Traditional Recommendation Methods Research Articles

E-commerce and Mobile Application Development in the Sports Industry

In order to achieve user recommendations that best match their current contextual needs, the author proposes a mobile service QoS (Quality of Service) hybrid recommendation model based on sports user situational awareness. Cluster the users and service items covered by mobile users based on their location contextual information according to the classification principle of autonomous systems, forming a collaborative filtering and recommendation mechanism for mobile user service items; In response to the cold start problem of new users and new projects in traditional QoS recommendation methods, prediction and recommendation of missing QoS attribute preference values are based on User based and Item based CF; In response to the problem of difficult determination of mixed recommendation weights caused by massive data and uneven distribution of service QoS attribute values in mobile network environments, MapReduce based ant colony neural network weight training is used for CF mixed recommendation. Experimental results have shown that the Hadoop sports industry has improved the operational efficiency of algorithms and reduced the time for global QoS preference prediction; And by comparing the operation results of the 10% and 100% sub datasets, it can also be seen that the acceleration ratio of the algorithm will continuously improve with the increase of data volume, thereby improving the operational efficiency of the recommendation algorithm. It has been proven that the MapReduce based ant colony neural network weight training method significantly reduces the global computation time of the algorithm and improves the operational efficiency of the recommendation system.

REVOLUTIONIZING TREND RECOMMENDATIONS: A DEEP LEARNING APPROACH FOR IMAGE-BASED INSIGHTS

The rapid increases in visual content creation across different sectors, starting from social media to even e-commerce, have had a major impact on their respective users, who want bespoke experiences: preferences are recognized and satisfied by recommendations contextualized within a specific sense of the world. Over many years, traditional recommendation methods took into consideration only textual content, user profiles, or purchase history as the material for recommendations. As images become the staple of engagement in the digital world, the old system misses those very nuances of visual preference that affect choice through color, texture, and style.This shift has paved way for image-based recommendation systems. Indeed, these are further dependent on powerful machine learning and deep learning models in the analysis of visual data to discern meaningful patterns in drawing recommendations in line with a user's visual taste. In such industries as fashion and design, social media, style and aesthetic become much stronger drivers of engagement.We propose in this paper a sophisticated AI-based Trend Recommendation System, which combines deep learning model feature extraction abilities with the precision of the Nearest Neighbor Search algorithms. Our system has at its core the ResNet50 model, a widely recognized CNN which is associated with superior ability to analyze images. This is achieved using ResNet50 to extract features from the images at a deep level so that their style and trend may be characterized with intricate visual characteristics. These features are then compared to a large curated dataset using Nearest Neighbor Search, in turn ensuring that the recommended images are not only visually relevant but also contextually accurate.Our research expands ResNet50 by evaluating other state-of-the-art CNN models, including VGG16, InceptionV3, and MobileNetV2. These models are tested on their ability to extract features, provide recommendations with accuracy, and consume fewer computations. By comparing these models, we will know which one is more effective for real applications for speed and precision.This study is a landmark in developing an image-based recommender system and finds how deep learning has the transforming capability to make experience highly personalized and visually oriented.

Multi-task Information Enhancement Recommendation model for educational Self-Directed Learning System

In Self-Directed Learning Systems (SDLS), suggesting specific exercises to students after viewing educational videos is crucial, which promotes the integration of learning resources and improves learning efficiency. Given that exercises and educational videos are distinct learning resources, suggesting exercises centered on educational videos containing knowledge concepts is referred to as cross-type recommendations. Traditional cross-type recommendation methods usually separate data representation and recommendation tasks, which hampers effective knowledge transfer and information fusion. Furthermore, educational resources frequently feature an irregular distribution of information, posing a significant challenge to existing models in extracting crucial information. Besides, due to the inadequate data, the cross-type recommendation system needs help in developing a robust model. To address these challenges, we propose a Multi-task Information Enhancement Recommendation (MIER) Model in this paper. This model provides a comprehensive recommendation capability by integrating resource representation and merging recommendations. Multi-task learning improves the capacity to integrate information and transfer knowledge. Then, we propose an Information Enhancement Encoder (IEE) module that utilizes the attention mechanism to enhance the model’s ability to extract essential information from educational videos and exercises. Finally, to address the problem of inadequate data, we design a fusion structure that incorporates the concept knowledge graph as expert information into the model. To evaluate the effectiveness of the MIER, we compare it with other models on both predicting concepts and recommending exercises task. The corresponding results demonstrate that the MIER outperforms all the other models. We comprehensively evaluated the MIER model’s performance across a range of datasets varying in labeled data quantity. The findings demonstrate that the model consistently achieves outstanding performance and exhibits robustness, even under conditions of limited label availability.

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.

A DQN-based traffic classification method for mobile application recommendation with continual learning

With the popularity and development of smartphones, many mobile applications of various types have emerged. How to recommend mobile applications that match the user’s preferences and usage habits among the massive applications is a problem that needs to be solved. Traditional mobile application recommendation methods cannot dynamically track user behavior and preference changes in time and cannot timely correct the recommendation model, resulting in poor recommendation effects. The continual update of mobile applications will also invalidate the recommendation model based on traffic classification. To solve these problems, this paper proposes A Deep Q-Network (DQN) based traffic classification method for mobile application recommendation with continual learning, which embeds a DQN-based traffic classification model in the mobile terminal and sets up a reward and punishment mechanism to achieve self-supervised learning. By continuously adjusting and optimizing the model, the effectiveness of the traffic classification model is ensured, and the recommendation model is provided with accurate and reliable user behavior data support. Experiments on the ISCX and private datasets show that the proposed method performs better and can effectively guarantee the accuracy of the classification model.

SR-MACL: Session-Based Recommendation with Multi-layer Aggregation Augmentation in Contrastive Learning

Session-based recommendation (SBR) aims to predict the next item of interest in chronological order based on a given sequence of short-term behaviour of anonymous users. Due to the limited data available for short-term user interactions, its performance is more susceptible to data sparsity problems than traditional recommendation methods. Contrastive learning is often used to solve the data sparsity problem due to its ability to extract general features from the raw data. Existing session-based recommendation methods based on graph contrastive learning typically build graph contrastive learning by using information from other sessions to generate augmented views. While this avoids the problem that the use of dropout in traditional contrast learning methods can cause damage to the session context, it inevitably introduces irrelevant item information, which interferes with accurately modelling user interests and leads to sub-optimal model performance. To address these issues, we propose a new session recommendation method based on multi-layer aggregation augmentation contrastive learning, namely SR-MACL. In SR-MACL we construct a contrastive view by adding noise to the embedding representation and forming a contrastive embedding representation by multi-layer aggregation, which not only effectively solves the problem that traditional graph enhancement methods can destroy the context of the whole session, but also avoids the interference of irrelevant items. Experimental results on three real datasets have shown that SR-MACL can improve the accuracy of recommendation results and predict the user's next interaction more effectively.

A Job Recommendation Model Based on a Two-Layer Attention Mechanism

In the field of job recruitment, traditional recommendation methods only rely on users’ rating data of positions for information matching. This simple strategy has problems such as low utilization of multi-source heterogeneous data and difficulty in mining relevant information between recruiters and applicants. Therefore, this paper proposes a recurrent neural network model based on a two-layer attention mechanism. The model first improves the entity representation of recruiters and applicants through user behavior, company-related knowledge and other information. The entities and their combinations are then mapped to the vector space using one-hot and TransR methods, and a recurrent neural network with a two-layer attention mechanism is used to obtain their potential interests from the click sequence, and then a recommendation list is generated. The experimental results show that this model achieves better results than previous models.

Application Analysis of English Personalized Learning Based on Large-scale Open Network Courses

In the context of Big data, large-scale open online courses increase learning paths for learners, but in the face of countless high-quality curriculum resources, it is easy for derivative learners to face the dilemma of rich curriculum resources but difficult to choose resources, which leads to information maze for learners. How to help learners quickly and accurately find their own learning resources in the explosive growth of MOOC resources is an urgent problem in the field of education Big data. However, the traditional Collaborative filtering recommendation technology does not perform well when dealing with sparse data and cold start. The recommendation content is repeated and can not effectively deal with high-dimensional and nonlinear data of online learning users, resulting in low efficiency of resource recommendation. Therefore, the study adopts a deep belief network (DBN) to construct a personalized resource recommendation model. The model combines the learner behavior characteristics with the curriculum resource content attribute characteristics to form the learner feature vector. The parameters of the model are adjusted according to the characteristics of learners. Through experiments, the proposed model has shown good performance. The experiment explored the effects of training set size, learner characteristics, and GPU on model performance. The experimental results show that when the training set proportion is 100%, the RMSE, Accuracy, Recall, and F1 values of the model are 0.76, 0.946, 0.957, and 0.951, respectively. When the model is trained using a training set containing learner features, the RMSE, Accuracy, Recall, and F1 values of the model are 0.75, 0.962, 0.908, and 0.958, respectively. After using GPU to accelerate the model, the running time of the model decreased from 360 minutes to 90 minutes. The results indicate that the model cannot effectively mine data information when the degree of correlation between sample information is low. The richer the relationships between samples, the better the performance of the model. Simultaneously learning hunger feature vectors and learner behavior feature vectors for training can significantly improve the recommendation accuracy of the model. The main contribution of this study is to propose a recommendation method based on DBN classification to replace traditional similarity calculation methods, using DBN's efficient feature abstraction and feature extraction capabilities to fully explore learners' interest and preference for course resources. In addition, in view of the common problems of cold start and data sparsity in traditional Collaborative filtering recommendation methods, the research deeply mines the characteristics of learners' Demographics and curriculum resources' content attributes, and constructs a learner interest model based on DBN combined with learners' behavior characteristics, which effectively solves the problems of cold start and data sparsity, as well as the inaccurate expression of learners' interest preferences for curriculum resources.

NEURAL COLLABORATIVE FILTERING BASED GROUP RECOMMENDATIONS

Group recommendations, where a system suggests items for a group of users rather than individuals, present a unique challenge in collaborative filtering. Traditional approaches to group recommendations often rely on aggregating individual preferences, which can lead to suboptimal results when preferences are diverse or conflicting. This paper explores a novel approach using Neural Collaborative Filtering (NCF) to improve group recommendation accuracy. NCF, which leverages deep learning techniques to model complex user-item interactions, offers a more nuanced understanding of group dynamics by incorporating both individual and group-level preferences. We propose a new NCF-based framework designed to handle group recommendations by effectively learning and integrating diverse user preferences. Our experimental results demonstrate that the proposed approach outperforms traditional group recommendation methods in terms of prediction accuracy and user satisfaction. This research highlights the potential of neural networks in enhancing group recommendation systems and provides a foundation for future developments in this area.

Research on Kindergarten Drama Education Activity System with Traditional Culture as Carrier under Big Data Platform

Abstract Drama education activities carried out by traditional culture comply with, guide and stimulate children’s dramatic nature so that each child can find a suitable place for himself in drama education activities and promote the growth of children with individuality. This paper builds a flipped classroom teaching model for kindergarten drama education activities based on the microblogging public platform. Teachers in the flipped classroom of drama education activities need to collect traditional cultural drama resources before class, and the basic framework of a personalized drama resource recommendation system is proposed on the WeChat public platform. The optimization of drama resource recommendation results is carried out using the preference score of drama resources using the collaborative filtering recommendation algorithm of multi-featured users. The performance test is conducted for the personalized drama resource recommendation system constructed in this paper to provide drama resources for the development of kindergarten drama education activities in the flipped classroom. The results show that when the preference recommendation of multi-featured users’ drama resources has a certain number of users, its accuracy is about 4.5% higher than that of the traditional method, the F-score value is about 0.05 higher than that of the traditional recommendation method, and the real-time updating delay of the personalized recommendation system fluctuates between 1.18s~1.65s. The personalized recommendation system can help teachers to obtain the update and download of traditional culture drama resources in time, make a good guarantee of drama teaching resources for kindergartens to carry out drama education activities with traditional culture as the carrier, and cultivate young children’s awareness of the protection of traditional culture.

Personalized route recommendation for passengers in urban rail transit based on collaborative filtering algorithm

AbstractThe rapid advancements in information technology and intelligent systems within urban rail transit (URT) systems have highlighted the need for more personalized route recommendations that consider passengers’ travel habits. This study aims to address this issue by investigating passenger travel routes alongside other passengers who share similar travel preferences, utilizing collaborative filtering (CF) techniques. The approach involves analyzing historical card data to assess passenger travel profiles, including actual travel time under crowded conditions. By considering both individual passenger preferences and the preferences of similar passengers, a CF algorithm is employed to offer personalized route recommendations. The Shenzhen metro is used as a case study to illustrate the proposed method. The results demonstrate that the proposed approach surpasses traditional route recommendation methods by providing tailored suggestions that align more closely with passengers’ travel preferences. These findings emphasize the value of incorporating passenger travel preferences into route recommendation models, thereby enhancing the accuracy and effectiveness of personalized route recommendations within URT systems.

Job Recommendation System Using Deep Reinforcement Learning (DRL)

The rapid growth of online job portals and the increasing volume of job listings have made it challenging for job seekers to efficiently navigate through the vast number of available opportunities. Job recommendation systems play a crucial role in assisting users in finding relevant job opportunities based on their skills, preferences, and past experiences. This research paper proposes a job recommendation system that leverages deep learning techniques to enhance the accuracy and effectiveness of job recommendations. The system utilizes advanced algorithms to analyses user profiles, job descriptions, and historical data to generate personalized job recommendations. Experimental evaluations demonstrate the superiority of the proposed system compared to traditional recommendation methods, thereby improving the job search process for both job seekers and employers. This paper provides Job recommendation system using Deep Reinforcement Learning (DRL).

Learning From Atypical Behavior: Temporary Interest Aware Recommendation Based on Reinforcement Learning

Traditional robust recommendation methods view atypical user-item interactions as noise and aim to reduce their impact with some kind of noise filtering technique, which often suffers from two challenges. First, in real world, atypical interactions may signal users' temporary interest different from their general preference. Therefore, simply filtering out the atypical interactions as noise may be inappropriate and degrade the personalization of recommendations. Second, it is hard to acquire the temporary interest since there are no explicit supervision signals to indicate whether an interaction is atypical or not. To address this challenges, we propose a novel model called Temporary Interest Aware Recommendation (TIARec), which can distinguish atypical interactions from normal ones without supervision and capture the temporary interest as well as the general preference of users. Particularly, we propose a reinforcement learning framework containing a recommender agent and an auxiliary classifier agent, which are jointly trained with the objective of maximizing the cumulative return of the recommendations made by the recommender agent. During the joint training process, the classifier agent can judge whether the interaction with an item recommended by the recommender agent is atypical, and the knowledge about learning temporary interest from atypical interactions can be transferred to the recommender agent, which makes the recommender agent able to alone make recommendations that balance the general preference and temporary interest of users. At last, the experiments conducted on real world datasets verify the effectiveness of TIARec.

Research on cloud manufacturing service recommendation based on graph neural network.

There are an increasing number of manufacturing service resources appeared on the cloud manufacturing (CMfg) service platform recently, which leads to a serious information overloading problem to the enterprises that need these resources. To tackle this problem, a graph neural network-based recommendation method for CMfg service resources is proposed, which effectively overcomes some limitations of the traditional recommendation methods. Specifically, we first use different similarity calculation methods (e.g., Cosine similarity, Pearson correlation coefficient, etc.) to calculate the similarities between different resources based on the feature information of CMfg service resources. A resource graph dataset is accordingly established. A graph neural network is then used to perform representation learning of nodes in these graphs, obtaining the vector representations of these nodes. Finally, new links that may appear in a graph are predicted by performing dot product calculations on these nodes' vector representations. And these links can be used to recommend suitable resources. Experiments mainly show that (i) the proposed method obtains better link prediction accuracy compared with that of other link prediction algorithms; (ii) when the network density used for training is relatively high, the predictive performance of the proposed method is improved significantly. Our method can shed light on how to choose suitable CMfg service resources from the CMfg service platform.

Knowledge graph preference migration network for recommendation

Knowledge graph (KG) is being introduced into recommender systems in more and more scenarios. However, the previous recommendation methods based on KG usually focus on mining the relative signals between a user and an item, while ignoring the exploration of collaborative signals between users. In addition, the traditional recommendation methods based on KG completely overlooks the discrepancies among users in the modeling process, which will lead to the problem of insufficient information in the obtained cold-start users’ representations. To tackle these, a novel model named Knowledge Graph Preference Migration Network (KGPM) is proposed by us, aiming to address the lack of information in cold-start users’ representations in the knowledge-aware recommendation. To be specific, the method aggregates the representations from a node’s neighborhoods of different hops to renew the node representation on a collaborative knowledge graph (CKG) consisting of a KG and a user–item interaction graph (UIIG), and differentiates the importance of different neighbors via employing an attention mechanism. Next, to achieve preference migration by mining more collaborative signals among users, the neighbor users of the cold-start users should be further propagated in our elaborately constructed user logical interaction space. We apply two datasets accompanying specific scenarios to verify our proposed model, and the experimental results show that KGPM achieves significant improvements over the most advanced baselines.

A Job Recommendation Method Based on Attention Layer Scoring Characteristics and Tensor Decomposition

In the field of job recruitment, a classic recommendation system consists of users, positions, and user ratings on positions. Its key task is to predict the unknown rating data of users on positions and then recommend positions that users are interested in. However, traditional recommendation methods only rely on user rating data for jobs and provide recommendation services for recruiters and candidates through simple information matching. This simple recommendation strategy not only causes a lot of information waste but also cannot effectively utilize the multi-source heterogeneous data information in the field of job recruitment. Therefore, this paper proposes a job recommendation model based on users’ attention levels and tensor decomposition for specific recruitment positions. This model puts forward assumptions based on browsing time for the special behaviors and habits of users in the field of job recruitment, defines corresponding label values for different interactive behaviors, and establishes a grading method based on the attention of job seekers, thus constructing a three-dimensional tensor of “job seeker user-position-attention layered”. Then, a recommendation model is constructed by decomposing the three-dimensional tensor. The effectiveness of the model is verified by comparative experiments with other recommendation algorithms.

Research on personalized recommendation method of preschool e-learning resources

Abstract E-learning is a very popular learning method at this stage, and Internet learners need to invest much time to retrieve the required e-learning network resources. In building the existing e-learning resources, the cognitive level, thinking ability, learning style, and other factors of learners are not considered. For this challenge, the paper gives the recommendation methods of the collaborative filtering algorithm model and learner model, compares the advantages and disadvantages of these models and traditional recommendation methods using the data of mean square error, response time, and accuracy, and examines the students’ suggestions for this recommendation method in the field. The collaborative filtering recommendation technique had the highest completion rate, above 0.7 for 5-20 recommendations and above 0.75 and 0.8 for 20-35. The rate of the learner model is stable between 0.7 and 0.8. In contrast, the detection rate of traditional methods is between 0.6 and 0.7. The collaborative filtering recommendation method outperforms the other two methods regarding completeness, response time, accuracy, and F1 value. In terms of satisfaction, the collaborative filtering recommendation algorithm had the highest satisfaction rate, followed by the learner model; the collaborative filtering method had the best results in improving learning performance. Therefore, personalized recommendation methods can improve learning efficiency and reduce retrieval time, and the collaborative filtering recommendation method is the most effective among the two methods proposed in this paper.

A Two-Path Multibehavior Model of User Interaction

Personalized recommendation is an important part of e-commerce platforms. In recommendation systems, a neural network is used to enhance collaborative filtering to accurately capture user preferences, so as to obtain better recommendation performance. Traditional recommendation methods focus on the results of a single user behavior, ignoring the modeling of multiple interaction behaviors of users, such as click, add to cart and purchase. Although many studies have also focused on multibehavior modeling, two important challenges remain: (1) Since the multiple behaviors of the time-evolving trends of context information are ignored, it is still a challenge to identify the multimodal relationships of behaviors; (2) surveillance signals are still sparse. In order to solve these problem, this paper proposes a two-path multibehavior model of user interaction (TP_MB). First, a two-path learning strategy is introduced to maximize the multiple-interaction information of users and items learned by the two paths, which effectively enhances the robustness of the model. Second, a multibehavior dependent encoder is designed. Contextual information is obtained through behavior dependencies in the interaction of different users. In addition, three contrastive learning methods are designed, which not only obtain additional auxiliary supervision signals, but also alleviate the problem of sparse supervision signals. Extensive experiments on two real datasets demonstrate that our method outperforms state-of-the-art multibehavior recommendation methods.

Multi-Feature Video Recommendation Based on Hypergraph Convolution for Mobile Edge Environment

With the massive growth of edge devices, how to provide users with video recommendation services in a mobile edge environment has become a research hotspot. Most traditional video recommendation methods regard the relationship between user and neighbor to be linear and ignore higher-order connectivity among users, which results in poor recommendation performance. Besides, these methods use a single feature to represent user preferences, which cannot effectively alleviate the data sparsity problem. To improve the performance of video recommendation, this article proposes a multi-feature video recommendation method based on hypergraph convolution (MVRHC). Hypergraph convolution is adopted to compute user neighborhood-level features for modeling high-order correlations among users. Final features are obtained by fusing multi-party features through attention mechanism. And video recommendation is then implemented based on the obtained features. Experimental results on two real-world datasets demonstrate that MVRHC has better performance compared with baseline methods.

CGRS: Collaborative Knowledge Propagation Graph Attention Network for Recipes Recommendation

In the age of big data, recipe recommendation is of great significance. It can recommend recipes in line with the user's eating habits in massive data. Compared with other recommendation tasks, recipe recommendation is influenced by multiple aspects and requires fine-grained learning to obtain entity representations. Therefore, the traditional recommendation method cannot meet people's requirements. In this paper, we propose the Collaborative Knowledge Propagation Graph Attention Network for Recipes Recommendation (CGRS). This method designs collaborative information propagation to make full use of user interaction and recipe attribute information to meet the needs of multiple influencing factors. Use the graph attention feature learning network to obtain the high-order feature information of the entity to meet the demand for fine-grained representation. Specifically, the method first obtains the multi-hop triplet sets of users and recipes through a collaborative message propagation strategy. Then utilises a graph attention feature learning layer to learn the topological proximity structure features of the triplet sets. Obtain high-level semantic information of entities by superimposing network layers. Design an attention aggregator at the prediction layer to refine the embedding representation of entities. Finally predict the user-recipe interaction probability. Experimental results prove the advancement and effectiveness of CGRS.