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

Author(s):  
Mariia Kozulia ◽  
Vladyslava Sushko

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.

2022 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Imen Gmach ◽  
Nadia Abaoub ◽  
Rubina Khan ◽  
Naoufel Mahfoudh ◽  
Amira Kaddour

PurposeIn this article the authors will focus on the state of the art on information filtering and recommender systems based on trust. Then the authors will represent a variety of filtering and recommendation techniques studied in different literature, like basic content filtering, collaborative filtering and hybrid filtering. The authors will also examine different trust-based recommendation algorithms. It will ends with a summary of the different existing approaches and it develops the link between trust, sustainability and recommender systems.Design/methodology/approachMethodology of this study will begin with a general introduction to the different approaches of recommendation systems; then define trust and its relationship with recommender systems. At the end the authors will present their approach to “trust-based recommendation systems”.FindingsThe purpose of this study is to understand how groups of users could improve trust in a recommendation system. The authors will examine how to evaluate the performance of recommender systems to ensure their ability to meet the needs that led to its creation and to make the system sustainable with respect to the information. The authors know very well that selecting a measure must depend on the type of data to be processed and user interests. Since the recommendation domain is derived from information search paradigms, it is obvious to use the evaluation measures of information systems.Originality/valueThe authors presented a list of recommendations systems. They examined and compared several recommendation approaches. The authors then analyzed the dominance of collaborative filtering in the field and the emergence of Recommender Systems in social web. Then the authors presented and analyzed different trust algorithms. Finally, their proposal was to measure the impact of trust in recommendation systems.


Author(s):  
A.Y. Zhubatkhan ◽  
Z.A. Buribayev ◽  
S.S. Aubakirov ◽  
M.D. Dilmagambetova ◽  
S.A. Ryskulbek

The trend of the Internet makes the presentation of the right content for the right user inevitable. To this end, recommendation systems are used in areas such as music, books, movies, travel planning, e-commerce, education, and more. One of the most popular recommendation systems in the world is Netflix, which generated record profits during quarantine in the first quartile of 2020. The systematic approach of recommendations is based on the history of user selections, likes and reviews, each of which is interpreted to predict future user selections. This article provides a meaningful analysis of various recommendation systems, such as content-based, collaborative filtering and popularity. We reviewed 7 articles published from 2005 to 2019 to discuss issues related to existing models. The purpose of this article is to compare machine learning algorithms in the Surprise library for a recommendation system. Recommendation system has been implemented and quality has been evaluated using the MAE and RMSE metrics.


2020 ◽  
Vol 17 (6) ◽  
pp. 2605-2612
Author(s):  
Dharminder Yadav ◽  
Himani Maheshwari ◽  
Umesh Chandra

Recommendation Systems (RS) suggest the right item to the right user. It predicts the user’s rating to an item and based on this rating RS provides the suggestion to users. In today’s world many online applications are already using the Recommendation system that provides a recommendation for a particular item like books, movies, music etc. in an automated fashion. This paper proposed a system that helps to find the best suitable hotel in a given geographical area according to the user query by using library “recommenderlab” in R. This study proposed a system that gives the best hotel available according to the user rating available in database. User makes their decision according to their recommendation provides by the proposed system for finding best suitable hotel from available database and shows on the map by using a leaflet map package.


Author(s):  
A.Y. Zhubatkhan ◽  
Z.A. Buribayev ◽  
S.S. Aubakirov ◽  
M.D. Dilmagambetova ◽  
S.A. Ryskulbek

The trend of the Internet makes the presentation of the right content for the right user inevitable. To this end, recommendation systems are used in areas such as music, books, movies, travel planning, e-commerce, education, and more. One of the most popular recommendation systems in the world is Netflix, which generated record profits during quarantine in the first quartile of 2020. The systematic approach of recommendations is based on the history of user selections, likes and reviews, each of which is interpreted to predict future user selections. This article provides a meaningful analysis of various recommendation systems, such as content-based, collaborative filtering and popularity. We reviewed 7 articles published from 2005 to 2019 to discuss issues related to existing models. The purpose of this article is to compare machine learning algorithms in the Surprise library for a recommendation system. Recommendation system has been implemented and quality has been evaluated using the MAE and RMSE metrics.


In this article, the data mining algorithms like apriori algorithm is used to suggest the best cast and crew to make a particular genre of movies so that the movie is successful. The data of cast and crew is extracted for which the users have given high average ratings and apply apriori algorithm to give recommendation. There are recommendation systems which gives the recommendation to the users so that the users can watch movies in which they are interested in. But there are no recommendation systems which gives the right information which is helpful for movie making. It is important to make good movies so that the viewers and entertained by it due to which the profit of the producers increases. This research work can be used by the movie makers to select the best cast and crew for the particular genre of movie.


Author(s):  
Htay Htay Win ◽  
Aye Thida Myint ◽  
Mi Cho Cho

For years, achievements and discoveries made by researcher are made aware through research papers published in appropriate journals or conferences. Many a time, established s researcher and mainly new user are caught up in the predicament of choosing an appropriate conference to get their work all the time. Every scienti?c conference and journal is inclined towards a particular ?eld of research and there is a extensive group of them for any particular ?eld. Choosing an appropriate venue is needed as it helps in reaching out to the right listener and also to further one’s chance of getting their paper published. In this work, we address the problem of recommending appropriate conferences to the authors to increase their chances of receipt. We present three di?erent approaches for the same involving the use of social network of the authors and the content of the paper in the settings of dimensionality reduction and topic modelling. In all these approaches, we apply Correspondence Analysis (CA) to obtain appropriate relationships between the entities in question, such as conferences and papers. Our models show hopeful results when compared with existing methods such as content-based ?ltering, collaborative ?ltering and hybrid ?ltering.


Informatics ◽  
2021 ◽  
Vol 8 (3) ◽  
pp. 49
Author(s):  
Samit Chakraborty ◽  
Md. Saiful Hoque ◽  
Naimur Rahman Jeem ◽  
Manik Chandra Biswas ◽  
Deepayan Bardhan ◽  
...  

In recent years, the textile and fashion industries have witnessed an enormous amount of growth in fast fashion. On e-commerce platforms, where numerous choices are available, an efficient recommendation system is required to sort, order, and efficiently convey relevant product content or information to users. Image-based fashion recommendation systems (FRSs) have attracted a huge amount of attention from fast fashion retailers as they provide a personalized shopping experience to consumers. With the technological advancements, this branch of artificial intelligence exhibits a tremendous amount of potential in image processing, parsing, classification, and segmentation. Despite its huge potential, the number of academic articles on this topic is limited. The available studies do not provide a rigorous review of fashion recommendation systems and the corresponding filtering techniques. To the best of the authors’ knowledge, this is the first scholarly article to review the state-of-the-art fashion recommendation systems and the corresponding filtering techniques. In addition, this review also explores various potential models that could be implemented to develop fashion recommendation systems in the future. This paper will help researchers, academics, and practitioners who are interested in machine learning, computer vision, and fashion retailing to understand the characteristics of the different fashion recommendation systems.


2013 ◽  
Vol 718-720 ◽  
pp. 1961-1966
Author(s):  
Hong Sheng Xu ◽  
Qing Tan

Electronic commerce recommendation system can effectively retain user, prevent users from erosion, and improve e-commerce system sales. BP neural network using iterative operation, solving the weights of the neural network and close values to corresponding network process of learning and memory, to join the hidden layer nodes of the optimization problem of adjustable parameters increase. Ontology learning is the use of machine learning and statistical techniques, with automatic or semi-automatic way, from the existing data resources and obtaining desired body. The paper presents building electronic commerce recommendation system based on ontology learning and BP neural network. Experimental results show that the proposed algorithm has high efficiency.


Author(s):  
Lakshmikanth Paleti ◽  
P. Radha Krishna ◽  
J.V.R. Murthy

Recommendation systems provide reliable and relevant recommendations to users and also enable users’ trust on the website. This is achieved by the opinions derived from reviews, feedbacks and preferences provided by the users when the product is purchased or viewed through social networks. This integrates interactions of social networks with recommendation systems which results in the behavior of users and user’s friends. The techniques used so far for recommendation systems are traditional, based on collaborative filtering and content based filtering. This paper provides a novel approach called User-Opinion-Rating (UOR) for building recommendation systems by taking user generated opinions over social networks as a dimension. Two tripartite graphs namely User-Item-Rating and User-Item-Opinion are constructed based on users’ opinion on items along with their ratings. Proposed approach quantifies the opinions of users and results obtained reveal the feasibility.


2021 ◽  
Vol 13 (2) ◽  
pp. 47-53
Author(s):  
M. Abubakar ◽  
K. Umar

Product recommendation systems are information filtering systems that uses ratings and predictions to make new product suggestions. There are many product recommendation system techniques in existence, these include collaborative filtering, content based filtering, knowledge based filtering, utility based filtering and demographic based filtering. Collaborative filtering techniques is known to be the most popular product recommendation system technique. It utilizes user’s previous product ratings to make new product suggestions. However collaborative filtering have some weaknesses, which include cold start, grey sheep issue, synonyms issue. However the major weakness of collaborative filtering approaches is cold user problem. Cold user problem is the failure of product recommendation systems to make product suggestions for new users. Literature investigation had shown that cold user problem could be effectively addressed using active learning technique of administering personalized questionnaire. Unfortunately, the result of personalized questionnaire technique could contain some user preference uncertainties where the product database is too large (as in Amazon). This research work addresses the weakness of personalized questionnaire technique by applying uncertainty reduction strategy to improve the result obtained from administering personalized questionnaire. In our experimental design we perform four different experiments; Personalized questionnaire approach of solving user based coldstart was implemented using Movielens dataset of 1M size, Personalized questionnaire approach of solving user based cold start was implemented using Movielens dataset of 10M size, Personalized questionnaire with uncertainty reduction was implemented using Movielens dataset of 1M size, and also Personalized  questionnaire with uncertainty reduction was implemented using Movielens dataset of 10M size. The experimental result shows RMSE, Precision and Recall improvement of 0.21, 0.17 and 0.18 respectively in 1M dataset and 0.17, 0.14 and 0.20 in 10M dataset respectively over personalized questionnaire.


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