Enhanced Recommender System Research Articles

The role of utilizing artificial intelligence and renewable energy in reaching sustainable development goals

Many nations want to use only renewable energy by 2050. Given the recent rapid expansion in RE use in the global energy mix and its progressive impact on the global energy sector, the evaluation and analysis of Renewable Energy's impact on achieving sustainable development goals is insufficient. Wind energy could be renewable. For smart grid supply-and-demand issues, wind power forecasting is critical. One of the biggest challenges of wind energy is its significant fluctuation and intermittent nature, which makes forecasting difficult. This study's goal is to develop data-driven models to predict wind speed and power. This paper uses Machine Learning (ML) and Deep Learning (DL) to improve a wind speed prediction and recommendation system for wind turbine power production using site climatological data. This system optimizes turbine use by selecting the right number of turbines to operate solely based on the required energy, which is related to wind power and speed, and recommends the best power station location to determine the best turbine run time. The Proposed Enhanced Recommendation System (PERS) includes the Wind Speed Prediction Module (WSPM), Wind Speed vs. Power Consumption Calculation (WSPC), and Recommendation Module (RM). Test results showed the suggested method works in run time. The XGBoost or Random Forest regressor predicted 15-day power output with 94 % accuracy and 6 % mean average percentage error.

An attention mechanism and residual network based knowledge graph-enhanced recommender system

Recommender systems enhanced by a knowledge graph (KG) have attained widespread popularity and attention in recent years. However, traditional KG-based recommender systems encounter the challenge of gradient explosion as the network depth increases. Additionally, the abundance of unreliable paths in a KG has a detrimental impact on feature representation learning. In this article, we propose a KG-enhanced recommender system based on residual network and attention mechanism, which can capture high-order connectivity and long-range dependencies of the KG. Specifically, a resource allocation approach is employed to calculate the resource amount, which is subsequently utilized to evaluate the path reliability of the KG. After completing path extraction, we employ an attention mechanism to capture semantic correlations and structural information. To leverage the KG for enhancing recommender systems, we design a deep residual network with shortcut connections, effectively amalgamating advanced and abstract features using deep neural networks. The introduction of shortcut connections not only facilitates the fitting of residual mappings but also mitigates potential issues such as gradient explosion and convergence difficulties due to excessive network depth. Extensive experiments conducted on three standard datasets over baseline methods have demonstrated the superiority of our proposed recommender system.

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.

UISA: User Information Separating Architecture for Commodity Recommendation Policy with Deep Reinforcement Learning

Commodity recommendation contributes an important part of individuals’ daily life. In this context, deep reinforcement learning methods have demonstrated substantial efficacy in enhancing recommender systems’ performance. Nevertheless, several recommender systems directly utilize original feature information as a foundational element for decision-making, which seems simplistic and low efficient. Furthermore, the incorporation of sequential decision-making adds complexity to the task of recommendation. In pursuit of maximizing the long-term sequential returns of recommender systems, our study introduces a novel architecture, named User Information Separating Architecture (UISA). This framework is tailored to align with classic reinforcement learning algorithms and aims to extract the user’s interest value through the discrete processing of both static and dynamic user information. Through integration with deep reinforcement learning, the architecture is oriented towards the maximization of long-term profit and is applicable in sequential recommendation scenarios. We conduct experimental assessments by combining the proposed architecture with proximal policy optimization (PPO) and deep deterministic policy gradient (DDPG) algorithms. The outcomes illustrate marked improvements in commodity recommendation, showcasing enhancements ranging from approximately 5% to 40% in both reward and click-through rate metrics across a self-constructed JDEnv environment and the Virtual Taobao environment. Through comparison experiments, the UISA models demonstrate comparable performance.

Integrating textual reviews into neighbor-based recommender systems

Recommender systems are developed to personalize services for each user. The focus of recommender systems is to accurately discover the unknown preferences of users. To address this issue, neighbor-based recommender systems compute a weighted average of neighbors’ known preferences to predict the unknown preferences of an active user. Its weights represent the influence of neighbors’ preferences on the active user’s ones. Previous research estimates these weights solely based on users’ observed preferences. However, preferences are commonly expressed through numerical ratings, presenting a challenge for users to grasp the rating scale and assign the most precise numerical values. Therefore, the observed rating data is characterized by sparsity, a lack of precision, and insufficient detail. In modern recommender systems, besides collecting ratings, it’s entirely possible to gather various types of information. One valuable data source proven to enhance recommender systems significantly is textual reviews authored by users following their experiences with items. In this study, we utilize Bert models to derive user vectors from observed textual reviews. These vectors are then employed to estimate weights between the active user and each neighbor in a neighbor-based recommender system. In contrast to numerical ratings, textual reviews provide a more detailed and precise representation of users’ preferences. This contributes to improving the performance of neighbor-based recommender systems.

BIKAGCN: Knowledge-Aware Recommendations Under Bi-layer Graph Convolutional Networks

Recommender systems are a popular solution for the problem of information overload, offering personalized recommendations to users. Recent years, research has aimed to enhance recommender systems by employing knowledge graphs in conjunction with Graph convolutional network (GCN) to extract user and item features. Although GCN possess a great potential, they are still far from reaching their full capability in recommender systems. This paper introduces a novel approach—knowledge-aware recommendations under bi-layer graph convolutional networks (BIKAGCN) that combines attention and bi-layer GCNs to improve performance. The first layer of the BIKAGCN model trains embedding representations of users and items based on user-item interaction graphs. The second layer introduces a novel knowledge-aware layer of attention and graph convolutional network (KAGCN) layer that leverages both the first layer’s user-item embeddings and item knowledge graph embeddings. Experimental results on three publicly available datasets (MovieLens-20M, Last-FM, and Book-Crossing) demonstrate that BIKAGCN leads to significant performance improvements in recall@20 metric (14.41%, 8.86%, and 20.90%, respectively) compared to currently available state-of-the-art approaches. Moreover, the model maintains satisfactory performance in cold-start cases.The research provides some guidance for the direction of subsequent research on recommender systems.

ML assisted enhanced recommendation system for reliable volunteering environment

The coronavirus pandemic has negatively impacted everyone around the globe. The need to follow social distancing has increased the demand for technology that makes remote volunteering accessible. Alerting, aiding, and educating individuals at risk in a privacy-preserving manner are the need of the hour. Several NGOs have taken up this responsibility but are lacking the resources to reach willing volunteers and donations. This project attempts to make a novel on-demand service application using ID3, CART, and C4.5 decision tree classification algorithms and compare their accuracies. Algorithm C4.5 here we use as a Decision Tree classifier that can be make decisions based on univariate or multivariate predictor. C4.5 Algorithm builds decision trees based on the conception of information gain, with the decision of each classification associated with the target classification. The best way to determine uncertainty is to use entropy that reflects the basis of decision. We aim to classify volunteer data based on predicted volunteer reliability. C4.5 algorithm shows 11.63 higher accuracy when compared to other algorithm and the value of 0.36 Gini index for the C4.5 indicates that there is an adequate equality. It is conferred that the C4.5 algorithm will best solve this problem based on the required dataset that we have created.

Facial expression-enhanced recommendation for virtual fitting rooms

With the development of Augmented Reality (AR) technology in the retail industry, virtual fitting room (VFR) are considered promising enhancement of e-commerce by providing users with an immersive environment to try on new products, especially fashion products. While allowing users having more vivid impression of products, virtual fitting rooms also offer sellers more channels to collect information on user preferences, which can be used to enhance recommender systems. This study proposes to leverage facial expression recognition technology together with fine-grained human-computer interactions in virtual fitting rooms to personalize product recommendations. This paper proposes a recommendation algorithm based on confidence setting, negative feedback sampling, and matrix factorization to model user behaviors in virtual fitting rooms. We conduct an experiment on 81 subjects to evaluate the proposed method. Experimental results show the proposed method outperforms existing methods using traditional behavior information. Our study provides a strong support to the value of AR in enhancing e-commerce.

Enhancing Recommender Systems with Semantic User Profiling through Frequent Subgraph Mining on Knowledge Graphs

Recommender systems play a crucial role in personalizing online user experiences by creating user profiles based on user–item interactions and preferences. Knowledge graphs (KGs) are intricate data structures that encapsulate semantic information, expressing users and items in a meaningful way. Although recent deep learning-based recommendation algorithms that embed KGs have demonstrated impressive performance, the richness of semantics and explainability embedded in the KGs are often lost due to the opaque nature of vector representations in deep neural networks. To address this issue, we propose a novel user profiling method for recommender systems that can encapsulate user preferences while preserving the original semantics of the KGs, using frequent subgraph mining. Our approach involves creating user profile vectors from a set of frequent subgraphs that contain information about user preferences and the strength of those preferences, measured by frequency. Subsequently, we trained a deep neural network model to learn the relationship between users and items, thereby facilitating effective recommendations using the neural network’s approximation ability. We evaluated our user profiling methodology on movie data and found that it demonstrated competitive performance, indicating that our approach can accurately represent user preferences while maintaining the semantics of the KGs. This work, therefore, presents a significant step towards creating more transparent and effective recommender systems that can be beneficial for a wide range of applications and readers interested in this field.

FPLV: Enhancing recommender systems with fuzzy preference, vector similarity, and user community for rating prediction.

Rating prediction is crucial in recommender systems as it enables personalized recommendations based on different models and techniques, making it of significant theoretical importance and practical value. However, presenting these recommendations in the form of lists raises the challenge of improving the list's quality, making it a prominent research topic. This study focuses on enhancing the ranking quality of recommended items in user lists while ensuring interpretability. It introduces fuzzy membership functions to measure user attributes on a multi-dimensional item label vector and calculates user similarity based on these features for prediction and recommendation. Additionally, the user similarity network is modeled to extract community information, leading to the design of a set of corresponding recommendation algorithms. Experimental results on two commonly used datasets demonstrate the effectiveness of the proposed algorithm in enhancing list ranking quality, reducing prediction errors, and maintaining recommendation diversity and accurate user preference classification. This research highlights the potential of integrating heuristic methods with complex network theory and fuzzy techniques to enhance recommendation system performance with interpretability in mind.

Justification vs. Transparency: Why and How Visual Explanations in a Scientific Literature Recommender System

Significant attention has been paid to enhancing recommender systems (RS) with explanation facilities to help users make informed decisions and increase trust in and satisfaction with an RS. Justification and transparency represent two crucial goals in explainable recommendations. Different from transparency, which faithfully exposes the reasoning behind the recommendation mechanism, justification conveys a conceptual model that may differ from that of the underlying algorithm. An explanation is an answer to a question. In explainable recommendation, a user would want to ask questions (referred to as intelligibility types) to understand the results given by an RS. In this paper, we identify relationships between Why and How explanation intelligibility types and the explanation goals of justification and transparency. We followed the Human-Centered Design (HCD) approach and leveraged the What–Why–How visualization framework to systematically design and implement Why and How visual explanations in the transparent Recommendation and Interest Modeling Application (RIMA). Furthermore, we conducted a qualitative user study (N = 12) based on a thematic analysis of think-aloud sessions and semi-structured interviews with students and researchers to investigate the potential effects of providing Why and How explanations together in an explainable RS on users’ perceptions regarding transparency, trust, and satisfaction. Our study shows qualitative evidence confirming that the choice of the explanation intelligibility types depends on the explanation goal and user type.

An enhanced recommender system based on heterogeneous graph link prediction

Graph-based data has gained popularity in various applications, including social networks, recommendation systems, and knowledge graphs. Detecting missing links between nodes is a critical challenge in processing graph-based data, as it directly impacts the performance of these applications. The framework for inductive representation learning on large graphs called GraphSAGE method is commonly used for identifying node embeddings, but it has limitations in terms of sensitivity to hyperparameters and difficulties in processing large graphs and capturing global graph information. To address these issues, we propose an improved GraphSAGE architecture for link prediction. Our approach leverages advanced aggregation functions and diverse neural network architectures. Through extensive experimentation on benchmark datasets, we demonstrate the superiority of our method in terms of link prediction accuracy compared to standard GraphSAGE. Additionally, by integrating clustering techniques, we develop a robust recommender system that outperforms state-of-the-art techniques. Our findings highlight the practical potential of our methodology for recommendation tasks and underline its significance in graph-based data processing.

A novel KG-based recommendation model via relation-aware attentional GCN

Leveraging knowledge graphs (KGs) to enhance recommender systems has gained considerable attention, with researchers obtaining user preferences by aggregating entity pairs with explicit relations in KGs via graph convolutional networks (GCNs). Existing approaches currently overlook many entity pairs without relations, which, however, may have potentially useful information. To address this issue, we propose a novel relation-aware attentional GCN (RAAGCN) with the following improvements over vanilla GCNs: (1) it aggregates all entity pairs with and without explicit relations and (2) it distinguishes the importance of different relational context information. Based on the proposed RAAGCN, we further propose a user preference and item attractiveness capturing model (UPIACM) for KG-based recommendation. In the UPIACM, the user preference is decomposed into interest and rating preferences. The interest preference is the user’s interest taste toward the items with specific features, while the rating preference reflects the intention of rating high or low. Additionally, our model accounts for item attractiveness, which reflects an item’s popularity among users. Additionally, we incorporate a gated filtering mechanism to further improve our model’s performance. Through extensive experiments, we show that the proposed UPIACM outperforms state-of-the-art baseline methods.

Enhancing Recommender Systems by Fusing Diverse Information Sources through Data Transformation and Feature Selection

Enhancing Recommender Systems by Fusing Diverse Information Sources through Data Transformation and Feature Selection

이종 데이터 간 관계 모델링을 통한 개인화 추천 시스템의 지식 그래프 확장 기법

Many researchers have investigated ways to enhance recommender systems by integrating heterogeneous data to address the data sparsity problem. However, only a few studies have successfully integrated heterogeneous data using knowledge graph. Additionally, most of the knowledge graphs built in these studies only incorporate explicit relationships between entities and lack additional information. Therefore, we propose a method for expanding knowledge graphs by using deep learning to model latent relationships between heterogeneous data from multiple knowledge bases. Our extended knowledge graph enhances the quality of entity features and ultimately increases the accuracy of predicted user preferences. Experiments using real music data demonstrate that the expanded knowledge graph leads to an increase in recommendation accuracy when compared to the original knowledge graph.

Exploring indirect entity relations for knowledge graph enhanced recommender system

Knowledge graph (KG)-based recommendation models generally explore auxiliary information to alleviate the sparsity and cold-start problems in recommender systems. Previous approaches enhance representations of users and items by exploring the influence of multi-hop neighbors. However, existing works fail to consider the indirect feedback for improving user representation and the diversity of the multi-hop neighbors for enriching item representation. To this end, we present a novel recommender system, called Entity Relation Similarity and Indirect Feedback-based Knowledge graph enhanced Recommendation (ERSIF-KR) to enhance representation learning in KG-based recommender systems. In addition, our model exploits indirect feedback of items that are not directly interacted with users to alleviate the exposure bias while enhancing user similarity computation when learning user representation. Moreover, our method directly incorporates representation of multi-hop neighbors into the target item embedding with weights determined by the correlations between high-order and low-order relations, which can significantly boost the item representation learning. Extensive experiments on three real-world datasets demonstrate that our model achieves remarkable gains in terms of recommendation performance and model convergence time, and effectively alleviates the sparsity and cold start problems.

A blockchain framework data integrity enhanced recommender system

AbstractRecommender system for the IoT (RSIoT) has attracted considerable attention. By leveraging emerging technologies such as the Internet of Things (IoT), artificial intelligence, and blockchain, RSIoT improves various indicators of residents' life. However, data integrity threats may affect the accuracy and consistency of the data particularly in the IoT environment where most devices are inherently dynamic and have limited resources that could fail in ensuring the quality of data transmission. Prior work has focused on processing big data and ensuring their integrity by considering cloud storage service as the popular way. In this article, we address integrity of data leveraging blockchain capabilities to ensure the integrity of the critical data. We adapted the Ethereum blockchain to our RCS for ensuring integrity of data during sharing them between doctor and patient without handling their data by third party. We build four smart contracts that enable our system of gaining more advantage of blockchain. We evaluated the performance of our smart contracts in Kovan and Rinkeby test networks. The preliminary results show the feasibility and effectiveness of the proposed solution.

What social characteristics enhance recommender systems? The effects of network embeddedness and preference heterogeneity

What social characteristics enhance recommender systems? The effects of network embeddedness and preference heterogeneity

An Enhanced Multi-Modal Recommendation Based on Alternate Training With Knowledge Graph Representation

Deep network recommendation is a cutting-edge topic in current recommendation system research, which as a combination of recommendation systems and deep learning theory can effectively improve recommendation accuracy. In a real recommendation scenario, all the effective information in a data set should be extracted, both explicit and implicit, because the comprehensive degree of information is proportional to the recommendation performance. This article proposes an enhanced multi-modal recommendation based on alternate training with knowledge graph representation (SI-MKR) based on the MKR deep learning recommendation model. Our framework is an enhanced recommendation system based on knowledge graph representation, using valuable external knowledge as multi-modal information. The SI-MKR model solves the problem of ignoring the diversity of data types in the multi-modal knowledge-based recommendation system, which adds user and item attribute information from a knowledge graph as an enhancement recommendation multi-tasking training. By analysing the content of the item and user attributes, the SI-MKR model classifies the attributes of the items and users, processes the text type attributes and multi-value type attributes separately for feature extraction, and other types of attributes are used as inputs to the knowledge graph embedding unit. In addition, the knowledge graph data form a triplet unit, thus continuing the knowledge graph data training process. The feature extraction unit of the knowledge graph and the recommended unit are connected through the cross-compression unit for alternate training. During the deep learning framework training process, the recommendation system’s item has a potential correlation with the head entity in the knowledge graph which embodies the idea of multi-tasking. Through extensive experiments on real-world datasets, we demonstrate that SI-MKR achieves substantial gains in movie recommendation over advanced model baselines. Even user-item interactions are sparse, SI-MKR maintains better performance than the MKR model.

Enhanced Recommendation System for E-commerce Applications

Recommendation algorithms play a quintessential role in development of E-commerce recommendation system, Where in Collaborative filtering algorithm is a major contributor for most recommendation systems since they are a flavor of KNN algorithm specifically tailored for E-commerce Web Applications, the main advantages of using CF algorithms are they are efficient in capturing collective experiences and behavior of e-commerce customers in real time, But it is noted that , this results in the phenomenon of Mathew effect, Wherein only popular products are listed into the recommendation list and lesser popular items tend to become even more scarce. Hence this results in products which are already familiar to users being discovered redundantly, thus potential discovery of niche and new items in the e-commerce application is compromised. To address this issue , this paper throws light on user behavior on the online shopping platform , accordingly a novel selectivity based collaborative filtering algorithm is proposed with innovator products that can recommend niche items but less popular products to users by introducing the concept of collaborative filtering with consumer influencing capability. Specifically, innovator products are a special subset of products which are less popular/ have received less traction from users but are genuinely of higher quality, therefore, these aforementioned products can be captured in the recommendation list via innovator-recognition table, achieving the balance between popularity and practicability for the user