Content-based Filtering Techniques Research Articles

Implementation and Evaluation of a Hybrid Recommendation System for the Real Estate Market

Introduction: The real estate market has been transformed by digital technologies, especially Industry 4.0, which has made property searching and evaluation more efficient, improving its accuracy with the use of advanced algorithms. Traditional methods have been replaced by online platforms using modern machine learning (ML) algorithms, leading to the need for personalized recommendation systems to improve user experiences. Methodology: This study designed and implemented a hybrid recommendation system that combines collaborative and content-based filtering techniques. The development process involved four phases: literature review, technology selection, prototype implementation, and system deployment. Findings: The proposed hybrid model effectively addressed challenges such as data sparsity and the cold start problem, improving recommendation accuracy. In the evaluation, users indicated high satisfaction with the system’s ability to offer personalized property recommendations. Conclusion: Thus, hybrid recommendation systems can significantly improve the property search experience by offering personalized recommendations. However, further research into the applicability of the system in different domains remains a need for further exploration.

Movie Recommendation System using Machine Learning

There is already enough content available on the movie recommendation system. Showing the movie recommendations is essential so that the user need not waste a lot of time searching for the content which he/she might like. Thus, movie recommendation system plays a vital role to get user personalized movie recommendations. After searching a lot on the internet and referring to a lot of research papers, we got to know that the recommendations made using Content-based Filtering are using a single text to vector conversion technique and a single technique to find the similarity between the vectors. In this research work, we have used multiple text to vector conversion techniques and manipulated the results of the multiple algorithms to get the final recommendation list. You can think of it as a hybrid approach using the Content-based Filtering technique only.

Movie Recommendation System Using Optimized RNN Approach.

This paper proposes a movie recommendation system that utilizes an optimized Recurrent Neural Network (RNN) approach. The proposed system is designed to provide users with personalized movie recommendations based on their previous movie preferences and the sentiments. The system works by taking user input, analyzing their movie preferences using content-based filtering techniques, and generating a list of recommended movies. The RNN architecture used in this system is optimized using a combination of techniques such as dropout regularization, early stopping, and parameter tuning. The proposed optimization techniques aim to reduce overfitting, improve convergence speed, and increase the model's overall accuracy. To evaluate the effectiveness of the proposed approach, we conducted experiments on the Movie Lens dataset. The results indicate that the optimized RNN-based movie recommendation system outperforms other existing recommendation systems such as collaborative filtering, content-based filtering, and standard RNN models. Furthermore, the proposed system achieved a significant improvement in accuracy and provided highly personalized recommendations to users. Overall, the proposed movie recommendation system using optimized RNN approach is a promising solution for providing personalized movie recommendations to users. It can be implemented in various platforms such as movie streaming websites, social media, and other movie-related platforms to improve the user experience and increase engagement

Blockchain Based Placement Management System And Job Recommendation Using Content-Based Filtering

The integration of blockchain technology with placement management systems and job recommendation mechanisms presents a robust solution for enhancing the efficiency, transparency, and security of the recruitment process. This paper proposes a Blockchain-based Placement Management System (BPMS) coupled with Content-Based Filtering (CBF) techniques to streamline the job matching process for both job seekers and recruiters. The BPMS leverages the decentralized nature of blockchain to ensure immutable records of job listings, candidate profiles, and transactional data, thereby reducing the risk of fraudulent activities and ensuring data integrity. Smart contracts are utilized to automate various aspects of the recruitment process, such as job application submissions, candidate evaluations, and offer acceptance, facilitating trustless interactions between parties.In parallel, the CBF approach is employed to provide personalized job recommendations to candidates based on the analysis of their skills, qualifications, and preferences. By analyzing the content of job descriptions and candidate profiles, the system generates tailored recommendations that match the expertise and interests of the candidates, improving the likelihood of successful placements.The proposed system offers several advantages over traditional placement management systems, including increased transparency, reduced operational costs, minimized bias in job recommendations, and enhanced security of sensitive data. Through the synergy of blockchain technology and content-based filtering techniques, the BPMS aims to revolutionize the recruitment industry by fostering trust, efficiency, and fairness in the job market.

Tourist Guide AI

Abstract—The research paper presents an innovative AI-driven platform that revolutionizes the landscape of personalized travel recommendations within specific cities. Leveraging the power of Artificial Intelligence (AI), the "Tourist Guide AI" system offers curated suggestions for attractions, activities, and dining options based on individual preferences. The platform integrates machine learning algorithms, geospatial data, and natural language processing to provide comprehensive insights into city attractions, ensuring a personalized and enriching travel experience. The deployment phase involves launching the system on cloud servers, ensuring widespread accessibility and scalability to accommodate varying user demands. The methodology employed encompasses rigorous data collection, preprocessing, and the implementation of a recommendation algorithm that combines collaborative and contentbased filtering techniques. The user interface is designed with intuitiveness and user- friendliness at its core, ensuring an intuitive experience for travelers of all backgrounds. The platform's advantages include personalized recommendations, efficient trip planning, real-time updates, and potential integration with mobile devices for on-the-go guidance. The research paper also acknowledges potential disadvantages and limitations, such as data accuracy, privacy concerns, and limited coverage for remote or lessvisited destinations, emphasizing the commitment to continuous improvement and expansion of coverage. The "Tourist Guide AI" project represents a significant step towards simplifying and enriching the tourist planning process, ensuring that each trip is a unique and tailored adventure for travelers of all backgrounds and preferences. Keywords—Tourist Guide AI, places recommendation, OpenAI APIs, Tourism form AI,Content Based Filtering, Vectorization .

Measuring Tourist Experience in Semarang City through an Advanced Recommendation System

In the tourism sector which includes recreation and holiday activities, the Indonesian tourism sector has a very important role because of its impact on the country's foreign exchange reserves. Indonesia, with its diverse attractions ranging from nature, culture, religion, family activities, shopping and gastronomy, presents many choices for tourists. The city of Semarang is actively increasing its tourism offerings, but the sheer number of choices can overwhelm tourists. This research presents an advanced recommendation system based on collaborative filtering and content-based filtering techniques. By leveraging historical travel data, including attraction visits, ratings, and frequently visited categories, the system provides tailored suggestions. Content-based filtering prioritizes tourist attractions such as Chinatown Semarang, Kampoeng Djadhoel Semarang, Kapal Mosque Semarang, Tugu Muda Semarang, and Tinjomoyo Forest Tourism Semarang based on ratings. Collaborative filtering resulted in recommendations such as Gua Maria Kerep Ambarawa (rating: 4.8), La Kana Chapel (rating: 4.5), Palagan Ambarawa Monument (rating: 4.4), Eling Bening Tourism (rating: 4.3), and Kampoeng Kopi Banaran (rating: 4.3).In a world where choices are many and time is limited, this advanced recommendation system simplifies travel decisions, elevating ordinary trips into extraordinary adventures. This heralds the future of tourism, where technology aligns with exploration to uncover Semarang's hidden treasures.” of 4.3.

Cinephiles Integration System using ML

Abstract: Our project aims to create a movie recommendation and community platform where users can discover and share their favorite movies with others. The platform will utilize a recommendation system to suggest personalized movie recommendations based on the user's preferences and viewing history. Users will also be able to rate and review movies, create watchlists, and follow other users with similar movie tastes. The community aspect of the platform will allow users to engage with others through forums, discussions, and private messaging. This will create a space for movie enthusiasts to connect and share their thoughts on the latest releases, hidden gems, and all-time favorites. To develop the recommendation system, we will use collaborative filtering and content-based filtering techniques. The platform will also utilize machine learning algorithms to analyze user behavior and provide more accurate recommendations over time. Overall, our movie recommendation and community platform will provide a comprehensive and interactive movie-watching experience for users while promoting a sense of community among movie enthusiasts.

A unified approach to designing sequence-based personalized food recommendation systems: tackling dynamic user behaviors

The recommender system (RS) is a well-known practical application of the state-of-the-art information filtering and machine learning technologies. Traditional recommendation approaches, including collaborative and content-based filtering techniques, have been widely employed to provide suggestions in RSs, where the user-item interaction matrix is the primary data source. In many application domains, interactions between users and items are more likely to be dynamic rather than static, and thus dynamic user behaviors should be taken into account when solving recommendation tasks in order to provide more accurate suggestions. In this work, we consider the sequentially ordered information from user-item interactions in the RSs where a sequence-based recommendation model is put forward with applications to the food recommendation scenario. Furthermore, the long short-term memory (LSTM) network is employed as the building block to establish such a recommendation model, and a collaborative filtering unit is adopted to make personalized food recommendation. The proposed LSTM-based RS is successfully applied to a real-world food recommendation data set. Experimental results demonstrate that the developed method outperforms some currently popular RSs in terms of precision, recall, mean average precision and mean reciprocal rank in food recommendation.

A Comparative Analysis of Collaborative Filtering Similarity Measurements for Recommendation Systems

Collaborative Filtering (CF) is a widely used technique in recommendation systems to suggest items to users based on their previous interactions with the system. CF involves finding correlations between the preferences of different users and using those correlations to provide recommendations. This technique can be divided into user-based and item-based CF, both of which utilize similarity metrics to generate recommendations. Content-based filtering is another commonly used recommendation technique that analyzes the attributes of items to suggest similar items. To enhance the accuracy of recommendation systems, hybrid algorithms that combine CF and content-based filtering techniques have been developed. These hybrid systems leverage the strengths of both approaches to provide more accurate and personalized recommendations. In conclusion, collaborative filtering is an essential technique in recommendation systems, and the use of various similarity metrics and hybrid techniques can enhance the quality of recommendations.

Movie Recommendation Using Hybrid-Based Approach

The field of recommendation systems has been rapidly growing due to the increasing amount of data available on the internet. Movie recommendation systems are one of the most widely used applications of recommendation systems. In this paper, we propose a hybrid-based approach to movie recommendation systems. The proposed approach combines content-based filtering and collaborative filtering techniques to provide better recommendations. The content-based filtering technique uses movie features such as genre, director, actors, and plot to recommend similar movies to the users. The collaborative filtering technique uses the user's past behaviour and other users' behaviour to recommend movies to the user. We evaluated the proposed hybrid approach on the Cosine Similarity and SVD dataset to achieve better results compared to the individual content-based and collaborative filtering techniques.

Content-Based Movie Recommendation System Using MBO with DBN

The content-based filtering technique has been used effectively in a variety of Recommender Systems (RS). The user explicitly or implicitly provides data in the Content-Based Recommender System. The system collects this data and creates a profile for all the users, and the recommendation is generated by the user profile. The recommendation generated via content-based filtering is provided by observing just a single user’s profile. The primary objective of this RS is to recommend a list of movies based on the user’s preferences. A content-based movie recommendation model is proposed in this research, which recommends movies based on the user’s profile from the Facebook platform. The recommendation system is built with a hybrid model that combines the Monarch Butterfly Optimization (MBO) with the Deep Belief Network (DBN). For feature selection, the MBO is utilized, while DBN is used for classification. The datasets used in the experiment are collected from Facebook and MovieLens. The dataset features are evaluated for performance evaluation to validate if data with various attributes can solve the matching recommendations. Each file is compared with features that prove the features will support movie recommendations. The proposed model’s mean absolute error (MAE) and root-mean-square error (RMSE) values are 0.716 and 0.915, and its precision and recall are 97.35 and 96.60 percent, respectively. Extensive tests have demonstrated the advantages of the proposed method in terms of MAE, RMSE, Precision, and Recall compared to state-of-the-art algorithms such as Fuzzy C-means with Bat algorithm (FCM-BAT), Collaborative filtering with k-NN and the normalized discounted cumulative gain method (CF-kNN+NDCG), User profile correlation‑based similarity (UPCSim), and Deep Autoencoder.

Sistem Rekomendasi Content-based Filtering Menggunakan TF-IDF Vector Similarity Untuk Rekomendasi Artikel Berita

The population of active students in the Informatics Bachelor Program, Universitas Amikom Yogyakarta, in the odd semester of 2021 is 3,870. Efforts to track interest in the three concentration options were carried out early on through article literacy recommendations. Various articles are produced continuously and provided on an ongoing basis to students. However, the many articles offered daily make students overwhelmed and tend to choose articles that do not match what they want. To help solve this problem, recommender system is developed. A recommender system helps to estimate the prediction value or relevancy of an article and create a ranking according to user's taste. Content-based Filtering technique is used in this research. Using the dataset from Kabar Informatika news portal of University of Amikom Yogyakarta, the developed Content-based Filtering Recommendation System is able to produce Recall@5 score at around 73% and Recall@10 at around 80%.

Provenance and social network analysis for recommender systems: a literature review

<span>Recommender systems (RS) and their scientific approach have become very important because they help scientists find suitable publications and approaches, customers find adequate items, tourists find their preferred points of interest, and many more recommendations on domains. This work will present a literature review of approaches and the influence that social network analysis (SNA) and data provenance has on RS. The aim is to analyze differences and similarities using several dimensions, public datasets for assessing their impacts and limitations, evaluations of methods and metrics along with their challenges by identifying the most efficient approaches, the most appropriate assessment data sets, and the most appropriate assessment methods and metrics. Hence, by correlating these three fields, the system will be able to improve the recommendation of certain items, by being able to choose the recommendations that are made from the most trusted nodes/resources within a social network. We have found that content-based filtering techniques, combined with term frequency-inverse document frequency (TF-IDF) features are the most feasible approaches when combined with provenance since our focus is to recommend the most trusted items, where trust, distrust, and ignorance are calculated as weight in terms of the relationship between nodes on a network.</span>

A Comprehensive Survey Using Novel Content-Based Filtering Technique to Improve Effectiveness on Social Media Datasets for Group Recommendation System Based on External Social Trust Networks

Aim:To build a Movie Recommendation system for Netflix to improve the accuracy percentage using Content-Based Filtering technique to analyze the prediction rate of movies. Materials and Methods: Two different algorithms are used Content-Based filtering algorithm and External Social Trust Networks to achieve maximum accuracy with a content based filtering with a sample size = 6 and external social trust network with a sample size= 6 was iterated 10 times for accurate outcome. Pre test is calculated by using G-Power tool value of 80% and confidence interval is 95% mean and standard deviation. Result and Discussions: These results proved that Recommendation System for Netflix using Novel Content-Based filtering algorithm gives better accuracy (0.94) in comparison with External Social Trust Networks(0.76). Conclusion: Content-Based Filtering algorithm helps to build a recommendation system gives effective results with the better accuracy rate of percentage.

Recommended System

In this study searched for the survey paper on recommendation system. Recommendation system sorts through massive amounts of data to identify interest of users and makes the information search easier. For that purpose many methods have been used. Collaborative Filtering (CF) is a method of making automatic predictions about the interests of customers by collecting information from number of other customers, for that purpose many collaborative base algorithms are used. Web Recommendation(WR) help the website visitors for easy navigation of web pages, quickly reaching their destination and to obtain relevant information. Content based filtering method(CBF) filtering is done based on customer’s interested items. In content-based filtering technique, the web pages are recommended for a user very quickly from ancient database. In that database different content of items are added that the user has used in the ancient times and/or user’s personal information and preferences.

A Comparing Collaborative Filtering and Hybrid Recommender System for E-Commerce

Abstract: Here we are building an collaborative filtering matrix factorization based hybrid recommender system to recommend movies to users based on the sentiment generated from twitter tweets and other vectors generated by the user in their previous activities. To calculate sentiment data has been collected from twitter using developer APIs and scrapping techniques later these are cleaned, stemming, lemetized and generated sentiment values. These values are merged with the movie data taken and create the main data frame.The traditional approaches like collaborative filtering and content-based filtering have limitations like it requires previous user activities for performing recommendations. To reduce this dependency hybrid is used which combines both collaborative and content based filtering techniques with the sentiment generated above. Keywords: machine learning, natural language processing, movie lens data, root mean square equation, matrix factorization, recommenders system, sentiment analysis

An ontology-based hybrid e-learning content recommender system for alleviating the cold-start problem

An e-learning recommender system (RS) aims to generate personalized recommendations based on learner preferences and goals. The existing RSs in the e-learning domain still exhibit drawbacks due to its inability to consider the learner characteristics in the recommendation process. In this paper, we are dealing with the new user cold-start problem, which is a major drawback in e-learning content RSs. This problem can be mitigated by incorporating additional learner data in the recommendation process. This paper proposes an ontology-based (OB) content recommender system for addressing the new user cold-start problem. In the proposed recommendation model, ontology is used to model the learner and learning objects with their characteristics. Collaborative and content-based filtering techniques are used in the recommendation model to generate the top N recommendations based on learner ratings. Experiments were conducted to evaluate the performance and prediction accuracy of the proposed model in cold-start conditions using the evaluation metrics mean absolute error, precision and recall. The proposed model provides more reliable and personalized recommendations by making use of ontological domain knowledge.

Content boosted hybrid filtering for solving pessimistic user problem in recommendation systems

The main goal of a recommendation system is to recommend items of interest to users by analyzing their historical data. Content-based and collaborative filtering are the traditional recommendation strategies, each with its own strengths and weaknesses. Some of their weaknesses can be overcome by combining the two strategies. The resulting hybrid system performs qualitatively better than the traditional recommendation systems. However, historical data of some users may consist largely of only likes or only dislikes. Those users are termed as optimistic or pessimistic users respectively. On an average there are around 10 to 20% of pessimistic users present in a given dataset. For pessimistic users, whose profiles have mostly dislikes and very few likes, content-based filtering can hardly recommend any items of interest. In content-based filtering technique pessimistic users get poor recommendations of either uninteresting movies or no recommendations at all. This can be alleviated by boosting the content profiles of pessimistic users using the top-n recommendations of collaborative filtering. This content boosted hybrid filtering system provides a novel list of recommendations even for pessimistic users, with predictive accuracy better than that of a traditional content-based filtering system.

A content-based recommender system for choosing universities

Recommender system (RS) is a knowledge discovery and decision-making system that has been extensively used in a myriad of applications to assist people in making distinct choices from vast sources. This paper proposes a recommendation system that will help the prospective students of Bangladesh in choosing the most suitable private universities for getting admission. Since selecting the best private university does not depend merely on a few criteria or choices and making a decision considering all those criteria is not an easy task, a recommendation system can be of great assistance in this scenario for the prospective students. In this proposed recommendation system a list of top-K private universities is recommended to the students who are willing to get admitted to the private universities using content-based filtering technique. To attain this goal we considered six parameters, namely grade point average (GPA) of secondary school certificate (SSC) examination, GPA of higher secondary certificate (HSC) examination, total GPA, tuition fees, university ratings, and university rankings. Finally, we evaluated the system with a total of 947 real feedback from prospective students and obtained the accuracies of 89.05%, 95.85%, 48%, 92.32%, and 71.93% using 5 different performance metrics: precision, recall, specificity, F1 score, and balanced accuracy, respectively.

Techniques of Recommender System

The term Recommender system is described as any organization that provides personalized suggestions as a result and it effects the user in the individualized way to favorable items from the large number of opinions. The voluminous inflation of the reachable data online and also the number of users have lead to the information overload problem. To overcome this problem the recommender system came into play as it is able to prioritize and personalize the data. Recommendation systems have developed alongside with the net. Recommender system has mainly three data filtering methods such as content based filtering technique, collaborative based filtering technique and the hybrid approach to manage the data overload problem and to recommends the items to the user the items they are interested in from the dynamically generated data. This paper makes a comprehensive introduction to the recommender system with its types, content based filtering , collaborative filtering and the hybrid recommendation.