Improve Recommendation Accuracy Research Articles

Extracting Implicit User Preferences in Conversational Recommender Systems Using Large Language Models

Conversational recommender systems (CRSs) have garnered increasing attention for their ability to provide personalized recommendations through natural language interactions. Although large language models (LLMs) have shown potential in recommendation systems owing to their superior language understanding and reasoning capabilities, extracting and utilizing implicit user preferences from conversations remains a formidable challenge. This paper proposes a method that leverages LLMs to extract implicit preferences and explicitly incorporate them into the recommendation process. Initially, LLMs identify implicit user preferences from conversations, which are then refined into fine-grained numerical values using a BERT-based multi-label classifier to enhance recommendation precision. The proposed approach is validated through experiments on three comprehensive datasets: the Reddit Movie Dataset (8413 dialogues), Inspired (825 dialogues), and ReDial (2311 dialogues). Results show that our approach considerably outperforms traditional CRS methods, achieving a 23.3% improvement in Recall@20 on the ReDial dataset and a 7.2% average improvement in recommendation accuracy across all datasets with GPT-3.5-turbo and GPT-4. These findings highlight the potential of using LLMs to extract and utilize implicit conversational information, effectively enhancing the quality of recommendations in CRSs.

Individualized recommending exercises and improving DINA algorithm by CLP-parameter adjustments

The traditional mode of education has been integrated with the technologies of internet to give rise to smart and or intelligent assistant education. A recommender “recommending items to customers” has been applied with great success in many commercial platforms, such as Amazon, Google, JD.com, and Tiktok, but there still has been no such an assisted learning system that effectively “recommending exercises (as the items) to learners (as the customers)”. This paper addresses the problems and challenges in applying recommender technology to the design of an intelligence-assisted learning system for individualized exercises recommendation. After providing an overview with some analysis on state-of-the-art researches, a model is proposed to improve DINA algorithm by four adjustments with changeable parameters of cognitive level perception (CLP), together with three primary steps of implementation in practice. It aims to improve recommendation accuracy of the mostly-concerned DINA algorithm based on widely-concerned cognitive diagnosis method (CDM). The proposed algorithm would have better performances on individualized exercises recommendation due to better CLP-capability. Another part of this study will provide further verification of the performances.

Optimizing Personalized and Context-Aware Recommendations in Pervasive Computing Environments

The researchers in the current era provided many new recommendation methodologies. Though various recommendation techniques exist, there is a need to develop a unique technique for capturing latent factors and patterns from sparse and high-dimensional data in pervasive environments, specifically for optimizing dynamic recommendations. This study proposes a hybrid approach for optimizing dynamic recommendations in pervasive environments by combining Non-Negative Matrix Factorization (NMF) with deep learning and reinforcement learning techniques. The goal is to overcome the challenge of capturing latent factors and patterns from sparse and high-dimensional data. By leveraging NMF, meaningful latent factors are extracted, while deep learning, specifically Faster recurrent neural networks (FRNNs), learns complex feature representations. Reinforcement learning algorithms optimize the recommendation policy based on user feedback. This Hybrid Context-Aware Optimized Recommendation (HCOR) approach improves recommendation accuracy and relevance in pervasive environments, adapts to changing contexts, and enhances user experiences. The performance benefits are achieved by effectively capturing latent factors and patterns, resulting in improved accuracy and the ability to provide personalized and context-aware recommendations. The performance indicators to validate the research work include the recommendations' accuracy, relevance, and adaptability in pervasive environments. Additionally, metrics, such as precision, recall, and F1-score, are used to evaluate the effectiveness of the hybrid approach in capturing latent factors and patterns. User feedback and satisfaction are also measured to assess the impact on user experiences. The HCOR approach shows substantial performance gains, measuring a precision of 0.932, a recall of 0.922, and an F1-score of 0.943, which indicates the excellent ability of the approach to deliver accurate and personalized recommendations in a pervasive environment.

Research on Music Neighborhood-Based Recommendation Algorithms

This paper presents a comprehensive review of recommendation algorithms in the music domain. Music recommendation systems play a crucial role in delivering personalized content to users by analyzing patterns and preferences. We analyze several prominent recommendation algorithms, including collaborative filtering, content-based filtering, and hybrid models, each of which has been extensively applied to music recommendation tasks. The study also delves into the emerging techniques of deep learning and reinforcement learning in improving recommendation accuracy. A comparative analysis is provided, highlighting the strengths and limitations of each algorithm in terms of scalability, accuracy, and computational efficiency. Special attention is given to the challenges of balancing personalization and diversity in music recommendations. Furthermore, we explore how hybrid recommendation systems, which combine multiple methods, can overcome the limitations of individual models. The review concludes by identifying future research directions in the field of music recommendation systems, particularly the potential of leveraging more advanced machine learning techniques.

ENSG: Enhancing Negative Sampling in Graph Convolutional Networks for Recommendation Systems

In the field of recommendation, negative samples that are close to positive samples are referred to as “hard negative samples”. These hard negative samples are more likely to be incorrectly recommended to users. Therefore, researching how to enhance the ability to identify hard negative samples and thereby improve recommendation accuracy is an important direction in the field of recommendation systems. To address this issue, we propose a new model named Enhancing Negative Sampling in Graph Convolutional Networks for Recommendation Systems (ENSG). Firstly, part of the information in the positive sample was injected into the negative sample, and the high-quality, difficult negative sample was selected by the inner product method. Secondly, a lightweight graph convolutional network is used for feature extraction to obtain node representations for both users and items. Then, contrastive learning is used to maximize the similarity between the hard negative samples and the positive samples, so that the hard negative samples are as close to the positive samples as possible in the feature space. Ultimately, the model is trained by taking hard negative samples as input and minimizing the loss function to optimize model parameters; the recommendation accuracy of the model is enhanced. To validate the effectiveness of our approach, we conducted comparative evaluations utilizing three publicly accessible datasets. The experimental outcomes indicate that our proposed model surpasses the baseline model in terms of performance. On three public datasets, the algorithm proposed in this paper achieved the highest improvements of 16.27% and 12.72% in the evaluation metrics Recall@20 and NDCG@20, respectively.

Integrating contrastive learning and adversarial learning on graph denoising encoder for recommendation

In this era of information explosion, recommender systems have become increasingly vital in research and everyday applications. Collaborative filtering and matrix factorization are foundational techniques in this domain, while they encounter limitations in capturing complex user behavior and managing sparse data. Graph Convolutional Networks (GCNs) have emerged as a superior alternative. However, challenges for GCNs remain, particularly regarding data sparsity and interaction noise. We propose a Contrastive Adversarial Graph Denoising Encoder (CAGDE), which conducts contrastive learning and adversarial training into a Graph Denoising Encoder (GDE) framework to address these issues. This approach eliminates the necessity for neighborhood aggregation to address the over-smoothing problem and focuses on essential graph features by adding different types of noise to enhance robustness. Extensive experiments were conducted on five benchmark datasets, and the proposed solution, CAGDE, demonstrates significant improvements in recommendation accuracy and robustness against noise. Our implementations are available at https://github.com/Azunyan-bot/CAGDE.

Understanding Before Recommendation: Semantic Aspect-Aware Review Exploitation via Large Language Models

Recommendation systems harness user-item interactions like clicks and reviews to learn their representations. Previous studies improve recommendation accuracy and interpretability by modeling user preferences across various aspects and intents. However, the aspects and intents are inferred directly from user reviews or behavior patterns, suffering from the data noise and the data sparsity problem. Furthermore, it is difficult to understand the reasons behind recommendations due to the challenges of interpreting implicit aspects and intents. To address these constraints, we harness the sentiment analysis capabilities of Large Language Models (LLMs) to enhance the accuracy and interpretability of the conventional recommendation methods. Specifically, inspired by the deep semantic understanding offered by LLMs, we introduce a chain-based prompting strategy to uncover semantic aspect-aware interactions, which provide clearer insights into user behaviors at a fine-grained semantic level. To incorporate the rich interactions of various aspects, we propose the simple yet effective Semantic Aspect-based Graph Convolution Network (short for SAGCN). By performing graph convolutions on multiple semantic aspect graphs, SAGCN efficiently combines embeddings across multiple semantic aspects for final user and item representations. The effectiveness of the SAGCN was evaluated on four publicly available datasets through extensive experiments, which revealed that it outperforms all other competitors. Furthermore, interpretability analysis experiments were conducted to demonstrate the interpretability of incorporating semantic aspects into the model.

The HYBRID CONTENT-BASED FILTERING AND CLASSIFICATION RNN WITH PARTICLE SWARM OPTIMIZATION FOR TOURISM RECOMMENDATION SYSTEM

Economic recovery in the tourism sector after the COVID-19 pandemic is one of the main focuses of the Indonesian government at the moment, especially in Bandung City. This research aims to develop a personalized tourist spot recommendation system, by addressing the gaps in the existing literature through the integration of Content-Based Filtering (CBF) and Simple Recurrent Neural Network (RNN) methods that aim to improve recommendation accuracy. This study uses a hybrid approach that combines Term Frequency - Inverse Document Frequency (TF-IDF) and word embedding with the Robustly Optimized BERT (RoBERTa) model to identify similarities between tourist destinations based on their content characteristics. Simple RNN is used to analyze user preference patterns over time, which is then further optimized using Particle Swarm Optimization (PSO). As a result, the Simple RNN model that has been optimized with PSO shows an increased accuracy of up to 94.37%, outperforming other optimizations such as Adam and SGD. This research makes a novel contribution by applying advanced machine learning techniques to improve personalization in travel recommendation systems.

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. Received: 28 April 2024 | Revised: 12 June 2024 | Accepted: 8 July 2024 Conflicts of Interest The authors declare that they have no conflicts of interest to this work. Data Availability Statement The MovieLens10M data set that support the findings of this study are openly available at https://doi.org/10.1145/2827872, reference number [48]. The MovieLens-2k data set that support the findings of this study are openly available in Grouplens at https://grouplens.org/datasets/hetrec-2011/. Author Contribution Statement Kexin Yin: Conceptualization, Methodology, Software, Validation, Formal analysis, Investigation, Resources, Data Curation, Writing – original draft, Writing – review & editing, Visualization, Supervision, Project administration, Funding acquisition. Junqi Zhao: Writing – original draft, Writing – review & editing, Visualization, Project administration.

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.