Personalized Recommendation System Research Articles

Analysis of Personalized Recommendation System on Cloud Platform

A good recommendation system can recommend a group of movies to users based on their interests or the popularity of the films. The recommendation systems are important because they assist them in making good choices without requiring them to expend their time. It is difficult and expensive for programmers to build an on-premise recommendation system to automate this process as there is a massive increase in data volume which requires high computational capacity. This paper describes the deployment of a movie recommendation engine in a cloud storage environment that uses ALS algorithm and POST API. The advantage of such a deployment is the use of cloud factors in the generation of recommendations, the cloud and the cloud environment promises high availability and thus reduces downtime for recommendation services.

Construction of Personalized Learning Platform Based on Collaborative Filtering Algorithm

On the network service platform for vocational education, there are currently over 10,000 online courses. Learners face a challenge in selecting interesting courses from the vast resources available. Learners’ urgent need for personalized learning is becoming more apparent as educational informatization progresses. Personalized recommendation (PR) technology can aid personalized learning and increase learners’ learning efficiency significantly. This paper constructs a smart classroom model based on AI (artificial intelligence) by studying the connotation and characteristics of smart classroom in light of the current research status and trend of smart classroom at home and abroad. The merits of the recommendation system are determined by the recommendation algorithm used by PR system. This paper primarily focuses on developing a personalized learning platform based on the CF (collaborative filtering) algorithm, as well as conducting system requirements analysis, database design, functional module design, implementation, and testing on this foundation. Experiments are carried out to see if the optimized PR algorithm in the network learning platform is effective.

Hierarchical reinforcement learning with dynamic recurrent mechanism for course recommendation

In online learning scenarios, the learners usually hope to find courses that meet their preferences and the needs for their future developments. Thus, there is a great need to develop effective personalized course recommender systems that can guide the learners to choose suitable courses. In practice, Reinforcement Learning (RL) can be applied to build dynamic user profiles from users’ interactions with courses, which is the key to the success of a course recommender system. However, existing RL-based course recommendation methods usually suffer from the trade-off between exploration and exploitation. In this paper, we propose a novel course recommendation model, namely Hierarchical rEinforcement Learning with dynAmic Recurrent mechanism (HELAR), in which a profile constructor with autonomous learning ability is designed to make personalized course recommendation. To address the exploration-exploitation trade-off issue in constructing user profiles, we propose a novel policy gradient method. It employs a recurrent scheme by context-aware learning to exploit the current knowledge, while utilizing a dynamic baseline to explore the user’s future preferences. Extensive experiments are conducted on two real-world datasets to evaluate the performance of the proposed HELAR model, and the experimental results demonstrate the advantage of HELAR over state-of-the-art course recommendation methods.

Personalized Recommendation Algorithm for University Civics Courses with Multiple User Interests

In response to the problems of unity, lack of relevance, lack of synergy, and inability to form a personalized collaborative education mechanism in the current curriculum setting of university thinking and politics education, a personalized recommendation system for university thinking and politics courses based on the multiple interests of users was developed. The system of interests is divided into two parts: first is initial interest guidance, in which the N meta-model is used to learn the context of known course processes; second is user interest extraction; at the end of creating the recommendation process, facing the diversity of user interests, probabilistic latent semantic analysis trains the interest-service-flow distribution of students to recommend the civics course that matches the current interest for students. A good recommendation algorithm can simulate learners’ enthusiasm and give full play to different learners’ learning personalities. The simulation experiments show that the system is stable in operation, complete in function, and has strong practicality and robustness, which is of positive significance in creating a win-win, diverse, and innovative atmosphere for students’ and teachers’ thinking education in colleges and universities.

Method of Ideological and Political Teaching Resources in Universities Based on School-Enterprise Cooperation Mode

The personalized recommendation system influences the recommendation of ideological and political teaching resources in universities, resulting in a high MAE score. As a result, under the school-enterprise collaboration paradigm, this study proposes a customised recommendation approach for ideological and political teaching resources in colleges and universities. The ideological and political teaching resource bank is developed against the backdrop of the teaching paradigm that combines universities and businesses. Learners’ browsing data history is gathered to create a learning interest model for them. A hybrid collaborative filtering recommendation method was devised, and a recommendation engine was established by Taste component, taking into account individualised resource recommendation needs and information entropy weight distribution mode. When compared to previous techniques, the developed customised recommendation method considerably enhances the recommendation quality of instructional resources and reduces MAE by 29% and 34%, respectively.

Implementation of a personalized food recommendation system based on collaborative filtering and knapsack method

Food recommendation system is one of the most interesting recommendation problems since it provides data for decision-making to users on selection of foods that meets individual preference of each user. Personalized recommender system has been used to recommend foods or menus to respond to requirements and restrictions of each user in a better way. This research study aimed to develop a personalized healthy food recommendation system based on collaborative filtering and knapsack method. Assessment results found that users were satisfied with the personalized healthy food recommendation system based on collaborative filtering and knapsack problem algorithm which included ability of operating system, screen design, and efficiency of operating system. The average satisfaction score overall was 4.20 implying that users had an excellent level of satisfaction.

Affinity Propagation-Based Hybrid Personalized Recommender System

A personalized recommender system is broadly accepted as a helpful tool to handle the information overload issue while recommending a related piece of information. This work proposes a hybrid personalized recommender system based on affinity propagation (AP), namely, APHPRS. Affinity propagation is a semisupervised machine learning algorithm used to cluster items based on similarities among them. In our approach, we first calculate the cluster quality and density and then combine their outputs to generate a new ranking score among clusters for the personalized recommendation. In the first phase, user preferences are collected and normalized as items rating matrix. This generated matrix is then clustered offline using affinity propagation and kept in a database for future recommendations. In the second phase, online recommendations are generated by applying the offline model. Negative Euclidian similarity and the quality of clusters are used together to select the best clusters for recommendations. The proposed APHPRS system alleviates problems such as sparsity and cold-start problems. The use of affinity propagation and the hybrid recommendation technique used in the proposed approach helps in improving results against sparsity. Experiments reveal that the proposed APHPRS performs better than most of the existing recommender systems.

A Personalized Recommendation System for English Teaching Resources Based on Multi-K Nearest Neighbor Regression Algorithm

In order to ensure the quality of resource recommendation and solve the problems of low recommendation accuracy, long recommendation time, and high data loss rate in the process of resource recommendation in traditional methods, a personalized recommendation system of English teaching resources based on the multi-K nearest neighbor regression algorithm is designed. According to the overall architecture of the personalized recommendation system of teaching resources, this study designs the resource browsing function module, teaching resource detailed page recommendation module, and teaching resource database. Based on the basic idea of the multi-K nearest neighbor regression algorithm, in order to avoid the loss of important data in English teaching resource recommendation and reduce the data loss rate, a missing data reconstruction algorithm of English teaching resources is proposed. Finally, the path interest of student users is considered from the selection of browsing path and access time to realize the personalized recommendation of English teaching resources. The experimental results show that the system has high resource recommendation accuracy, short recommendation time, and low data loss rate.

Design of a Personalized Recommendation System for Learning Resources based on Collaborative Filtering

At present, personalized recommendation system has become an indispensable technology in the fields of e-commerce, social network and news recommendation. However, the development of personalized recommendation system in the field of education and teaching is relatively slow with lack of corresponding application.In the era of Internet Plus, many colleges have adopted online learning platforms amidst the coronavirus (COVID-19) epidemic. Overwhelmed with online learning tasks, many college students are overload by learning resources and unable to keep orientation in learning. It is difficult for them to access interested learning resources accurately and efficiently. Therefore, the personalized recommendation of learning resources has become a research hotspot. This paper focuses on how to develop an effective personalized recommendation system for teaching resources and improve the accuracy of recommendation. Based on the data on learning behaviors of the online learning platform of our university, the authors explored the classic cold start problem of the popular collaborative filtering algorithm, and improved the algorithm based on the data features of the platform. Specifically, the data on learning behaviors were extracted and screened by knowledge graph. The screened data were combined with the collaborative filtering algorithm to recommend learning resources. Experimental results show that the improved algorithm effectively solved the loss of orientation in learning, and the similarity and accuracy of recommended learning resources surpassed 90%. Our algorithm can fully satisfy the personalized needs of students, and provide a reference solution to the personalized education service of intelligent online learning platforms.

ProbSky: Efficient Computation of Probabilistic Skyline Queries over Distributed Data

Skyline queries have drawn great interest and been widely used in various application domains including multi-criteria decision making, search pruning, and personalized recommendation systems. Given multiple criteria, skyline queries return objects that are not dominated by any other objects. As an extension of traditional skyline queries, probabilistic skyline queries aim to cope with uncertain datasets. This paper presents a novel MapReduce-based framework, ProbSky, in support of fast parallel evaluation of probabilistic skyline queries on large high-dimensional data. ProbSky efficiently evaluates exact p-skyline queries on large uncertain data without compromising the quality of query results. From the theoretical point of view, we formally prove two pruning lemmas integrated with ProbSky to strengthen the early pruning capacity. ProbSky builds on top of three optimization techniques, namely, dominant instance pruning, grid-based partitioning, and pivot point-based acceleration. Extensive experiments on both real and synthetic datasets unveil that compared to the state-of-the-art, ProbSky speeds up the evaluation of exact p-skyline queries on large high-dimensional data by at least one order of magnitude in most cases. Our experimental results also validate that by balancing the memory consumption and execution time among machines, ProbSky is adroit at curbing the bottleneck effect that causes severe system performance deterioration.

Near-Memory Processing in Action: Accelerating Personalized Recommendation With AxDIMM

Near-memory processing (NMP) is a prospective paradigm enabling memory-centric computing. By moving the compute capability next to the main memory (DRAM modules), it can fundamentally address the CPU-memory bandwidth bottleneck and thus effectively improve the performance of memory-constrained workloads. Using the personalized recommendation system as a driving example, we developed a scalable, practical DIMM-based NMP solution tailor-designed for accelerating the inference serving. Our solution is demonstrated on a versatile FPGA-enabled NMP platform called AxDIMM that allows rapid prototyping and evaluation of NMP’s performance potential on real hardware under a realistic system setting using industry-representative recommendation framework. We experimentally validated the performance of a two-ranked AxDIMM prototype, which achieves up to 1.89× speedup in latency and 31.6% memory energy saving for embedding operations. For end-to-end recommendation inference serving, AxDIMM improves the throughput up to 1.5× and latency-bounded throughput up to 1.77×, respectively.

Survey on personalised recommendation system development in big data analytics

Survey on personalised recommendation system development in big data analytics

Personalized Recommendation System Based on Social Relationships and Historical Behaviors

Personalized Recommendation System Based on Social Relationships and Historical Behaviors

Global Multi-Source Information Fusion Management and Deep Learning Optimization for Tourism

The purpose is to solve the problems of sparse data information, low recommendation precision and recall rate and cold start of the current tourism personalized recommendation system. First, a context based personalized recommendation model (CPRM) is established by using the labeled-LDA (Labeled Latent Dirichlet Allocation) algorithm. The precision and recall of interest point recommendation are improved by mining the context information in unstructured text. Then, the interest point recommendation framework based on convolutional neural network (IPRC) is established. The semantic and emotional information in the comment text is extracted to identify user preferences, and the score of interest points in the target location is predicted combined with the influence factors of geographical location. Finally, real datasets are adopted to evaluate the recommendation precision and recall of the above two models and their performance of solving the cold start problem.

DETERMINE RECOMMENDATION SYSTEMS TO SEARCH FOR BOOKS BY PREFERENCES OF WEB USERS

Currently, the question of state, formation and development of the information source interaction system, the scientific interaction and users' requestsin certain fields of activity remains relevant under the conditions of the development of the use of Internet services. Recommendation systems are oneof the types of artificial intelligence technologies for predicting parameters and capabilities.Due to the rapid increase in data on the Internet, it is becoming more difficult to find something really useful. And the recommendations offered by theservice itself may not always correspond to the user's preferences. The relevance of the topic is to develop a personal recommendation system forsearching books, which will not only reduce time and amount of unnecessary information, but also meet the user's preferences based on the analysis oftheir assessments and be able to provide the necessary information at the right time. All this makes resources based on referral mechanisms attractiveto the user. Such a system of recommendations will be of interest to producers and sellers of books, because it is an opportunity to provide personalrecommendations to customers according to their preferences.The paper considers algorithms for providing recommender systems (collaborative and content filtering systems) and their disadvantages.Combinations of these algorithms using a hybrid algorithm are also described. It is proposed to use a method that combines several hybrids in onesystem and consists of two elements: switching and feature strengthening. This made it possible to avoid problems arising from the use of each of thealgorithms separately.A literature web application was developed using Python using the Django and Bootstrap frameworks, as well as SQLite databases, and a system ofrecommendations was implemented to provide the most accurate suggestion. During the testing of the developed software, the work of the literatureservice was checked, which calculates personal recommendations for users using the method of hybrid filtering. The recommendation system wastested successfully and showed high efficiency.

Adaptive personalized recommender system using learning automata and items clustering

The personalized recommender systems provide user-related services based on user preferences; these preferences are recorded in an individual profile. Therefore, the more complete and precise each user profile leads more successful the recommendation process. The people’s interests change over time though traditional researches do not follow these changes regularly. Under such circumstances, designing an efficient user model to track users’ interests is greatly important. In the current study, we suggest an algorithm to create the learning automata-based user profiling. Due to many items and the commonality of features between them, we clustered items. In this technique, a learning automaton is assigned to the active user. The learning automaton adjusts the amount of user interest in each cluster based on user feedback. As the user interactions with the system increase, the internal state of the learning automaton converges towards the user’s genuine interests in the item clusters. The experimental results demonstrate that our algorithm outperforms compared approaches in precision, recall, RMSE, and MAE. In addition, the proposed algorithm for new users has acceptable performance.

Intelligent Tourism Personalized Recommendation Based on Multi-Fusion of Clustering Algorithms

Actual tourism mining models are often used to discover potential information in documents, but tourism models without human knowledge often produce unexplainable topics. This paper combines big data technology to build a personalized recommendation system for smart tourism, model the contextual information usage ontology under the tourism information system, and give the association between various ontologies. Then, this paper uses a matrix to describe each discrete attribute and interval attribute and uses a vector to model the user’s preferences. In addition, this paper constructs an intelligent recommendation system based on the actual needs of travel recommendation and verifies the system in combination with experimental research. Through experimental analysis, it can be known that the intelligent tourism personalized recommendation system based on big data technology proposed in this paper has a high practical effect.

Design of Personalized Recommendation System for Swimming Teaching Based on Deep Learning

Swimming is not only an entertaining hobby but also a sporting event. It is a sport for strengthening the body. Although there are many swimming coaches, there are different swimming teaching courses. However, choosing the right swimming instructor or course is the motivation for learning swimming activities. To this end, this paper conducts related research on the personalized recommendation system for swimming teaching based on deep learning with the purpose of improving the accuracy of the recommendation system to meet the needs of the users and promote the development of swimming events. This article mainly uses the experimental test method, the system construction method, and the questionnaire survey method to analyze and study the personalized swimming teaching system and the students’ attitude to it and draw a conclusion finally. The data results show that the accuracy of the system designed in this paper can meet the basic requirements. Hence, it can bring an excellent experience to the users. According to the questionnaire data, 85%–95% of people have great confidence in the personalized recommendation system.

Semantic Trajectory Analytics and Recommender Systems in Cultural Spaces

Semantic trajectory analytics and personalised recommender systems that enhance user experience are modern research topics that are increasingly getting attention. Semantic trajectories can efficiently model human movement for further analysis and pattern recognition, while personalised recommender systems can adapt to constantly changing user needs and provide meaningful and optimised suggestions. This paper focuses on the investigation of open issues and challenges at the intersection of these two topics, emphasising semantic technologies and machine learning techniques. The goal of this paper is twofold: (a) to critically review related work on semantic trajectories and knowledge-based interactive recommender systems, and (b) to propose a high-level framework, by describing its requirements. The paper presents a system architecture design for the recognition of semantic trajectory patterns and for the inferencing of possible synthesis of visitor trajectories in cultural spaces, such as museums, making suggestions for new trajectories that optimise cultural experiences.

A Data Mining Approach for Developing Online Streaming Recommendations

ABSTRACT Online streaming has become increasingly popular with the availability of broadband networks and the increase in computing power and electronic distribution. Online streaming operators have difficulty developing flexible business alternatives according to users’ changing streaming behaviors in terms of generating a good and profitable business model. In terms of e-commerce development, the live-streaming platform that provides streaming of the main merchandise to users, allowing the users to directly consume via live-streaming become critical issues. In this regard, personalized recommendation systems can use the user’s interests and purchasing behavior to recommend information and merchandise. Thus, this study investigates the online streaming experiences of Taiwanese consumers to evaluate online streaming users and their online purchase behaviors for developing online recommendations. This study uses a snowflake schema, which is an extension of the star schema. In addition, this study develops a rule-based recommendation approach for investigating online streaming and purchasing behaviors in terms of online recommendations. This study is the first to determine how online streaming proprietors and their affiliates are disseminated using online streaming consumer behaviors in terms of online recommendations for further electronic commerce development.