Design Of Recommendation System Research Articles

Research on the Design of Mass Recommendation System Based on Lambda Architecture

In the era of big data, in order to increasing the information data for conforms to the personalized needs of content, research scholars put forward based on the Lambda mass recommendation system architecture design, it can not only to the recessive and dominant behavior of users of the system data collection storage and research analysis, can also be based on the analysis of cascading hybrid algorithm to explore how to carry out real-time recommendation. Therefore, on the basis of understanding the research and development achievements of recommender systems at home and abroad in recent years, and based on the understanding and analysis of Lambda architecture and cascading hybrid algorithm, this paper aims at how to design a massive recommender system in line with users’ behavior, and makes clear the recommendation effect by combining with system testing.

Multicriteria recommendation based on bacterial foraging optimization

Recommender systems assist users to make decisions among a huge volume of options. Accuracy-oriented recommender systems focus on the prediction power of algorithms and neglect that users may appreciate diverse and novel recommendations in real-world scenarios. Thus, this paper proposed a multicriteria recommendation model that can optimize the recommendation accuracy, diversity, novelty, and individual tendency simultaneously. Additionally, a new multiobjective bacterial foraging optimization method is proposed to improve its searching capability and the performance of recommendation model. The proposed optimization-based multicriteria recommendation algorithm is compared with existing methods on both benchmark functions and real-world data sets. The results demonstrate that the proposed algorithm is superior to other recommendation algorithms in most cases. This study provides insights in recommendation system design and draws scholarly attention to the optimization-based recommendation strategy.

Fitness Exercise Recommendation System Using Weighted Products

The corona virus pandemic that has been going on since the beginning of 2020 has changed everyone's lifestyle. Even though we are allowed to do activities with health protocols, many people are used to being at home and not exercising so they gain weight. There are also those who want to do fitness exercises but are confused in choosing what type of exercise is appropriate. Doing exercise regularly can help us burn excess calories, but we can't just go on a diet, one of which is to train in the fitness center. So this study discusses the design of a fitness exercise recommendation system using the web-based Weighted Product method. The Weighted Product method evaluates several alternatives to several attributes or criteria, where each attribute is independent of each other. Based on the results of the evaluation of the recommendation system questionnaire to get a success rate of 83.89%, so it can be ascertained that the fitness training recommendation system is successful in implementing the Weighted Product method

A framework to shape the recommender system features based on participatory design and artificial intelligence approaches

<span lang="EN-GB">A recommender system is an algorithm aiming at giving suggestions to users on relevant elements or items such as products to purchase, books to read, jobs to apply or anything else depending on industries or situations. Recently, there has been a surge in interest in developing a recommender system in a variety of areas. One of the most widely used approaches in recommender systems is collaborative filtering (CF). The CF is a strategy for automatically creating a filter based on a user's needs by extracting desires or recommendation information from a large number of users. The CF approach uses multiple correlation steps to do this. However, the occurrence of uncertainty in finding the best similarity measure is unavoidable. This paper outlines a method for improving the configuration of a recommender system that is tasked with recommending an appropriate study field and supervisor to a group of final-year project students. The framework we suggest is built on a participatory design methodology that allows students' individual opinions to be factored into the recommender system's design. The architecture of the recommender scheme was also illustrated using a real-world scenario, namely mapping the students' field of interest to a possible supervisor for the final year project.</span>

The architecture design of information intelligent recommendation system for mobile communication terminal

In order to effectively generate product recommendation information for specific users and push it to all kinds of mobile terminal systems, combined with the application environment of recommendation system in e-commerce, the overall architecture of information recommendation system for mobile terminal is given, and the recommendation engine structure with multi engine strategy is designed and analyzed, Processing flow and two kinds of information push schemes for mobile terminals: user-defined recommendation technology and third-party recommendation platform. The proposed recommender system architecture has clear division, strong scalability and easy expansion, and has achieved good results in practical application.

Restaurant recommender system based on sentiment analysis

Today, exploiting sentiment analysis has become popular in designing recommender systems in various fields, including the restaurant and food area. However, most of the sentiment analysis-based restaurant recommender systems only use static information such as food quality, price, and service quality. The analysis of users’ opinions and the extraction of their food preferences lead to the provision of personalized recommendations, which is a research gap in literature; In this paper, a context-aware recommender system is proposed that extracts the food preferences of individuals from their comments and suggests restaurants in accordance with these preferences. For this purpose, the semantic approach is used to cluster the name of foods extracted from users’ comments and analyze their sentiments about them. Finally, nearby open restaurants are recommended based on their similarity to user preferences. For evaluation, the TripAdvisor website has been used and comments from 100 different users have been collected during the first 9 months of 2018. The precision, recall and f-measure of the system are measured in three scenarios of top1, top3, and top5. The results indicate that the proposed system can provide recommendations with a precision of 92.8%, giving users a high degree of precision. Besides, the system outperforms the previous research in these criteria.

A Recommender System for the Additive Manufacturing of Component Inventories Using Machine Learning

Abstract To appropriately leverage the benefits of additive manufacturing (AM), it would be advantageous if a printing could be guaranteed before allocating the necessary resources. Furthermore, when considering AM for an inventory of existing components traditionally fabricated through traditional means, such a guarantee could result in significant technical and economic advantages. To realize such advantages, this paper presents a platform that allows for a successful and efficient transition of part-inventories to AM. This is accomplished using a novel design recommender system supported by machine learning, capable of making suggestions towards effective design modifications. This system uses an automatic AM feasibility analysis of existing parts and a clustering of the parts based on similarities in their AM-feasibilities to develop a set of recommendations for those part clusters whose current designs are deemed as infeasible and/or inefficient for AM. The design modifications leverage a redesign algorithm to address not only problematic geometric issues but also potential infeasibilities associated with resource consumption. The utility of the presented modification algorithm is demonstrated using a number of case studies.

Users' Responsiveness to Persuasive Techniques in Recommender Systems.

Understanding user’s behavior and their interactions with artificial-intelligent-based systems is as important as analyzing the performance of the algorithms used in these systems. For instance, in the Recommender Systems domain, the accuracy of the recommendation algorithm was the ultimate goal for most systems designers. However, researchers and practitioners have realized that providing accurate recommendations is insufficient to enhance users’ acceptance. A recommender system needs to focus on other factors that enhance its interactions with the users. Recent researches suggest augmenting these systems with persuasive capabilities. Persuasive features lead to increasing users’ acceptance of the recommendations, which, in turn, enhances users’ experience with these systems. Nonetheless, the literature still lacks a comprehensive view of the actual effect of persuasive principles on recommender users. To fill this gap, this study diagnoses how users of different characteristics get influenced by various persuasive principles that a recommender system uses. The study considers four users’ aspects: age, gender, culture (continent), and personality traits. The paper also investigates the impact of the context (or application domain) on the influence of the persuasive principles. Two application domains (namely eCommerce and Movie recommendations) are considered. A within-subject user study was conducted. The analysis of (279) responses revealed that persuasive principles have the potential to enhance users’ experience with recommender systems. The study also shows that, among the considered factors, culture, personality traits, and the domain of recommendations have a higher impact on the influence of persuasive principles than other factors. Based on the analysis of the results, the study provides insights and guidelines for recommender systems designers. These guidelines can be used as a reference for designing recommender systems with users’ experience in mind. We suggest that considering the results presented in this paper could help to improve recommender-users interaction.

CourseQ: the impact of visual and interactive course recommendation in university environments

The abundance of courses available in a university often overwhelms students as they must select courses that are relevant to their academic interests and satisfy their requirements. A large number of existing studies in course recommendation systems focus on the accuracy of prediction to show students the most relevant courses with little consideration on interactivity and user perception. However, recent work has highlighted the importance of user-perceived aspects of recommendation systems, such as transparency, controllability, and user satisfaction. This paper introduces CourseQ, an interactive course recommendation system that allows students to explore courses by using a novel visual interface so as to improve transparency and user satisfaction of course recommendations. We describe the design concepts, interactions, and algorithm of the proposed system. A within-subject user study (N=32) was conducted to evaluate our system compared to a baseline interface without the proposed interactive visualization. The evaluation results show that our system improves many user-centric metrics including user acceptance and understanding of the recommendation results. Furthermore, our analysis of user interaction behaviors in the system indicates that CourseQ could help different users with their course-seeking tasks. Our results and discussions highlight the impact of visual and interactive features in course recommendation systems and inform the design of future recommendation systems for higher education.

A novel POI recommendation method based on trust relationship and spatial–temporal factors

Existing point-of-interesting (POI) recommendation methods lack sufficient integration of information related to the features of individual users and their corresponding contexts. Personalized location-based services built upon these methods have a few limitations such as low recommendation accuracy and untapped potential interests of users. To overcome these limitations, we propose an efficient POI recommendation method based on multiple factors (i.e., preference, social relationship, and spatial–temporal factors) to improve the recommendation accuracy and alleviate the cold-start and sparsity problems. Unlike existing methods, the proposed method considers the features of each factor. First, we identify direct and indirect trust relationships, upon which the improved trust relationship measurement methods are built respectively. Second, we fuse the comprehensive trust relationship, user preference, check-in time, and geographical location into a matrix factorization model. We pay special attention to the attraction of items, correlation between items, trajectory composed of locations, and the influence of interest-forgetting during the fusing process. Finally, we generate recommendation lists of POIs for the target users. With three real-world datasets, experimental and analytical results show that the proposed method outperforms existing methods, while alleviating the cold-start and sparsity problems that commonly hinder POI recommender systems. Theoretically, our study contributes to the effective usage of multidimensional data science and analytics for POI recommender system design. In practice, our results can be used to improve the quality of personalized POI recommendation services for websites and applications.

Understanding digitally enabled complex networks: a plural granulation based hybrid community detection approach

PurposeCommunities representing groups of agents with similar interests or functions are one of the essential features of complex networks. Finding communities in real-world networks is critical for analyzing complex systems in various areas ranging from collaborative information to political systems. Given the different characteristics of networks and the capability of community detection in handling a plethora of societal problems, community detection methods represent an emerging area of research. Contributing to this field, the authors propose a new community detection algorithm based on the hybridization of node and link granulation.Design/methodology/approachThe proposed algorithm utilizes a rough set-theoretic concept called closure on networks. Initial sets are constructed by using neighborhood topology around the nodes as well as links and represented as two different categories of granules. Subsequently, the authors iteratively obtain the constrained closure of these sets. The authors use node mutuality and link mutuality as merging criteria for node and link granules, respectively, during the iterations. Finally, the constrained closure subsets of nodes and links are combined and refined using the Jaccard similarity coefficient and a local density function to obtain communities in a binary network.FindingsExtensive experiments conducted on twelve real-world networks followed by a comparison with state-of-the-art methods demonstrate the viability and effectiveness of the proposed algorithm.Research limitations/implicationsThe study also contributes to the ongoing effort related to the application of soft computing techniques to model complex systems. The extant literature has integrated a rough set-theoretic approach with a fuzzy granular model (Kundu and Pal, 2015) and spectral clustering (Huang and Xiao, 2012) for node-centric community detection in complex networks. In contributing to this stream of work, the proposed algorithm leverages the unexplored synergy between rough set theory, node granulation and link granulation in the context of complex networks. Combined with experiments of network datasets from various domains, the results indicate that the proposed algorithm can effectively reveal co-occurring disjoint, overlapping and nested communities without necessarily assigning each node to a community.Practical implicationsThis study carries important practical implications for complex adaptive systems in business and management sciences, in which entities are increasingly getting organized into communities (Jacucci et al., 2006). The proposed community detection method can be used for network-based fraud detection by enabling experts to understand the formation and development of fraudulent setups with an active exchange of information and resources between the firms (Van Vlasselaer et al., 2017). Products and services are getting connected and mapped in every walk of life due to the emergence of a variety of interconnected devices, social networks and software applications.Social implicationsThe proposed algorithm could be extended for community detection on customer trajectory patterns and design recommendation systems for online products and services (Ghose et al., 2019; Liu and Wang, 2017). In line with prior research, the proposed algorithm can aid companies in investigating the characteristics of implicit communities of bloggers or social media users for their services and products so as to identify peer influencers and conduct targeted marketing (Chau and Xu, 2012; De Matos et al., 2014; Zhang et al., 2016). The proposed algorithm can be used to understand the behavior of each group and the appropriate communication strategy for that group. For instance, a group using a specific language or following a specific account might benefit more from a particular piece of content than another group. The proposed algorithm can thus help in exploring the factors defining communities and confronting many real-life challenges.Originality/valueThis work is based on a theoretical argument that communities in networks are not only based on compatibility among nodes but also on the compatibility among links. Building up on the aforementioned argument, the authors propose a community detection method that considers the relationship among both the entities in a network (nodes and links) as opposed to traditional methods, which are predominantly based on relationships among nodes only.

Design and Research of Mobile Learning Recommendation System Based on Deep Learning Perspective

With the advent of the era of big data, the amount of information shows an exponential explosive growth trend. At the same time, the personalized needs of users are increasing. Facing the massive data, how to help users quickly and accurately obtain the information they need is the main challenge facing them at present. With the rapid development of computer technology and communication technology, mobile teaching and deep learning have become new research hotspots in the field of education. Mobile education has become a very fashionable education mode, which not only brings modern people a more convenient education system, but also makes modern learners need not worry about learning time and content. Traditional recommendation methods generally have problems such as sparse data, cold start and difficulty in feature extraction. On the basis of expounding the concept, connotation and main characteristics of deep learning, this paper applies deep learning theory to mobile recommendation system, hoping to gradually improve and promote the existing mobile education and provide an effective learning way and method for learners.

Non-stationary Stochastic Multi-armed Bandit Problems with External Information on Stationarity

Multi-armed bandit problem is a fundamental mathematical problem in sequential optimization and reinforcementlearning that has a variety of application such as online recommendation system and clinical trial design. Multiarmedbandit problem can describe a situation in which a player tries to select a good choice sequentially from givencandidate choices to maximize the cumulative reward. In this paper, we consider the non-stationary multi-armed banditproblems. Non-stationary means the reward distribution of each arm varies with time. We point out that in somereal application, we can utilize information on the change of reward distribution. Especially we consider the type ofinformation that may restrict the rounds at which the reward distribution changes. Against such scenario, we proposea novel strategy called PM policy. The proposed policy is based on existing CUSUM-UCB policy and M-UCB policythat do not consider external information. Though such existing policies monitor all arms to detect the change ofreward distribution, our policy monitors only important arms and rounds. As a result, the ratio of unnecessary monitoringis reduced, and an efficient search can be performed. The regret bound of the proposed policy is described. Wealso show the effectiveness of the proposed method by numerical experiments.

Design and Implementation of Visual Employment Recommendation System

This paper studies the design and implementation of a visual employment recommendation system based on deep learning. The system mainly uses web crawler technology, word segmentation system jieba, Django framework, data analysis algorithm Kmeans, data visualization Echarts framework and other technologies. The system can make two-way precise recommendation for enterprises and fresh graduates to improve employment matching.

Multi level Recommendation System of College Online Learning Resources Based on Multi Intelligence Algorithm

In order to accurately grasp the actual search habits of online learning objects and establish a more reasonable multi-level recommendation scheme, a multi-level recommendation system of online learning resources in Colleges and Universities Based on multi intelligent algorithm is designed. With the help of the given topology, the multi-level recommendation behavior in the system is analyzed. Then combined with various types of Resource Recommendation application units, the hardware execution environment of the system is built. On this basis, a multiple intelligent neural network model is established. Combined with multi intelligent recommendation technology, the actual Resource Recommendation behavior related to learning objects is determined, and the software execution environment of the system is built. Combined with the structure of relevant hardware equipment, the design of multi-level recommendation system for online learning resources in colleges and universities based on multiple intelligent algorithms is completed. The experimental results show that the system designed in this paper records a larger number of learning objects, but the recommended waiting time is relatively shorter. The system can accurately grasp the actual search habits of online learning objects and establish a more reasonable multi-level recommendation implementation scheme.

Design of online course knowledge recommendation system based on improved learning diagnosis model

In order to better manage curriculum resources, give full play to the practical value and efficiency of university curriculum, provide an efficient and convenient interface for the majority of students and curriculum resources, and meet the different needs of different students in learning, this paper aims at the current content-based recommendation algorithm cannot solve the problem of user interest drift, online curriculum resource sharing platform cannot directly communicate with students this paper proposes a learning diagnosis method which combines the similarity of courses and user interest model to calculate the similarity. According to the actual characteristics and attributes of online course resources, and using the improved algorithm, this paper designs and implements a set of online course knowledge recommendation system based on the improved learning diagnosis model, to effectively alleviate the problems of low user interest and direct communication between online and offline classroom users in the current online course learning process.

Design and Analysis of a Cluster-Based Intelligent Hybrid Recommendation System for E-Learning Applications

Recently, different recommendation techniques in e-learning have been designed that are helpful to both the learners and the educators in a wide variety of e-learning systems. Customized learning, which requires e-learning systems designed based on educational experience that suit the interests, goals, abilities, and willingness of both the learners and the educators, is required in some situations. In this research, we develop an intelligent recommender using split and conquer strategy-based clustering that can adapt automatically to the requirements, interests, and levels of knowledge of the learners. The recommender analyzes and learns the styles and characteristics of learners automatically. The different styles of learning are processed through the split and conquer strategy-based clustering. The proposed cluster-based linear pattern mining algorithm is applied to extract the functional patterns of the learners. Then, the system provides intelligent recommendations by evaluating the ratings of frequent sequences. Experiments were conducted on different groups of learners and datasets, and the proposed model suggested essential learning activities to learners based on their style of learning, interest classification, and talent features. It was experimentally found that the proposed cluster-based recommender improves the recommendation performance by resulting in more lessons completed when compared to learners present in the no-recommender cluster category. It was found that more than 65% of the learners considered all criteria to evaluate the proposed recommender. The simulation of the proposed recommender showed that for learner size values of <1000, better metric values were produced. When the learner size exceeded 1000, significant differences were obtained in the evaluated metrics. The significant differences were analyzed in terms of a computational structure depending on L, the recommendation list size, and the attributes of learners. The learners were also satisfied with the accuracy and speed of the recommender. For the sample dataset considered, a significant difference was observed in the standard deviation σ and mean μ of parameters, in terms of the Recall (List, User) and Ranking Score (User) measures, compared to other methods. The devised method performed well concerning all the considered metrics when compared to other methods. The simulation results signify that this recommender minimized the mean absolute error metric for the different clusters in comparison with some well-known methods.

RETRACTED ARTICLE: User preference intelligent information recommendation system based on chaos genetic algorithm

There is a problem that the recommendation effect of user preference intelligent information recommendation is poor under the interference of strong noise. This paper presents a study on the user preference intelligent information recommendation system based on chaos genetic algorithm. Design of intelligent information recommendation system hardware architecture, the use of hypertext server will different space of text in the text of the information organization in the same network, according to the user submits the keyword search relevant information from the database, design preference recommendation subsystem, and through the client information query interface circuit, the query client information; According to the application principle of chaos sequence, the software introduces the perturbation chaos genetic mapping algorithm, and sets the intelligent information recommendation mechanism of users’ preference based on chaos genetic algorithm, and introduces the median filtering method to remove the strong noise in the recommendation system to ensure the integrity of the recommendation data. Through hardware and software cooperation to complete the user preference intelligent information recommendation system design. The experimental results show that the system has good throughput and high recommendation accuracy under the strong noise interference, which can meet the user's requirement of obtaining information efficiently.

The Impact of Randomized Algorithm over Recommender System

In today’s world, the amount of information is increasing drastically. There are 2.5 quintillion bytes of data generated each day at a current time. With this rapid increase of data, it is important to filter the huge amount of data from the existing unnecessary available storage. To handle this issue, the researchers have incorporated the concept of recommender system. Recommender system tries to filter the users’ data to come up with the exact amount of information required according to the needs and preferences of the users. Hence there is an increase in the demand of recommender system to tackle the data overload. However, the existing recommender systems do perform well but face some issues in the corporate world. Cold start, user bias, precision and speed are some of the major problems faced by recommender system which are yet to be addressed. To minimize such issues, we incorporate the concept of randomization such that random factor can be integrated in design of recommender systems. In this paper, we have also outlined the advantages as well as the procedure of using a randomized algorithm for recommender system. The proposed approach would provide a platform for researchers to enhance the recommender system using the randomized algorithms in the future.

Design of travel route recommendation system based on fast Spark artificial intelligence architecture

In order to overcome the problems of low accuracy of current system tourism route prediction and poor accuracy of route recommendation, this paper proposes a tourism route recommendation system design based on fast Spark artificial intelligence architecture. In the hardware part, the search process structure of tourist route, web page information classification and extraction module and tourist document classification process structure are designed. For the software part, firstly the problem is defined for the tourist route recommendation, secondly the Markov model is constructed, and finally the tourist route is mined. By ranking the transfer probability of tourist attractions, the tourist route can be obtained, and finally the tourist route recommendation is realised. The experimental results show that the designed system has a prediction accuracy of more than 70% and a recommendation accuracy of more than 80%, with high prediction accuracy and route recommendation accuracy.