Application Of Recommender Systems Research Articles

Food recommendation system based on nutritional needs of human beings and user preferences

Introduction: Nowadays the food types became so diverse and complicated, so human needs+ professional assistance to make his best choices especially after foods became global parameter. Food Recommendation System is a smart system that provides the best suggestions to the beneficiaries to know the best choices to their needs. Moreover, the human activities and lifestyle are affected by another types of dietaries in other foods. There is need for everybody to know what the nutrition is he/she needs. So, this research responding to these needs. The goal of the proposed system is to propose a system that provides recommendations for foods that are rich in nutritional components that people need in their daily lives based on computational model and expert preferences. Objective: The research aims to design and implement a food recommendation system has the ability to coordinate both user preferences and data clustering techniques to produce high accuracy recommendations. Material and methods: The proposed method focuses on merging computational model and user preferences to give the user the best recommended list of food options. Clustering techniques are approaches used in Recommendation system applications to group different foods according to the similarities in nutrition values.

An Adaptive Recommendation Algorithm of Intelligent Clothing Design Elements Based on Large Database

In the recent years, the developmental speed of intelligent technology continues to accelerate, and the research on the actual needs of users is also in depth. From the current situation of the clothing industry, how to combine artificial intelligence (AI) technology with clothing fashion has become the focus of customer’s attention. The application of intelligent clothing matching recommendation system (online) can effectively meet the needs of customers in dressing matching, so as to save a lot of time and energy (offline). With the maturity of artificial intelligence, machine learning, and other emerging computational technologies, the intelligent clothing matching system has laid a solid foundation. In this paper, several intelligent clothing matching recommendation systems that have been applied at present are deeply analyzed. Moreover, the basic algorithms and key technologies are elaborated in detail. In addition, the future research direction is found, so that the clothing matching recommendation system can be more personalized, and the comprehensive function is greatly improved in order to bring more ideal benefits.

Extended missing data imputation via GANs for ranking applications

We propose Conditional Imputation GAN, an extended missing data imputation method based on Generative Adversarial Networks (GANs). The motivating use case is learning-to-rank, the cornerstone of modern search, recommendation system, and information retrieval applications. Empirical ranking datasets do not always follow standard Gaussian distributions or Missing Completely At Random (MCAR) mechanism, which are standard assumptions of classic missing data imputation methods. Our methodology provides a simple solution that offers compatible imputation guarantees while relaxing assumptions for missing mechanisms and sidesteps approximating intractable distributions to improve imputation quality. We prove that the optimal GAN imputation is achieved for Extended Missing At Random (EMAR) and Extended Always Missing At Random (EAMAR) mechanisms, beyond the naive MCAR. Our method demonstrates the highest imputation quality on the open-source Microsoft Research Ranking (MSR) Dataset and a synthetic ranking dataset compared to state-of-the-art benchmarks and across various feature distributions. Using a proprietary Amazon Search ranking dataset, we also demonstrate comparable ranking quality metrics for ranking models trained on GAN-imputed data compared to ground-truth data.

New Hybrid Techniques for Business Recommender Systems

Besides the typical applications of recommender systems in B2C scenarios such as movie or shopping platforms, there is a rising interest in transforming the human-driven advice provided, e.g., in consultancy via the use of recommender systems. We explore the special characteristics of such knowledge-based B2B services and propose a process that allows incorporating recommender systems into them. We suggest and compare several recommender techniques that allow incorporating the necessary contextual knowledge (e.g., company demographics). These techniques are evaluated in isolation on a test set of business intelligence consultancy cases. We then identify the respective strengths of the different techniques and propose a new hybridisation strategy to combine these strengths. Our results show that the hybridisation leads to substantial performance improvement over the individual methods.

Effects of Feature-Based Explanation and Its Output Modality on User Satisfaction With Service Recommender Systems.

Recent advances in natural language based virtual assistants have attracted more researches on application of recommender systems (RS) into the service product domain (e.g., looking for a restaurant or a hotel), given that RS can assist users in more effectively obtaining information. However, though there is emerging study on how the presentation of recommendation (vocal vs. visual) would affect user experiences with RS, little attention has been paid to how the output modality of its explanation (i.e., explaining why a particular item is recommended) interacts with the explanation content to influence user satisfaction. In this work, we particularly consider feature-based explanation, a popular type of explanation that aims to reveal how relevant a recommendation is to the user in terms of its features (e.g., a restaurant's food quality, service, distance, or price), for which we have concretely examined three content design factors as summarized from the literature survey: feature type, contextual relevance, and number of features. Results of our user studies show that, for explanation presented in different modalities (text and voice), the effects of those design factors on user satisfaction with RS are different. Specifically, for text explanations, the number of features and contextual relevance influenced users' satisfaction with the recommender system, but the feature type did not; while for voice explanations, we found no factors influenced user satisfaction. We finally discuss the practical implications of those findings and possible directions for future research.

Impact of Centrality Measures on the Common Neighbors in Link Prediction for Multiplex Networks.

Complex networks are representations of real-world systems that can be better modeled as multiplex networks, where the same nodes develop multi-type connections. One of the important concerns about these networks is link prediction, which has many applications in social networks and recommender systems. In this article, similarity-based methods such as common neighbors (CNs) are the mainstream. However, in the CN method, the contribution of each CN in the likelihood of new connections is equally taken into account. In this work, we propose a new link prediction method namely Weighted Common Neighbors (WCN), which is based on CNs and various types of Centrality measures (including degree, k-core, closeness, betweenness, Eigenvector, and PageRank) to predict the formation of new links in multiplex networks. So, in this model, each CN has a different impact on the node connection likelihood. Moreover, we investigate the impact of interlayer information on improving the performance of link prediction in the target layer. Using Area under the ROC Curve and precision as evaluation metrics, we perform a comprehensive experimental evaluation of our proposed method on seven real multiplex networks. The results validate the improved performance of our proposed method compared with existing methods, and we show that the performance of proposed methods is significantly improved while using interlayer information in multiplex networks.

Design and Application of Handicraft Recommendation System Based on Improved Hybrid Algorithm

With the rapid development of online e-commerce, traditional collaborative filtering algorithms have the disadvantages of data set reduction and sparse matrix filling cannot meet the requirements of users. This paper takes handicrafts as an example to propose the design and application of handicraft recommendation system based on an improved hybrid algorithm. Based on the theory of e-commerce system, through the traditional collaborative filtering algorithm of users, the personalized e-commerce system of hybrid algorithm is designed and analyzed. The personalized e-commerce system based on hybrid algorithm is further proposed. The component model of the business recommendation system and the specific steps of the improved hybrid algorithm based on user information are given. Finally, an experimental analysis of the improved hybrid algorithm is carried out. The results show that the algorithm can effectively improve the effectiveness and exemption of recommending handicrafts. What’s more, it can reduce the user item ratings of candidate set and improve accuracy of the forecast recommendation.

Application of recommender systems and time series models to monitor quality at HIV/AIDS health facilities

Abstract The US government invests substantial sums to control the HIV/AIDS epidemic. To monitor progress toward epidemic control, PEPFAR, or the President’s Emergency Plan for AIDS Relief, oversees a data reporting system that includes standard indicators, reporting formats, information systems, and data warehouses. These data, reported quarterly, inform understanding of the global epidemic, resource allocation, and identification of trouble spots. PEPFAR has developed tools to assess the quality of data reported. These tools made important contributions but are limited in the methods used to identify anomalous data points. The most advanced consider univariate probability distributions, whereas correlations between indicators suggest a multivariate approach is better suited. For temporal analysis, the same tool compares values to the averages of preceding periods, though does not consider underlying trends and seasonal factors. To that end, we apply two methods to identify anomalous data points among routinely collected facility-level HIV/AIDS data. One approach is Recommender Systems, an unsupervised machine learning method that captures relationships between users and items. We apply the approach in a novel way by predicting reported values, comparing predicted to reported values, and identifying the greatest deviations. For a temporal perspective, we apply time series models that are flexible to include trend and seasonality. Results of these methods were validated against manual review (95% agreement on non-anomalies, 56% agreement on anomalies for recommender systems; 96% agreement on non-anomalies, 91% agreement on anomalies for time series). This tool will apply greater methodological sophistication to monitoring data quality in an accelerated and standardized manner.

Urdu Wikification and Its Application in Urdu News Recommendation System

Wikification is the process of linking the entities found in a sample text to their individual Wikipedia or Wikidata pages. Many natural language processing applications, including question-answering systems, information retrieval, fraud detection, and recommendation systems(RS), can benefit from this information extraction technique. There has been a great deal of effort put towards entity-linking(EL) for both Asian and Western languages, with several datasets and numerous proposed methodologies. Despite millions of Urdu language users globally, the entity-linking for the Urdu language has not been worked on, to the best of our knowledge. This work proposes an Urdu EL pipeline to identify named entities in text and link them to Wikidata. Secondly, a dataset of 550 Urdu news titles relating to their respective Wiki-ids has been prepared for the examination. Third, utilizing the proposed EL pipeline, 16738 news articles from the first-ever Urdu news RS dataset of 100 users are annotated. Fourthly, a sub Knowledge graph (KG) of 8439 entities and 23080 relationship tuples is retrieved from Wikidata. The Trans-E algorithm is then used to create KG embeddings so that the extracted KG may be used in an Urdu news RS. The final accuracy of Urdu news RS is 60.8%.

Research on the Construction and Application of Recommendation System of Educational Knowledge Map

Research on the Construction and Application of Recommendation System of Educational Knowledge Map

Trust Recommendation Based on Deep Deterministic Strategy Gradient Algorithm

Trust recommendation is a vital recommendation system application based on social networks. It can recommend items based on the trust between users, which can alleviate data sparseness and enhance the interpretability of results. A large number of recommendation algorithms have been proposed, but most of them believe that trust is fixed, ignoring the changes of trust in the process of interaction. In addition, deep learning models are good at solving complex tasks and processing high-dimensional data, and they can model recommendation algorithms, but they are insufficient in capturing changes in user preferences timely. Therefore, given the shortcomings of existing researches, we propose a DDPG-TR algorithm based on deep reinforcement learning to capture the changes in user preferences and update the trust between users. The algorithm uses deep deterministic policy gradient algorithm DDPG to model the user-item interaction process. Firstly, we improve a state representative structure to express the user’s state, which is convenient to capture the changes in user preferences. Then, when the user accepts the recommendation, algorithm combines trust and similar information to predict item score, as well as calculates the difference of the score. Finally, Agent gets the score feedback and uses the difference to update trust. Experiments are conducted on three datasets, and they are verified that the DDPG-TR algorithm can provide more accurate recommendation results, compared with other recommendation algorithms.

Key Technologies and Discrete Dynamic Modeling Analysis of Online Travel Planning System Based on Big Data Scenario Aware Service

The key technology of online travel recommendation system has been widely concerned by many Internet experts. This paper studies and designs a scenario aware service model in online travel planning system and proposes an online travel planning recommendation model which integrates collaborative filtering and clustering personalized recommendation algorithm. At the same time, the algorithm performance test method and model evaluation index are given. The results show that CTTCF algorithm can find more neighbor users than UCF algorithm, and the smaller the search space is, the more significant the advantage is. The number of neighbors is 5, 10, 15, 20, and 25, respectively, and the corresponding average absolute error values are about 0.815, 0.785, 0.765, 0.758, and 0.755, respectively. The scores of the six emotional travel itinerary recommendation schemes are all higher than 142 points. Only the two schemes have no obvious rendering effect. The proposed online travel itinerary planning scheme has potential value and important significance in the application of follow-up recommendation system. It solves the problem of low scene perception satisfaction in the key technologies of online tourism planning system.

Multi-view Graph Attention Network for Travel Recommendation

As an e-commerce feature, the recommender system can enhance the consumer shopping experience and create huge benefits for businesses. The e-tourism has become one of the largest service industries with the application and popularity of recommender systems. Many studies have confirmed that the travel product recommendation is widely different from traditional recommendations. Due to the financial and time costs, travel products are usually browsed and purchased relatively infrequently compared with other traditional products (e.g., books, movies and food). In addition, choosing the appropriate travel product will be influenced by many factors, such as departure, destination and price. To tackle this challenging problem, we propose a MV-GAN (short for Multi-View Graph Attention Network for travel recommendation) model. It enriches user and product semantics through both metapath-guided neighbors aggregation and multi-view fusion in heterogeneous travel product recommendation graph. In particular, we design node-level and path-level attention networks for learning user and product representations from every single view. To collaboratively integrate multiple types of relationships in different views, a view-level attention mechanism is proposed to aggregate the node representations and obtain global user and product representations. We evaluate the proposed method on a public dataset and a dataset constructed from a large tourism e-commerce website in China. Extensive experiments not only validate the effectiveness of MV-GAN, but also show its potentially good interpretability.

A novel user review-based contextual recommender system

Recommendation systems are shrewd applications for knowledge mining that profoundly handle the problem of data overload. Various literature explores different philosophies to create ideas and recommends different strategies according to the needs of customers. Most of the work in the suggested structure space focuses on extending the accuracy of the recommendation by using a few possible methods where the principle purpose remains to improve the accuracy of suggestions while avoiding other plan objectives, such as the particular situation of a client. By using appropriate customer rating data, the biggest test for a suggested system is to generate substantial proposals. A setting is an enormous concept that can think of numerous points of view: for example, the community of friends of a client, time, mindset, environment, organization, type of day, classification of an item, description of the object, place, and language. The rating behavior of customers typically varies in different environments. We have proposed a new review-based contextual recommender (RBCR) system application from this line of analysis, in particular a novel recommender system, which is an adaptable, quick, and accurate piece planning framework that perceives the significance of setting and fuses the logical data using piece stunt while making expectations. We have contrasted our suggested calculation with pre- and post-sifting methods as they have been the most common methodologies in writing to illuminate the issue of setting conscious suggestion. Our studies show that considering the logical data, the display of a system will increase and provide better, appropriate and important results on various evaluation measurements.

Recommendations for marathon runners: on the application of recommender systems and machine learning to support recreational marathon runners

Every year millions of people, from all walks of life, spend months training to run a traditional marathon. For some it is about becoming fit enough to complete the gruelling 26.2 mile (42.2 km) distance. For others, it is about improving their fitness, to achieve a new personal-best finish-time. In this paper, we argue that the complexities of training for a marathon, combined with the availability of real-time activity data, provide a unique and worthwhile opportunity for machine learning and for recommender systems techniques to support runners as they train, race, and recover. We present a number of case studies—a mix of original research plus some recent results—to highlight what can be achieved using the type of activity data that is routinely collected by the current generation of mobile fitness apps, smart watches, and wearable sensors.

Recommender Systems in the Real Estate Market—A Survey

The shift to e-commerce has changed many business areas. Real estate is one of the applications that has been affected by this modern technological wave. Recommender systems are intelligent models that assist users of real estate platforms in finding the best possible properties that fulfill their needs. However, the recommendation task is substantially more challenging in the real estate domain due to the many domain-specific limitations that impair typical recommender systems. For instance, real estate recommender systems usually face the clod-start problem where there are no historical logs for new users or new items, and the recommender system should provide recommendations for these new entities. Therefore, the recommender systems in the real estate market are different and substantially less studied than in other domains. In this article, we aim at providing a comprehensive and systematic literature review on applications of recommender systems in the real estate market. We evaluate a set of research articles (13 journal and 13 conference papers) which represent the majority of research and commercial solutions proposed in the field of real estate recommender systems. These papers have been reviewed and categorized based on their methodological approaches, the main challenges that they addressed, and their evaluation procedures. Based on these categorizations, we outlined some possible directions for future research.

Binary Classification With XOR Queries: Fundamental Limits and an Efficient Algorithm

We consider a query-based data acquisition problem for binary classification of unknown labels, which has diverse applications in communications, crowdsourcing, recommender systems and active learning. To ensure reliable recovery of unknown labels with as few number of queries as possible, we consider an effective query type that asks “group attribute” of a chosen subset of objects. In particular, we consider the problem of classifying $m$ binary labels with XOR queries that ask whether the number of objects having a given attribute in the chosen subset of size $d$ is even or odd. The subset size $d$ , which we call query degree, can be varying over queries. We consider a general noise model where the accuracy of answers on queries changes depending both on the worker (the data provider) and query degree $d$ . For this general model, we characterize the information-theoretic limit on the optimal number of queries to reliably recover $m$ labels in terms of a given combination of degree- $d$ queries and noise parameters. Further, we propose an efficient inference algorithm that achieves this limit even when the noise parameters are unknown.

Application of hybrid metaheuristic with perturbation-based K-nearest neighbors algorithm and densest imputation to collaborative filtering in recommender systems

Since the rise of E-commerce companies and many other web services, the applications of recommender systems have been adopted more broadly than ever before. Although collaborative filtering is the most well-known approach which utilizes customer’s preference to discover their interest, the problems of data sparsity and similarities selection still exist in it. Thus, this study intends to propose a hybrid metaheuristic with perturbation-based K-nearest neighbors and densest imputation for collaborative filtering (KDI-KNN) algorithm to reduce the effects of data sparsity. A similarities union function is proposed to determine the fittest similarity and enhance the prediction performance. Eventually, the experimental results indicate that hybrid metaheuristics with perturbation-based KDI-KNN algorithms are superior to basic KNN, original KDI-KNN, and most single metaheuristic-based KDI-KNN. In addition, a real-world dataset, fund transaction dataset is adopted in the case study. The analysis reveals that the similarity is seriously affected by the different content of the dataset.

Pola Beli Konsumen Menggunakan Algoritma Fp-Growth Untuk Rekomendasi Promosi Pada Aneka Jaya Motor

A piece of appropriate information can create and establish a business strategy in increasing sales through technology that can affect the trade-in buying and selling goods with the data information generated can be calculated in detail and accurately. At Aneka Jaya Motor Semarang, this was triggered by the demand for competition. One solution is a product promotion target. For determining which items are feasible for promotion, the application of a promotional decision recommendation system is made using data mining techniques associated with FP-Growth algorithms, its function is to find items that are often purchased simultaneously by consumers. Data used in the form of transaction data with the total amount used 501 data. The results obtained by appearing 1 rule is if consumers buy spark plug parts then buy oil parts with minimum support of 10% and minimum confidence of 35%. The lift ratio obtained is 1 so that valid rules are generated.

An Improved Recommendation Method Based on Content Filtering and Collaborative Filtering

With the popularization of the Internet and the prevalence of online marketing, e-commerce systems provide enterprises with unlimited display space and provide customers with more product choices, while its structure is becoming increasingly complex. The emergence and application of the network marketing recommendation system have greatly improved this series of problems. It can effectively retain customers, prevent customer loss, and increase the cross-selling volume of the e-commerce system. However, the current network marketing recommendation system is still immature in practical applications, and the problem of data sparseness is serious. The problem of user interest drift is not well dealt with, resulting in poor recommendation quality and poor real-time recommendation. Therefore, this paper proposes an online marketing recommendation algorithm based on the integration of content and collaborative filtering. First, content-based methods are used to discover users’ existing interests. After that, the mixed similarity model of content and behaviour is used to find the similar user group of the target user, predict the user’s interest in the feature words, and discover the user’s potential interest. Then, the user’s existing interest and potential interest are merged to obtain a user interest model that is both personalized and diverse. Finally, the similarity between the marketing content and the fusion model is calculated to form a set of user ratings combined with characteristics and then clustered through K-means to finally achieve recommendation. Experiments have proved that this method has good recommendation performance.