Analisis dan Implementasi Recency-Based Collaborative Filtering pada Recommender sSystem Studi Kasus pada Data Movielens

ABSTRAKSI: Recommender system merupakan sebuah sistem yang dapat digunakan untuk memprediksi sebuah items berdasarkan informasi yang diperoleh dari user, sehingga didapatkan rekomendasi berdasarkan profil penggunanya. Collaborative filtering merupakan teknik yang umum digunakan dalam recommender system akan tetapi hanya sedikit yang membahas tentang concept drift. Recent rating dari user lebih mencerminkan preferensi yang akan datang dibandingkan data rating yang lama Tugas akhir ini menganalisis akurasi prediksi rating yang dihasilkan oleh recommender system setelah diimplementasikan algoritma recency-based collaborative filtering yang menggunakan pembobotan berdasarkan recent rating. Data yang digunakan adalah data set movielens. Parameter yang digunakan dalam analisis adalah parameter Alpha dan jumlah neighborhood. Selain itu, tugas akhir ini juga menganalisis kesesuaian hasil rekomendasi dengan genre dari items yang direkomendasikan. Akurasi prediksi yang dihasilkan oleh algoritma recency-based collaborative filtering lebih besar dibandingkan dengan classic collaborative filtering. Performansi terbaik terjadi saat jumlah neighborhood sama dengan jumlah kecenderungan user dalam merating items. Hasil rekomendasi pada algoritma recency-based collaborative filtering pada recommender system menunjukkan ketidaksesuaian antara genre items hasil rekomendasi dengan genre items yang telah diberi rating oleh active user.Kata Kunci : recommender system, collaborative filtering, concept drift, recentABSTRACT: Recommender System is a system that can be used to predict items-based on information obtained from users. Collaborative filtering is a common technique used in recommender system, but few of it are discussing about the concept drift. Recent rating reflect user preferences more than older rating. This final task analyzing the prediction ratings generated by the recommender system was implemented recency-based collaborative filtering using a weighting based on the recent rating. Data is used data from movielens. The parameter used in analysis is alpha and number of neighborhood. In addition, this final task is to analyze the suitability of recommendations to genre of the recommended items. Accuracy of prediction algorithms generated by the recency-based collaborative filtering larger than the classic collaborative filtering. The best performance occurs when the number of neighborhood equal with the number of trends in user rate the items. Result of recommendations with recency algorithmbased collaborative filtering in recommender systems showed a discrepancy between the recommended items genres with genres items that have been rated by the active user.Keyword: recommender system, collaborative filtering, concept drift, recen

Big Data Analytics in the Entertainment Industry: Audience Behavior Analysis, Content Recommendation, and Revenue Maximization

This research contributes to the understanding of the significant role of big data analytics in transforming the entertainment industry. In this study, we investigate the impact of big data analytics on the entertainment industry, focusing on three key aspects: audience behavior analysis, content recommendation, and revenue maximization. To understand audience behavior, entertainment companies leverage big data analytics to collect and analyze vast amounts of data from various sources, including social media platforms, streaming services, ticket sales, and website traffic. By analyzing viewer preferences, engagement metrics, and geographic information, companies gain valuable insights into audience behavior. These insights help in creating content that resonates with the target audience, optimizing future content creation, and tailoring marketing strategies based on geographical preferences. Furthermore, big data analytics plays a vital role in powering content recommendation systems. Through collaborative filtering and content-based filtering techniques, entertainment platforms personalize content recommendations based on user behavior, preferences, and historical data. This enhances user satisfaction and increases the likelihood of discovering relevant and appealing content. Hybrid approaches that combine collaborative and content-based filtering techniques are also explored to achieve more accurate and diverse recommendations. Moreover, big data analytics enables entertainment companies to optimize revenue generation strategies. By analyzing historical data, market trends, and consumer behavior, companies can implement dynamic pricing strategies, adjusting ticket prices, subscription fees, or content pricing based on demand and viewer preferences. Additionally, targeted advertising based on user data enhances advertising revenue by delivering personalized advertisements. Furthermore, analyzing market data and consumer behavior patterns helps optimize licensing agreements and content distribution strategies, maximizing revenue opportunities

Recommendation System for News Reader

Recommendation Systems help users to find information and make decisions where they lack the required knowledge to judge a particular product. Also, the information dataset available can be huge and recommendation systems help in filtering this data according to users‟ needs. Recommendation systems can be used in various different ways to facilitate its users with effective information sorting. For a person who loves reading, this paper presents the research and implementation of a Recommendation System for a NewsReader Application using Android Platform. The NewsReader Application proactively recommends news articles as per the reading habits of the user, recorded over a period of time and also recommends the currently trending articles. Recommendation systems and their implementations using various algorithms is the primary area of study for this project. This research paper compares and details popular recommendation algorithms viz. Content based recommendation systems, Collaborative recommendation systems etc. Moreover, it also presents a more efficient Hybrid approach that absorbs the best aspects from both the algorithms mentioned above, while trying to eliminate all the potential drawbacks observed

Toward a collective intelligence recommender system for education

The development of Information and Communication Technology (ICT), have revolutionized the world and have moved us into the information age, however the access and handling of this large amount of information is causing valuable time losses. Teachers in Higher Education especially use the Internet as a tool to consult materials and content for the development of the subjects. The internet has very broad services, and sometimes it is difficult for users to find the contents in an easy and fast way. This problem is increasing at the time, causing that students spend a lot of time in search information rather than in synthesis, analysis and construction of new knowledge. In this context, several questions have emerged: Is it possible to design learning activities that allow us to value the information search and to encourage collective participation?. What are the conditions that an ICT tool that supports a process of information search has to have to optimize the student's time and learning? This article presents the use and application of a Recommender System (RS) designed on paradigms of Collective Intelligence (CI). The RS designed encourages the collective learning and the authentic participation of the students. The research combines the literature study with the analysis of the ICT tools that have emerged in the field of the CI and RS. Also, Design-Based Research (DBR) was used to compile and summarize collective intelligence approaches and filtering techniques reported in the literature in Higher Education as well as to incrementally improving the tool. Several are the benefits that have been evidenced as a result of the exploratory study carried out. Among them the following stand out: • It improves student motivation, as it helps you discover new content of interest in an easy way. • It saves time in the search and classification of teaching material of interest. • It fosters specialized reading, inspires competence as a means of learning. • It gives the teacher the ability to generate reports of trends and behaviors of their students, real-time assessment of the quality of learning material. The authors consider that the use of ICT tools that combine the paradigms of the CI and RS presented in this work, are a tool that improves the construction of student knowledge and motivates their collective development in cyberspace, in addition, the model of Filltering Contents used supports the design of models and strategies of collective intelligence in Higher Education.Postprint (author's final draft