Improve Recommendation Accuracy Research Articles

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.

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.

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.

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 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.

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 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.

An E-Commerce Personalized Recommendation Algorithm Based on Multiple Social Relationships

Environmental e-commerce is a sustainability-oriented e-commerce model. To address the problem of data sparsity and the lack of diversity in traditional e-commerce recommendation algorithms, a new collaborative filtering recommendation algorithm based on multiple social relationships is proposed in environmental e-commerce. In real social networks, there were many relationships between users. On the basis of the traditional matrix decomposition model, the proposed algorithm integrates multiple social relationships between users into the user feature matrix, and then the multiple social relationships between users and the user rating preference similarity were used to jointly predict the user’s rating value for commodity, thus the personalized recommendation for users was achieved. In order to verify the superiority of the proposed algorithm, in this paper, two open datasets were used to compare the performance of several recommendation algorithms. The experimental results show that compared with the traditional social recommendation algorithms, the proposed algorithm improves recommendation accuracy and diversity. In real environmental e-commerce recommendation systems, the proposed algorithm can provide users with more personalized recommendation results, and reduce the arbitrariness of customer purchases and frequent returns in reality.

Features of Recommendation Algorithm on Base of Analysis of Social Network Data Mining Methods

In recent years, social media platforms have become powerful data collection tools to improve user experience. The vast amount of data generated through social media provides a unique opportunity to develop innovative recommendation systems. This article analyzes the application of data mining methods for social networks in the context of effective recommendation systems, focusing on three key methodologies: sentiment analysis (SA), topic modeling (TM) and social network analysis (SNA), highlighting their positive features. SA allows the system to tailor recommendations based on sentiment analysis, offering users items that match their expressed emotions. Experiments show significant improvements in recommendation accuracy when sentiment data is integrated. TM allows the system to understand the main concerns of users by identifying dominant themes, thereby providing personalized recommendations and staying abreast of evolving trends. At the same time, AFM identifies influential users and communities, increasing relevance and awareness of system elements. The article highlights the enormous potential of social networks for the development of effective, personalized recommendation systems. Using sentiment analysis and topic modeling, these systems can provide personalized and relevant recommendations based on public sentiment, trending topics, and social network dynamics.

A Service Recommendation System Based on Dynamic User Groups and Reinforcement Learning

Recently, advancements in machine-learning technology have enabled platforms such as short video applications and e-commerce websites to accurately predict user behavior and cater to their interests. However, the limited nature of user data may compromise the accuracy of these recommendation systems. To address personalized recommendation challenges and adapt to changes in user preferences, reinforcement-learning algorithms have been developed. These algorithms strike a balance between exploring new items and exploiting existing ones, thereby enhancing recommendation accuracy. Nevertheless, the cold-start problem and data sparsity continue to impede the development of these recommendation systems. Hence, we proposed a joint-training algorithm that combined deep reinforcement learning with dynamic user groups. The goal was to capture user preferences for precise recommendations while addressing the challenges of data sparsity and cold-start. We used embedding layers to capture representations and make decisions before the reinforcement-learning process, executing this approach cyclically. Through this method, we dynamically obtained more accurate user and item representations and provide precise recommendations. Additionally, to address data sparsity, we introduced a dynamic user grouping algorithm that collectively enhanced the recommendations using group parameters. We evaluated our model using movie-rating and e-commerce datasets. As compared to other baseline algorithms, our algorithm not only improved recommendation accuracy but also enhanced diversity by uncovering recommendations across more categories.

A Knowledge Graph-Based Many-Objective Model for Explainable Social Recommendation

As an application area of social computing, social recommendation aims to exploit the richness of social relationships to improve recommendation accuracy. Current research mainly considers social information that can be directly observed, with little attention to indirect social relationships between users or the explainability of social recommendation results. To address this challenge, this article proposes a knowledge graph-based many-objective model for explainable social recommendation (KGMESR) by considering the explainability, accuracy, novelty, and content quality of social recommendation results. The model takes advantage of social behavior information to calculate user similarity and quantifies the explainability of social recommendation results using entity vectors and embedding vectors. To ensure model efficiency, a many-objective recommendation algorithm based on the partition deletion strategy is designed. It employs the association of individuals with the nearest reference vector to render the diversity of the population and then obtains the final solution by deleting poorly converged individuals in each partition. Experimental results show that many-objective optimization recommendation algorithm based on partition deletion strategy (MaOEA-PDS) allows for preferable recommendation results for users in real datasets. The explainability of the proposed model is demonstrated by two case studies.

ALBERT-Based Personalized Educational Recommender System: Enhancing Students’ Learning Outcomes in Online Learning

Online learners must navigate vast educational resources to find materials that meet their needs. This study introduces an ALBERT-based personalized educational recommender system to improve student learning. ALBERT (A Lite BERT), an optimized variant of the BERT algorithm, captures contextualized word representations and understands the semantic meaning of learning resources, student profiles, and interactions. This study evaluates the ALBERT-based recommender system’s personalized learning recommendations. To assess learning outcomes, a diverse group of students from different educational domains is evaluated. Before and after the recommender system, academic performance, knowledge retention, and engagement are assessed. User satisfaction surveys assess recommendation quality, relevance, and user experience. The recommender system uses ALBERT’s model optimization to improve recommendation accuracy, learner engagement, and personalized learning. The evaluation shows the ALBERT-based personalized recommender system improves online learning outcomes. System-generated recommendations boost student engagement, knowledge retention, and academic performance. User satisfaction surveys show that the ALBERT-based system meets learners’ needs by providing relevant and high-quality recommendations. This research shows how advanced deep learning algorithms like ALBERT can improve personalized online learning. ALBERT’s optimized training and inference speeds up the recommender system’s scalability. This empowers learners to access tailored and high-quality educational resources, maximizing their learning outcomes and potential in online learning.

Low Dimensional Relevance Coding for Personalized Tag Recommendation in Image Tagging Applications

An approach of image coding for tag recommendation based on feature clustering and weighted coding is presented in this paper. The existing tag recommendation approach develops a decision based on correlation of image features and their tag annotated. The descriptive feature of the image sample defines the content of an image and is correlated with database features for tag recommendation. The feature dimension and its representation have a greater impact on the recommendation performance. The recent method tag recommendation developed CNN based visual features and proposed a tag recommendation based on weight factor. The dimensional feature and the isolated weight allocation limit the performance of presented tag recommendation system. This paper presents a new weight allocation and feature clustering method for tag recommendation. An approach of integral coding for weighted image-tag is presented to improve recommendation accuracy. The proposed recommendation system performance is tested on Flickr dataset for retrieval and recommendation accuracy.

Attributed network embedding based on self-attention mechanism for recommendation method

Network embedding is a technique used to learn a low-dimensional vector representation for each node in a network. This method has been proven effective in network mining tasks, especially in the area of recommendation systems. The real-world scenarios often contain rich attribute information that can be leveraged to enhance the performance of representation learning methods. Therefore, this article proposes an attribute network embedding recommendation method based on self-attention mechanism (AESR) that caters to the recommendation needs of users with little or no explicit feedback data. The proposed AESR method first models the attribute combination representation of items and then uses a self-attention mechanism to compactly embed the combination representation. By representing users as different anchor vectors, the method can efficiently learn their preferences and reconstruct them with few learning samples. This achieves accurate and fast recommendations and avoids data sparsity problems. Experimental results show that AESR can provide personalized recommendations even for users with little explicit feedback information. Moreover, the attribute extraction of documents can effectively improve recommendation accuracy on different datasets. Overall, the proposed AESR method provides a promising approach to recommendation systems that can leverage attribute information for better performance.