Content-based Recommendation Research Articles

Personalized learning paths recommendation system with collaborative filtering and content-based approaches

Recommender systems have undergone a transformative evolution, reshaping user interactions across diverse domains. Notably, the emphasis on personalized learning paths has grown significantly in education. This research paper delves into the performance evaluation of User-based Collaborative Filtering and Content-based recommendation techniques to develop innovative recommender systems explicitly tailored to Information Systems students. By integrating the primary dataset collection rooted within the Knowledge - Skill - Attitude framework for students in the Faculty of Information Systems at the University of Economics and Law, this study assesses how effectively these two separate models develop personalized recommendation systems. Furthermore, the empirical evaluation of two distinct models, the Collaborative Filtering and Content-Based approach, across key metrics such as Precision, Recall, and F1-Score, provides a comprehensive view of their effectiveness in generating a Recommendation System for the University of Economics and Law. Findings reveal that the Collaborative Filtering approach excels in Precision, achieving a perfect score. At the same time, the Content-based technique demonstrates superior recall capabilities, suggesting its potential to cater to diverse educational needs. This paper also highlights the transformative role of recommendation systems in higher education, particularly in enhancing student engagement through personalized learning experiences and aligning curricula with industry requirements. Recognizing the limitations inherent in deploying either model independently, future research should propose a hybrid approach that combines the strengths of both Collaborative Filtering and Content-based methods, aiming to mitigate the existing drawbacks of the distinct model. The findings provide actionable insights for students, universities, and businesses to enhance educational content and career development tools and pave the way for future research on hybrid recommendation methodologies, which promise a more tailored and efficient learning experience for learners.

Exploration and Practice of High-Quality Talent Cultivation Path in the Context of Industry-Education Integration Drive

Abstract High-quality talent cultivation is an inevitable trend driven by the development of industry-education integration. This paper analyzes the differences in project goals between enterprises and universities, selects a content-based recommendation method, establishes an interest theme model for both entities and generates cooperative recommendation partners. A new type of partner recommendation scheme for industry-education integration projects is established based on geographic proximity by introducing the factor of geographic distance. Interpret the meaning of high-quality talent cultivation, establish a joint talent cultivation mechanism among enterprises, colleges, universities, and industrial organizations, and develop a three-stage segmented cultivation model. Finally, analyze the degree of participation and influence of each subject and variable on the practice of industry-industry fusion, respectively, and explore the effectiveness of industry-industry fusion collaborative cultivation by combining the teaching characteristics of teachers from schools and enterprises with the development of industry-industry fusion activities in the college. During the three years of junior, senior, and graduates, College D’s industry-education integration activities are mainly organized by lectures, visits, seminars, and enterprise recruitment meetings, of which the proportion of inviting relevant enterprises to give lectures can reach more than 80%. The activities of the college’s industry-education integration organization are one of the effective paths to building development space for the cultivation of high-quality talents.

Enhanced Travel Experience using Artificial Intelligence: A Data-driven Approach

Travelers are increasingly looking for customized itineraries that are suited to their specific interests. In response to this demand in this work, an automated travel planning system is offered to cater to personalized itinerary needs. It employs conversational Artificial Intelligence (AI), Chat Generative Pre-Trained Transformer (ChatGPT) to comprehend the user’s preferences and recommend appropriate destinations and activities. Playwright, a web scraper, is utilized to access real-time airline and hotel info from Google Travel. This data is used to train a content-based recommendation engine, which matches possible outcomes to the traveler’s budget and priorities. The algorithm generates personalized recommendations by taking into account essential parameters such as pricing, hotel ratings and fight layover. The precision score analysis validates the recommendations’ accuracy. The results demonstrate that the system provided accurate tailored recommendations with 86 percent precision. Based on user studies, comparative assessments revealed enhanced performance over existing technologies. User satisfaction was represented through customised suggestions that met their specific preferences and limits. The solution simplifies and improves the travel planning experience using AI-powered personalization that caters to individual preferences and limits.

Promoting Educational Reform to Enhance Talent Cultivation Quality of Civil Engineering Majors Based on Deep Learning Background

Abstract This paper focuses on improving the quality of talent cultivation for civil engineering students through reforming teaching quality and course selection management. Regarding teaching quality management, the article proposes to use improved Apriori algorithm to generate high interest rules and utilize SQL for effective data manipulation. Regarding course selection management, the content-based recommendation algorithm is used to optimize the course selection mechanism. After the implementation of the reform, the proportion of low scores in the self-quality evaluation of college students was significantly reduced, and the evaluation indexes of teachers also showed significant differences. In addition, employers were satisfied with the competence of civil engineering graduates. These results indicate that data-driven teaching management and personalized course selection recommendation can effectively improve education quality and student satisfaction.

AI-Powered Academic Guidance and Counseling System Based on Student Profile and Interests

Over the past few decades, the education sector has achieved impressive advancements by incorporating Artificial Intelligence (AI) into the educational environment. Nevertheless, specific educational processes, particularly educational counseling, still depend on traditional procedures. The current method of conducting group sessions between counselors and students does not offer personalized assistance or individual attention, which can cause stress to students and make it difficult for them to make informed decisions about their coursework and career path. This paper proposes a counseling solution designed to aid high school seniors in selecting appropriate academic paths at the tertiary level. The system utilizes a predictive model that considers academic history and student preferences to determine students’ likelihood of admission to their chosen university and recommends similar alternative universities to provide more opportunities. We developed the model based on data from 500 graduates from 12 public high schools in Morocco, as well as eligibility criteria from 31 institutions and colleges. The counseling system comprises two modules: a recommendation module that uses popularity-based and content-based recommendations and a prediction module that calculates the likelihood of admission using the Huber Regressor model. This model outperformed 13 other machine learning modules, with a low MSE of 0.0017, RMSE of 0.0422, and the highest R-squared value of 0.9306. Finally, the system is accessible through a user-friendly web interface.

The Connotation of Objectives and Hierarchical Orientation of Teaching English and American Literature Based on the Era of Big Data

Abstract This paper combines the knowledge structure of English and American literature and learners’ personalized knowledge characteristics to design and propose personalized teaching based on a genetic algorithm. Genetic algorithms are used to extract learner characteristics, and content-based recommendation algorithms are applied to match ability characteristics, goal characteristics and learning object characteristics to achieve personalized course teaching. The crossover probability, variation probability, selection operator and crossover operator of the genetic algorithm are determined, and simulation experiments are designed to analyze the optimization effect of its parameters. Setting three levels of colleges and universities, with the connotation of the objectives and level positioning of English and American literature teaching, i.e., reading experience, reading knowledge, reading method, and the cultivation of cultural connotation and humanistic sentiment as factors, a one-way ANOVA was carried out to examine whether the teaching objectives of different levels of colleges and universities differed significantly in personalized teaching. In terms of the highest level of the teaching objectives of English and American literature, i.e., the cultivation of humanistic sentiment, F=6.607, Sig=0.002, 985 colleges>211 colleges and universities, and general undergraduate schools>211 colleges and universities.

A content-based recommender system using stacked LSTM and an attention-based autoencoder

Recommender systems (RS) are popular in many areas, such as movies, music, news, books, research articles, search queries, and social tagging. Proprietary recommender systems are used by e-commerce websites like eBay, Amazon, and Alibaba to better match customers with products they are likely to purchase. This study suggests a recommender system that combines stacked long short-term memory (LSTM) and an attention-based autoencoder. This system would be used in a self-supervised learning paradigm, and the Amazon product datasets were used to run simulations. The results showed that the proposed method is more accurate, uses less computing power, and can be used on a large scale. No matter how big the data is, it can capture low-dimensional fixed latent representations and use the bare minimum of information already in the items, whether they are new or old. Several evaluation metrics show that the method works and that the cold start problem has been solved.

Towards Hyper-Relevance in Marketing: Development of a Hybrid Cold-Start Recommender System

Recommender systems position themselves as powerful tools in the support of relevance and personalization, presenting remarkable potential in the area of marketing. The cold-start customer problematic presents a challenge within this topic, leading to the need of distinguishing user features and preferences based on a restricted set of transactional information. This paper proposes a hybrid recommender system that aims to leverage transactional and portfolio information as indicating characteristics of customer behaviour. Four independent systems are combined through a parallelised weighted hybrid design. The first individual system utilises the price, target age, and brand of each product to develop a content-based recommender system, identifying item similarities. Secondly, a keyword-based content system uses product titles and descriptions to identify related groups of items. The third system utilises transactional data, defining similarity between products based on purchasing patterns, categorised as a collaborative model. The fourth system distinguishes itself from the previous approaches by leveraging association rules, using transactional information to establish antecedent and precedence relationships between items through a market basket analysis. Two datasets were analysed: product portfolio and transactional datasets. The product portfolio had 17,118 unique products and the included 4,408,825 instances from 2 June 2021 until 2 June 2022. Although the collaborative system demonstrated the best evaluation metrics when comparing all systems individually, the hybridisation of the four systems surpassed each of the individual systems in performance, with a 8.9% hit rate, 6.6% portfolio coverage, and with closer targeting of customer preferences and smaller bias.

Experimental Hybrid Approach for Improving the Quality of Personalized Product Recommendation Systems with Deep Learning

Deep learning has recently made significant advancements in recommender systems. Various methods, including hybrid recommendation systems, content-based recommendations, and deep learning, have been employed to enhance recommendation accuracy. Over the past decade, the proliferation of big data has led to a rapid increase in the amount of available data on the internet. However, dealing with complex and large data sets makes it challenging for many users to quickly find the information they need. At this juncture, recommendation systems play a crucial role in addressing the issue of data overload. The development of recommendation algorithms has been particularly driven by the growth of the e-commerce industry. Traditional single recommendation algorithms face challenges such as data sparsity, long-tail items, and cold starts. Hybrid recommendation algorithms can effectively mitigate some of these limitations. In response to these challenges, this paper proposes an experimental hybrid approach to improve the quality of personalized product recommendations using deep learning, specifically an Improved Attention-Convolutional Neural Network (IA-CNN). This method aims to address the shortcomings of single collaborative models. The algorithm first employs a comprehensive approach, combining product- and user-based collaborative filtering strategies to generalize and categorize the output results. This methodology captures detailed and abstract nonlinear interactions between users and products through advanced deep learning techniques. Finally, experiments were designed to test the algorithm's effectiveness. The IA-CNN algorithm was evaluated against a benchmark algorithm using the Amazon product rating dataset. The results demonstrate that the proposed IA-CNN algorithm outperforms the benchmark in rating prediction on the test dataset

ConCollA - A Smart Emotion-based Music Recommendation System for Drivers

Music recommender system is an area of information retrieval system that suggests customized music recommendations to users based on their previous preferences and experiences with music. While existing systems often overlook the emotional state of the driver, we propose a hybrid music recommendation system - ConCollA to provide a personalized experience based on user emotions. By incorporating facial expression recognition, ConCollA accurately identifies the driver’s emotions using convolution neural network(CNN) model and suggests music tailored to their emotional state. ConCollA combines collaborative filtering, a novel content-based recommendation system named Mood Adjusted Average Similarity (MAAS), and apriori algorithm to generate personalized music recommendations. The performance of ConCollA is assessed using various evaluation parameters. The results show that proposed emotion-aware model outperforms a collaborative-based recommender system.

A Study of Digital Museum Collection Recommendation Algorithm Based on Improved Fuzzy Clustering Algorithm

With the rapid advancement of internet technology, various industries have accumulated vast amounts of data, including on user behavior and personal preferences. Traditional museums can leverage this user data to uncover individual preferences and offer personalized services to their visitors. However, the exponential growth of information has also led to the problem of information overload, making it challenging for users to find relevant information within the vast data landscape. Consequently, the utilization rate of available information decreases. By harnessing the power of cloud computing, big data analytics, and recommendation systems, museums can enhance visitors’ touring experiences by helping them discover collections aligned with their interests and connecting with like-minded individuals. To address this objective, the research focuses on optimizing the initial clustering centers of the fuzzy clustering algorithm and parallelizing the optimized algorithm using MapReduce, resulting in the development of a novel MapReduce-based k-prototype fuzzy c-means (MRKPFCM) algorithm. Subsequently, the MRKPFCM algorithm is combined with the classical collaborative filtering algorithm to create a hybrid and parallelized collaborative filtering recommendation algorithm, incorporating elements such as MRKPFCM, audience, and collection. This hybrid algorithm is further supplemented by a content-based recommendation approach to generate comprehensive and refined recommendation results. Experimental findings demonstrate that the predictive scoring errors, as measured by RMSE and MAE, exhibited a downward trend when the number of nearest neighbors for target users fell within the range of 10–20. For instance, the studied algorithm’s MAE value decreased from 0.7512 to 0.7179, surpassing the corresponding figures for the two comparison algorithms. Moreover, with an increase in the number of nearest neighbors within the same range, all three algorithms experienced improved accuracy in prediction results. In particular, the accuracy rate rose from 17.84% to 18.82%, outperforming the two comparison algorithms. In summary, the enhanced hybrid recommendation algorithm achieved through this study displays superior recommendation accuracy and holds significant practical value.

A Comprehensive Survey of Recommender Systems Based on Deep Learning

With the increasing abundance of information resources and the development of deep learning techniques, recommender systems (RSs) based on deep learning have gradually become a research focus. Although RSs have evolved in recent years, a systematic review of existing RS approaches is still warranted. The main focus of this paper is on recommendation models that incorporate deep learning techniques. The objective is to guide novice researchers interested in this field through the investigation and application of the proposed recommendation models. Specifically, we first categorize existing RS approaches into four types: content-based recommendations, sequence recommendations, cross-domain recommendations, and social recommendation methods. We then introduce the definitions and address the challenges associated with these RS methodologies. Subsequently, we propose a comprehensive categorization framework and novel taxonomies for these methodologies, providing a thorough account of their research advancements. Finally, we discuss future developments regarding this topic.

Automatic Recommendation of Forum Threads and Reinforcement Activities in a Data Structure and Programming Course

Online learning is quickly becoming a popular choice instead of traditional education. One of its key advantages lies in the flexibility it offers, allowing individuals to tailor their learning experiences to their unique schedules and commitments. Moreover, online learning enhances accessibility to education, breaking down geographical and economical boundaries. In this study, we propose the use of advanced natural language processing techniques to design and implement a recommender that supports e-learning students by tailoring materials and reinforcement activities to students’ needs. When a student posts a query in the course forum, our recommender system provides links to other discussion threads where related questions have been raised and additional activities to reinforce the study of topics that have been challenging. We have developed a content-based recommender that utilizes an algorithm capable of extracting key phrases, terms, and embeddings that describe the concepts in the student query and those present in other conversations and reinforcement activities with high precision. The recommender considers the similarity of the concepts extracted from the query and those covered in the course discussion forum and the exercise database to recommend the most relevant content for the student. Our results indicate that we can recommend both posts and activities with high precision (above 80%) using key phrases to represent the textual content. The primary contributions of this research are three. Firstly, it centers on a remarkably specialized and novel domain; secondly, it introduces an effective recommendation approach exclusively guided by the student’s query. Thirdly, the recommendations not only provide answers to immediate questions, but also encourage further learning through the recommendation of supplementary activities.

Interactive Guide Assignment System with Destination Recommendation and Built-in Chatbox

This proposed system provides a website called 'Safari Nepal', where users can search for destinations and check their location on a map. Users when registering on the website, can fill up the details about themselves and choose to either be a tour guide or a tourist. Based on the search and preferences of the user, similar destinations are recommended to the user via a recommendation system that uses a content-based recommendation feature. This feature works on the data obtained from the user, either explicitly or implicitly. The concept of K-Nearest Neighbours (KNN) and Cosine similarity makes the recommendation more accurate. KNN uses a distance algorithm that sorts from most liked destinations to least liked, based on the preferences of the user. This sorted list of destinations is further filtered by Cosine similarity, which is a measure of how similar two vectors in an inner product space are. It is calculated by taking the cosine of the angle between two vectors and determining whether two vectors are pointing towards the same general direction. Thus, combined KNN and Cosine similarity gives a better recommendation to the user. The map is integrated into the system using Mapbox API. Also, the system connects users with tour guides and gives them space to chat via a chatbox called ‘Travel Buddy’ where they can discuss further the destination, the amount charged by the guide, etc. The chatting feature on the system allows multiple users to connect and make conversations about the destination creating various chatrooms. In the system, the user can also publish their blogs describing their experiences and share their thoughts on particular destinations.

Analysis of media content recommendation in the new media era considering scene clustering algorithm

Abstract With the advent of the new media era, we face the problem of information overload every day, based on which this paper proposes a scenario-conscious clustering algorithm for recommending media content. The improved K-means algorithm is used to cluster the media content, an initial cluster center is randomly selected, and the remaining initial cluster centers are obtained by executing the improved algorithm to reduce the number of iterations and avoid neighboring situations. The clustering algorithm is then compared with content-based recommendation techniques, KNN recommendation algorithm, and collaborative filtering recommendation algorithm in terms of accuracy, recall, MAE value, and execution time. The clustering algorithm is significantly better than the other algorithms regarding accuracy and recall. The accuracy of the clustering algorithm is 0.4 when the recommendation sequence is 30, which is 0.4 higher than the collaborative filtering technique, 0.1 higher than the KNN, and 0.12 higher than the content recommendation method. In terms of MAE value, the clustering algorithm outperforms the other algorithms when the number of nearest neighbors is selected to be above 20. In terms of execution time, the longer the amount of data, the more obvious the advantage of the clustering algorithm. Therefore, the applicability and reliability of the model proposed in this paper for media content recommendation are verified in terms of accuracy, recall and execution time, which meet the design requirements.

ClayRS: An end-to-end framework for reproducible knowledge-aware recommender systems

Knowledge-aware recommender systems represent one of the most innovative research directions in the area of recommender systems, aiming at giving meaning to information expressed in natural language and obtaining a deeper comprehension of the information conveyed by textual content.Though rich and constantly evolving, the literature on knowledge-aware recommender systems is particularly scattered when considering software libraries. This makes it difficult to easily exploit advanced content representation and implement replicable experimental protocols. Accordingly, this work aims to fill in these gaps by introducing ClayRS, an end-to-end framework for replicable knowledge-aware recommender systems. ClayRS provides researchers and practitioners with the most recent state-of-the-art methodologies to build knowledge-aware content representations and also includes methods to exploit these representations in content-based recommendation algorithms. Finally, the structure of the framework also allows for building replicable pipelines to push forward the current research in the area and to develop accountable recommender systems.

Rural tourism service design based on collaborative filtering algorithm after epidemic normalization

Abstract In the epidemic, normalized tertiary and rural tourism service industries are in the economic depression stage. Under the epidemic, combined with big data technology to improve the economic income and development scale of rural tourism services become the current development trend of the tourism industry. This paper first proposes a collaborative filtering recommendation algorithm based on big data technology to study the design of rural tourism services under the new epidemic normalization. Then the basic principle of the content-based recommendation algorithm is to obtain the interests of tourists based on their historical behaviors and recommend rural tourism similar to their interest preferences, and choosing the appropriate similarity function can improve the accuracy of the neighborhood-based CF method. Finally, to meet the tourists’ demand for a full range of tourism experience services and build a rural tourism service system, the psychological demand of rural tourism tourists’ consumption is analyzed based on a collaborative recommendation algorithm. The results show that among the main factors attracting tourists, 75.54% are natural scenery, 54.68% are folk culture, 51.08% are unique flavors and food, 43.17 are experiencing rural life, and 41.73% are promoting relationships with friends. This study plays an important role in accelerating rural revitalization by attracting urban tourists back to the countryside and driving the transfer of consumer groups and the rural economy to increase income; thus, rural tourism plays an important role in accelerating rural revitalization.

Calculation and evaluation of similarity in sports information resources based on combined algorithm

Abstract It is helpful for athletes to obtain real-time and accurate sports information resources by integrating various recommendation algorithms into a personalised sports recommendation system. Therefore, this paper designs user-based and content-based recommendation algorithms, and recommends their results to sporters by weighted selection. In addition, by acquiring the characteristics of sporters, the method of the constraint space of core parameters is determined by using ontology rule reasoning, which ensures the rationality of sports parameter extraction, and thus establishes the similarity calculation model of sports resources and designs the strategy and method of model verification. Finally, the validity of the model is evaluated by Mahalanobis distance, and the recommendation effects of different algorithms are compared by similarity grade, which can provide sporters with personalised sports resources with stronger pertinence and better effect.

Improving recommendation diversity and serendipity with an ontology-based algorithm for cold start environments

AbstractEvery real-life environments where users interact with items (products, films, research expert profiles) have several development phases. In the Cold-start phase, there are almost no interactions among users and items content-based recommendation systems (RS) can only recommend based on matching the attributes of the items. In the transition state, items start to collect user interactions but still a significant number of items have too small number of interactions, RS does not allow users to discover cold items. In a regular state, where most of the items in the system have enough interactions, the recommendations often suffer from low diversity of the items within a single recommendation. This article proposes a general recommendation algorithm based on Ontological-similarity, which is designed to address all the above problems. Our experiments show that recommendations generated by our approach are consistently better in all environment development phases and increase the success rate of recommendations by almost 50% measured using ontology-aware recall, which is also introduced in this article.

A hybrid recommendation algorithm for green food based on review text and review time

ABSTRACT Green food is well-known for its health benefits, environmental-friendliness, and safety. Current recommender systems used by e-commerce websites usually recommend products based on products' popularity or customers' ratings. However, users' reviews could be more representative of consumers' preferences. In addition, users' review time is not utilized. To reduce the recommendation bias, this study proposes a hybrid recommendation algorithm based on green food reviews and review time. The proposed algorithm combines a content-based recommendation algorithm with a user-based collaborative filtering approach, where affective values of reviews replace ratings and a time impact factor is considered. With the two classical evaluation indices of F1 and Mean Absolute Error (MAE), the experiments show that considering both reviews’ sentiments and dynamic changes of individuals’ preferences could improve recommendation effectiveness over three other algorithms, which provides a new reference direction for improving existing recommender systems on green food.