Online Product Recommendation System Using Multi Scenario Demographic Hybrid (MDH) Approach

IntroductionNovel mechanisms of service delivery are needed to expand access to pre-exposure prophylaxis (PrEP) for HIV prevention. Providing PrEP directly through pharmacies could offer an additional option for reaching potential...

Wireless technology is a revolutionary advancement providing users with ubiquitous data and telephony access anywhere and anytime with no physical (PHY) connection. The currently deployed wireless networks like Wi-Fi, Worldwide Interoperability for Microwave Access (WiMAX), Universal Mobile Telecommunications Systems (UMTS), and Long Term Evolution (LTE) systems have different characteristics that make them complementary in terms of performance, coverage, and cost. This network variety presents an opportunity to provide better services to end users, given the advances in mobile terminals (MTs). To reach this goal, an appropriate automatic network selection (ANS) mechanism, which is able to always select the best access network, is needed. This consists of constantly monitoring any type of available access network, i.e., automatically selecting and switching to the best one, as the network that maximizes the users quality of experience, taking into account their preferences and the terminal and network conditions. ANS is a multidimensional decision-making problem that can be solved by finding an appropriate complex tradeoff between possibly conflicting criteria. In this paper, we propose an analytical model to capture the preferences of end users. Based on this model, we design an ANS mechanism that takes into account all aspects of the tradeoff between the quality of the connections, the preferences of the end users, and the cost. To highlight the benefits of our approach from the perspectives of both end users and network operators, we have implemented and tested the solution in a multitechnology simulator. Results show that the proposed solution outperforms mainstream approaches.

User behaviour can be extracted from the web pages accessed by the end user while browsing the internet. This extracted behaviour helps in providing recommendations to the end user. This paper presents mining of frequent itemsets and refinement of usage clusters for web page recommendation. The not so interesting recommendations obtained from a cluster are in abundance due to large number of sessions in a cluster. To solve such a problem we intend to refine clusters on the basis of weighted frequent itemsets that in turn help to generate improved quality refined clusters. After getting refined clusters, the same can be used for a number of applications such as personalisation on the basis of interests of the end user, improvement in website structure and improving the accuracy of a recommender system. The accuracy of a centroid-based recommender system is evaluated using original and refined clusters.

Recommender systems (RS) have been successfully explored in a vast number of domains, e.g. movies and tv shows, music, or e-commerce. In these domains we have a large number of datasets freely available for testing and evaluating new recommender algorithms. For example, Movielens and Netflix datasets for movies, Spotify for music, and Amazon for e-commerce, which translates into a large number of algorithms applied to these fields. In scientific fields, such as Health and Chemistry, standard and open access datasets with the information about the preferences of the users are scarce. First, it is important to understand the application domain, i.e. the recommended item is. Second, who are the end users: researchers, pharmacists, clinicians or policy makers. Third, the availability of data. Thus, if we wish to develop an algorithm for recommending scientific items, we do not have access to datasets with information about the past preferences of a group of users. Given this limitation, we developed a methodology, called LIBRETTI - LIterature Based RecommEndaTion of scienTific Items, whose goal is the creation of datasets, related with scientific fields. These datasets are created based on the major resource of knowledge that Science has: scientific literature. We consider the users as the authors of the publications, the items as the scientific entities (for example chemical compounds or diseases), and the ratings as the number of publications an author wrote about an entity. In this tutorial we will approach state-of-the-art recommender systems in scientific fields, explain what is Named Entity Recognition/Linking (NER/NEL) in research literature, and to demonstrate how to create a dataset for recommending drugs and diseases through research literature related to COVID-19. Our goal is to spread the use of LIBRETTI methodology in order to help in the development of recommender algorithms in scientific fields. These datasets are created based on the major resource of knowledge that Science has: scientific literature. We consider the users as the authors of the publications, the items as the scientific entities (for example chemical compounds or diseases), and the ratings as the number of publications an author wrote about an entity. In this tutorial we will approach state-of-the-art recommender systems in scientific fields, explain what is Named Entity Recognition/Linking (NER/NEL) in research literature, and to demonstrate how to create a dataset for recommending drugs and diseases through research literature related to COVID-19. Our goal is to spread the use of LIBRETTI methodology in order to help in the development of recommender algorithms in scientific fields. More info about the tutorial at https://lasigebiotm.github.io/RecSys.Scifi/.

This work presents preliminary analysis and design of the tourism recommender system. The work considers Pokhara city of Nepal as the domain of study as it is the second-largest city and tourism capital of Nepal. The work is built on data from travel websites, published and unpublished reports, literature, and a pilot study conducted in the same city. Tourist decision factors and motivational factors along with seven demographic data are taken into consideration to build an open-end questionnaire for the study. A total sample of 250 respondents is taken as part of the pilot study. The study uses kNN as the classification algorithm to form clusters of data and calculate silhouette to confirm the consistency and validity of clusters. Scatter plots with different combinations of data, UML-based diagrams, and decision tree are used to come up with an initial recommender system design. The work is vital for the design and development of recommendation systems based on a model, mixed, and customized approach. As the requirements directly come from the user end, the system is more specific in terms of recommendations especially for tourists for a specific destination that can be further elaborated to build a generalized model. This work produces an initial system, a knowledge base, different design diagrams, and a fully functional algorithm for the recommender system that can be used by individuals, business entities, and tourism organizations as a tool and knowledge base.KeywordsRecommender systemTourismPokharaNepalClusters

The UofA* User Interface Management System (UIMS) generates graphical user interfaces based on a high-level description of semantic commands supported by the application. A main part of the UIMS, called Chisel, generates the presentation component of interfaces. Chisel selects interaction techniques, determines their attributes, and places them on the screen of the display device. While doing so it is capable of considering device properties, end user's preferences, and interface designer's guidelines. The aim of this paper is to discuss in detail the design and implementation of Chisel.

The language reading direction is probably one of the most determinant factors influencing the successful internationalization of graphical user interfaces, beyond their mere translation. Western languages are read from left to right and top to bottom, while Arabic languages and Hebrew are read from right to left and top to bottom, and Oriental languages are read from top to bottom. In order to address this challenge, we introduce flippable user interfaces that enable the end user to change the reading direction of a graphical user interface by flipping it into the desired reading direction by direct manipulation. This operation automatically and dynamically changes the user interface layout based on a generalized concept of reading direction and translates it according to the end user's preferences.

Предметом вивчення в статті є процеси формування рекомендованого списку товарів та послуг в ситуації циклічного холодного старту рекомендаційної системи. Така ситуація характеризується циклічною зміною інтересів користувачів, що потребує уточнення вхідних даних для побудови рекомендацій. Метою є розробка методу доповнення вхідних даних для побудови рекомендацій непостійним користувачам, що змінюють свої вимоги, з використанням темпоральних обмежень типу «Next». Завдання: виділити базові особливості темпоральних залежностей в рекомендаційних системах; розробити концепцію коригування вхідних даних у ситуації циклічного холодного старту; розробити метод доповнення вхідних даних на основі темпоральних обмежень типу «Next». Отримані наступні результати. Виконано структуризацію темпоральних залежностей з урахуванням особливості їх застосування в рекомендаційних системах. Показано, що для опису поведінки користувача в таких системах можна використовувати залежності типу «Next» між послідовним у часі вибором одного й того ж об’єкту, а також залежності типу «Future» з проміжним вибором інших товарів або послуг. Запропоновано концептуальний підхід до уточнення вхідних даних на основі темпоральних залежностей. В рамках представленого концептуального підходу запропоновано метод доповнення вхідних даних рекомендаційної системи на основі формування темпоральних обмежень типу «Next». Висновки. Наукова новизна отриманих результатів полягає в наступному. Запропоновано метод доповнення вхідних даних рекомендаційної системи в ситуації циклічного холодного старту з використанням темпоральних обмежень типу «Next». Метод містить етапи узагальнення вхідних даних, формування темпоральних обмежень типу «Next», а також доповнення вхідних даних згідно отриманих обмежень. Запропонований метод дозволяє підвищити ефективність побудови рекомендацій для непостійних користувачів на основі формування обмежень, що відображають обов’язкові зміни інтересів відомих користувачів.

Recommender systems play an important role in most modern e-commerce applications. They have allowed users to become aware of the myriad choices available to them. The ease of information and the abundance of options have helped users make educated decisions. A recommender system studies a user's preferences and continues learning the user's changing interests, so as to suggest items that incline with the user's interests. In cases where a user is new to the application, or the user prefers not to discourse preferences, the recommender system is unable to gather the user's preference on any item. This is called the cold start problem; wherein the system can make valid recommendations only once the user starts informing the system about his/her choices. In this paper, we discuss the challenges faced by the cold start problem and how this problem may be alleviated using social media. We suggest an approach where we collect public information from users' social media accounts and analyze this information to understand their preferences. In particular, we gather the new user's information using their Twitter profile; i.e., the user's interest and preferences are extracted from his/her Twitter profile by analyzing his/her tweets. These interests will help the system understand what kind of movies the user will be most interested in. We compare these preferences with the metadata about the individual items. Using this approach, we develop a movie recommendation system wherein we produce top-N movie recommendations for a user. We used the MovieTweetings dataset to model the application. Two sets of results have been produced. In the first, smaller set of 770 users, 72.67% of users have received 100% accurate movie recommendations while nearly 80% of users got more than 75% accuracy. For the second, larger set of more than 3,500 users, 53 % of users have received 100% accurate recommendations while 72% of users got more than 75% accuracy. These encouraging results have demonstrated that the approach is effectively in alleviating cold start problems in recommendation systems, and may be applicable to many other e-commerce applications.

Social trust model for rating prediction in recommender systems: Effects of similarity, centrality, and social ties

Recommender systems have become increasingly accurate in suggesting content to users, resulting in users primarily consuming content through recommendations. This can cause the user's interest to narrow toward the recommended content, something we refer to as preference amplification. While this can contribute to increased engagement, it can also lead to negative experiences such as lack of diversity and echo chambers. We propose a theoretical framework for studying such amplification in a matrix factorization based recommender system. We model the dynamics of the system, where users interact with the recommender systems and gradually drift'' toward the recommended content, with the recommender system adapting, based on user feedback, to the updated preferences. We study the conditions under which preference amplification manifests, and validate our results with simulations. Finally, we evaluate mitigation strategies that prevent the adverse effects of preference amplification and present experimental results using a real-world large-scale video recommender system showing that by reducing exposure to potentially objectionable content we can increase user engagement by up to 2%.

Overcoming barriers to proactive plastic recycling toward a sustainable future

Over the past decade, internet usage has grown rapidly, creating opportunities for various online businesses to expand. One of the most significant areas of growth is e-commerce. As more people shop online, recommendation systems have become essential. These systems personalize the user experience, help customers find products quickly, and boost sales for businesses. However, building an effective recommendation system comes with challenges, especially when handling large amounts of data. A recommendation system suggests products to users based on their interests. It identifies the right items by analyzing past purchases, browsing history, and product details. Many recommendation systems generate long lists of suggested items, ranking them based on predicted user preferences. Recommendations can be based on factors like price, location, wish-listed items, cart history, search history, and past purchases. A well-designed recommendation system improves customer satisfaction, increases sales, and enhances user engagement. This research will develop to create a recommendation system for both new and existing users. It will use Neighborhood-Based Collaborative Filtering to suggest products based on user ratings and past purchases. Additionally, the system will remove duplicate ratings to provide more accurate and relevant recommendations based on user interests.

In today's information-rich environment, recommendation systems play a pivotal role in helping users efficiently navigate vast amounts of data, with applications spanning online platforms, e-commerce, streaming services, and digital advertising. Among various algorithms, Thompson Sampling (TS) stands out for its ability to balance exploring new options with leveraging known preferences, thereby preventing recommendation stagnation and adapting to shifts in user interests. This paper investigates TS within the context of advertising and movie recommendations, exploring its Bayesian probabilistic foundation, uncertainty modeling, practical integration within recommendation architectures, handling of large-scale data streams, and real-time feedback mechanisms. Performance is evaluated through metrics including accuracy, user engagement, click-through rates, and conversion rates, with comparisons against ETC and UCB algorithms. Results demonstrate that TS enhances recommendation accuracy, user satisfaction, and system revenue, establishing its position as a valuable and versatile tool for improving modern recommendation systems. Furthermore, the study provides insights into practical deployment strategies for real-world applications.

Emerging tourist destinations face several challenges, including a lack of data on travellers, as well as indicators to strengthen the competitiveness of the city’s services, reliability and tourist security. This paper describes a recommender system based on multi-criteria logic aggregation operators called Jimataa. The method of analysis is based on graded conjunctive-disjunctive operators and asymmetric assignment of a penalty or reward score regarding mandatory and optional criteria satisfaction. Several criteria are included for the evaluation of suitability, such as location, category, food specialities, other visitors scores, or services available. This system has deployed in Riohacha (Colombia) and it has been tested with end users by means of a survey. The web recommender system received a satisfaction rating of 86.42%, and the ease of data entry was rated with 77.78%. In the data entry process, 91.36% of users gave a maximum rating. These results indicate the efficiency of the system both in its recommendations and in the simplicity of the data entry process. This web recommender is a new technological product that can help emerging destinations by providing visitors with personalised, user-oriented tools and, on the other hand, improving the management of tourist services when providers do not have the capacity to maintain information on traditional platforms or social networks.