics. User data and attraction characteristics are analyzed in this paper to obtain accurate travel recommendations. Combining the traveler’s interest matrix and the attraction area heat algorithm is used to optimize the recommendation process. This recommender system reduces the root mean square error (RMSE) and mean absolute error (MAE) by 0.487% and 21-60%, respectively, compared with the traditional algorithm in the travel recommendation error analysis, and improves the accuracy rate to 96.33%, with a significant increase in the recall rate. The spatial clustering cluster analysis demonstrates that the chosen number of clusters can effectively enhance the clustering quality and maximize the recommendation results. By effectively utilizing spatial data of users and attractions, the system can provide personalized travel recommendations and improve user experience with high accuracy and recall, demonstrating better recommendation performance than traditional methods.