An Approach Towards Hotel Recommender System

2020 ◽  
Vol 17 (6) ◽  
pp. 2605-2612
Author(s):  
Dharminder Yadav ◽  
Himani Maheshwari ◽  
Umesh Chandra
Keyword(s):  
Recommender System ◽  
Recommendation System ◽  
Geographical Area ◽  
Recommendation Systems ◽  
User Query ◽  
The Right

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.

scholarly journals Movie Recommenadation System

2021 ◽  
Vol 9 (4) ◽  
pp. 13-16
Author(s):  
Gandhali Malve ◽  
Lajree Lohar ◽  
Tanay Malviya ◽  
Shirish Sabnis
Keyword(s):  
Machine Learning ◽  
Recommender System ◽  
Recommendation System ◽  
Learning Algorithms ◽  
Recommendation Systems ◽  
Machine Learning Algorithms ◽  
The Internet ◽  
Amount Of Information ◽  
System Recommendation ◽  
User Ratings

Today the amount of information in the internet growth very rapidly and people need some instruments to find and access appropriate information. One of such tools is called recommendation system. Recommendation systems help to navigate quickly and receive necessary information. Many of us find it difficult to decide which movie to watch and so we decided to make a recommender system for us to better judge which movie we are more likely to love. In this project we are going to use Machine Learning Algorithms to recommend movies to users based on genres and user ratings. Recommendation system attempt to predict the preference or rating that a user would give to an item.

scholarly journals Recommendation System

2021 ◽  
Vol 6 (12) ◽  
pp. 484-492
Author(s):  
Dr. ML Sharma C Vinay Kumar Saini and Jai Raj Singh
Keyword(s):  
Recommender System ◽  
Recommendation System ◽  
Research Paper ◽  
Research Field ◽  
Collaborative Approach ◽  
Research Papers ◽  
Area Of Interest ◽  
Overall Performance ◽  
The Right ◽  
Filtering Approach

Research paper recommenders emerged over the last decade to ease finding publications relating to researchers’ area of interest. The challenge was not just to provide researchers with very rich publications at any time, any place and in any form but to also offer the right publication to the right researcher in the right way. Several approaches exist in handling paper recommender systems. However, these approaches assumed the availability of the whole contents of the recommending papers to be freely accessible, which is not always true due to factors such as copyright restrictions. This paper presents a collaborative approach for research paper recommender system. By leveraging the advantages of collab- orative filtering approach, we utilize the publicly available contextual metadata to infer the hidden associations that exist between research papers in order to personalize recommen- dations. The novelty of our proposed approach is that it provides personalized recommen- dations regardless of the research field and regardless of the user’s expertise. Using a publicly available dataset, our proposed approach has recorded a significant improvement over other baseline methods in measuring both the overall performance and the ability to return relevant and useful publications at the top of the recommendation list.

scholarly journals A Hypothesis is Placed to Justify the Extendibility of Recommender System/ Recommendation System into Social Life

2020 ◽  
Vol 10 (5) ◽  
pp. 37-39
Author(s):  
Shawni Dutta ◽  
Prof. Samir Kumar Bandyopadhyay
Keyword(s):  
Social Science ◽  
Recommender System ◽  
Medical Diagnosis ◽  
Social Life ◽  
Recommendation System ◽  
Information Filtering ◽  
Medical Science ◽  
Recommendation Systems ◽  
Basic Task ◽  
System Recommendation

Researchers still believe that the information filtering system/ collaborating system is a recommender system or a recommendation system. It is used to predict the "rating" or "preference" of a user to an item.  In other words, both predict rating or preference for an item or product on a specific platform. The aim of the paper is to extend the areas of the recommender system/recommendation systems. The basic task of the recommender system mainly is to predict or analyze items/product. If it is possible to include more products in the system, then obviously the system may be extended for other areas also. For example, Medicine is a product and doctors filter the particular medicine for the particular disease. In the medical diagnosis doctors prescribed a medicine and it a product. It depends on the disease of the user/patient so here doctor predicts a medicine or product just like an item is recommended in a recommender system. The main objective of the paper is to extend the Recommender System/Recommendation system in other fields so that the research works can be extended Social Science, Bio-medical Science and many other areas.

scholarly journals COMPARISON MODELS OF MACHINE LEARNING FOR MOVIE RECOMMENDATION SYSTEMS

2021 ◽  
Vol 335 (1) ◽  
pp. 26-31
Author(s):  
A.Y. Zhubatkhan ◽  
Z.A. Buribayev ◽  
S.S. Aubakirov ◽  
M.D. Dilmagambetova ◽  
S.A. Ryskulbek
Keyword(s):  
Machine Learning ◽  
Recommendation System ◽  
Systematic Approach ◽  
Recommendation Systems ◽  
Machine Learning Algorithms ◽  
Travel Planning ◽  
System Recommendation ◽  
History Of ◽  
Movie Recommendation ◽  
The Right

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.

scholarly journals Building A Movie Recommendation System Using Collaborative Filtering With TF-IDF

2021 ◽  
Vol 9 (9) ◽  
pp. 181-191
Author(s):  
S. A. Azeem Farhan
Keyword(s):  
Collaborative Filtering ◽  
Language Processing ◽  
Recommender System ◽  
Recommendation System ◽  
Mean Squared Error ◽  
Information Filtering ◽  
Recommendation Systems ◽  
Absolute Error ◽  
Machine Learning Algorithms ◽  
Value Decomposition

Abstract: The recommendation problem involves the prediction of a set of items that maximize the utility for users. As a solution to this problem, a recommender system is an information filtering system that seeks to predict the rating given by a user to an item. There are theree types of recommendation systesms namely Content based, Collaborative based and the Hybrid based Recommendation systems. The collaborative filtering is further classified into the user based collaborative filtering and item based collaborative filtering. The collaborative filtering (CF) based recommendation systems are capable of grasping the interaction or correlation of users and items under consideration. We have explored most of the existing collaborative filteringbased research on a popular TMDB movie dataset. We found out that some key features were being ignored by most of the previous researches. Our work has given significant importance to 'movie overviews' available in the dataset. We experimented with typical statistical methods like TF-IDF , By using tf-idf the dimensions of our courps(overview and other text features) explodes, which creates problems ,we have tackled those problems using a dimensionality reduction technique named Singular Value Decomposition(SVD). After this preprocessing the Preprocessed data is being used in building the models. We have evaluated the performance of different machine learning algorithms like Random Forest and deep neural networks based BiLSTM. The experiment results provide a reliable model in terms of MAE(mean absolute error) ,RMSE(Root mean squared error) and the Bi-LSTM turns out to be a better model with an MAE of 0.65 and RMSE of 1.04 ,it generates more personalized movie recommendations compared to other models. Keywords: Recommender system, item-based collaborative filtering, Natural Language Processing, Deep learning.

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

2021 ◽  
pp. 73-80
Author(s):  
Mariia Kozulia ◽  
Vladyslava Sushko
Keyword(s):  
Web Application ◽  
Recommendation System ◽  
High Efficiency ◽  
Information Source ◽  
Recommendation Systems ◽  
Content Filtering ◽  
Personal Recommendation ◽  
The Right ◽  
Use Of Internet ◽  
Scientific Interaction

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.

scholarly journals Recommendation System: Techniques, Evaluation and Limitations

2019 ◽  
Vol 8 (6) ◽  
pp. 2097-2101
Keyword(s):  
Information Retrieval ◽  
Recommender System ◽  
Recommendation System ◽  
Information Overload ◽  
Recommendation Systems ◽  
Research Papers

For the benefits of the user in selecting items based on their interests, the recommendation technology is developed in different domains. A recommender system is one of the major techniques, that handles information overload problem of Information Retrieval by suggesting users with appropriate and relevant items. This paper surveys recommendation technology, the challenges and its solutions. Recommendation technology is applied in many areas like movies, videos, books, research papers, libraries, music, news, tourism, etc. This survey is useful for the further implementation and analysis of how users are adapting these technologies and how helpful it is for the user. This work also helps understand the different techniques of recommendation systems and how they can be evaluated.

scholarly journals Neural Collaborative with Sentence BERT for News Recommender System

2021 ◽  
Vol 5 (4) ◽  
pp. 448
Author(s):  
Budi Juarto ◽  
Abba Suganda Girsang
Keyword(s):  
Neural Networks ◽  
Collaborative Filtering ◽  
Recommender System ◽  
Recommendation System ◽  
Recommendation Systems ◽  
Roc Curves ◽  
Leave One Out ◽  
Evaluation Measurement ◽  
Better Than ◽  
Roc Score

The number of news produced every day is as much as 3 million per day, making readers have many choices in choosing news according to each reader's topic and category preferences. The recommendation system can make it easier for users to choose the news to read. The method that can be used in providing recommendations from the same user is collaborative filtering. Neural collaborative filtering is usually being used for recommendation systems by combining collaborative filtering with neural networks. However, this method has the disadvantage of recommending the similarity of news content such as news titles and content to users. This research wants to develop neural collaborative filtering using sentences BERT. Sentence BERT is applied to news titles and news contents that are converted into sentence embedding. The results of this sentence embedding are used in neural collaboration with item id, user id, and news category. We use a Microsoft news dataset of 50,000 users and 51,282 news, with 5,475,542 interactions between users and news. The evaluation carried out in this study uses precision, recall, and ROC curves to predict news clicks by the user. Another evaluation uses a hit ratio with the leave one out method. The evaluation results obtained a precision value of 99.14%, recall of 92.48%, f1-score of 95.69%, and ROC score of 98%. Evaluation measurement using the hit ratio@10 produces a hit ratio of 74% at fiftieth epochs for neural collaborative with sentence BERT which is better than neural collaborative filtering (NCF) and NCF with news category.

scholarly journals COMPARISON MODELS OF MACHINE LEARNING FOR MOVIE RECOMMENDATION SYSTEMS

2021 ◽  
Vol 335 (1) ◽  
pp. 26-31
Author(s):  
A.Y. Zhubatkhan ◽  
Z.A. Buribayev ◽  
S.S. Aubakirov ◽  
M.D. Dilmagambetova ◽  
S.A. Ryskulbek
Keyword(s):  
Machine Learning ◽  
Recommendation System ◽  
Systematic Approach ◽  
Recommendation Systems ◽  
Machine Learning Algorithms ◽  
Travel Planning ◽  
System Recommendation ◽  
History Of ◽  
Movie Recommendation ◽  
The Right

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.

scholarly journals Recommendation System for Movie Cast and Crew using Datamining Algorithm

2020 ◽  
Vol 9 (4) ◽  
pp. 1495-1497
Keyword(s):  
Data Mining ◽  
Recommendation System ◽  
Research Work ◽  
Recommendation Systems ◽  
Apriori Algorithm ◽  
Data Mining Algorithms ◽  
The Right ◽  
Mining Algorithms

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.

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