Implementasi Algoritma New Heuristic Similarity Model (NHSM) Pada Web Based Recommender System

Dalam website e-commerce banyak produk atau jasa yang ditawarkan kepada user dan cukup membuat user kebingungan untuk memilih produk atau jasa apa yang akan mereka gunakan. Tetapi seiring berkembangnya pengetahuan dan teknologi, maka ditemukan suatu cara untuk membantu user mempersempit information overloads ini, yaitu dengan menggunakan recommender system. Tujuan penelitian adalah mengimplementasikan algoritma New Heuristic Similarity Model (NHSM) pada web based recommender system berbasis memory based collaborative filtering dan mengukur keakuratan prediksi menggunakan Mean Absolute Error. Metode pengujian menggunakan empat jenis skenario yaitu skenario perhitungan prediction score, perhitungan similarity, pengujian sparse dataset dan dense dataset. Keempat skenario tersebut diuji dengan menggunakan tiga dataset yaitu MovieLens, Jester Joke dan Yahoo Movie. Hasil penelitian menunjukkan bahwa algoritma NHSM dapat diterapkan pada web based recommender system dan keakuratan prediksi semakin baik jika dataset terisi rating penuh (dense dataset) serta hasil similarity mendekati satu. Kata Kunci: Recommender System, New Heuristic Similarity Model (NHSM), Memory Based Collaborative Filtering, Mean Absolute Error. There are many products or services offered to users in the e-commerce website. Those create users\u27 confusion to choose what products or services they will use. Along with science and technology development, then found a way to help users to narrow down the information overloads by using a recommender system. The research objectives are to implement New Heuristic Similarity Model (NHSM) algorithm in web-based recommender system on memory-based collaborative filtering and measuring prediction accuracy using Mean Absolute Error. The testing method uses four scenarios: calculation of prediction score, calculation of similarity, sparse datasets testing and dense datasets testing. The fourth scenario was tested by using three datasets which are MovieLens, Jester Joke and Yahoo Movie. The results showed that NHSM algorithm can be applied to a web-based recommender system. Prediction accuracy will be better if datasets are filled with full rating (dense dataset) and its value of similarity approaching 1. Keywords: Recommender System, New Heuristic Similarity Model (NHSM), Memory Based Collaborative Filtering, Mean Absolute Error. DAFTAR PUSTAKA Adomavicius, G., & Tuzhilin, A. (2005). Toward the Next Generation of Recommender Systems: A Survey of the State-of-the-Art and Possible Extensions. IEEE Transactions on Knowledge and Data Engineering Vol.17, 734-749. Ahn, H. J. (2007). A Hybrid Collaborative Filtering Recommender System Using a New Similarity Measure. Proceedings of the 6th WSEAS International Conference on Applied Computer Science, 494-498. Bhunje, S. (2014, Mei 29). Retrieved Desember 3, 2014, from The Geek: http://theegeek.com/do-you-know-about-collaborative-filtering/ Cacheda, F., Carneiro, V., Fernandez, D., & Formoso, V. (2011). Comparison of Collaborative Filtering Algorithms: Limitations of Current Techniques and Proposals for Scalable, High- Performance Recommender Systems. ACM Transactions on the Web Vol.5. Dennis, A., Wixom, B. H., & Tegarden, D. (2010). Systems Analysis and Design with UML. New Jersey: Wiley. Ekstrand, M. D., Riedl, J. T., & Konstan, J. A. (2010). Collaborative Filtering Recommender System. The Essence of Knowledge: Human-Computer Interaction Vol.4, 81-173. Hafid, Z., Maharani, W., & Firdaus A., Y. (2010). Similarity Measure menggunakan Algoritma Weighted Difference Entropy (WDE) berbasis Memory-based Collaborative Filtering. Bandung: Telkom University. Lee, J., Sun, M., & Lebanon, G. (2012). A Comparative Study of Collaborative Filtering Algorithms. arXiv preprint arXiv:1205.3193. Liu, H., Hu, Z., Mian, A., Tian, H., & Zhu, X. (2014). A New User Similarity Model to Improve the Accuracy of Collaborative Filtering. Knowledge-Based System, 156-166. Melville, P., & Sindhwani, V. (2010). Recommender Systems. Encyclopedia of Machine Learning (pp. 829-838). Springer US. Navidi, W. (2011). Statistics for Engineers and Scientists. New York: McGraw-Hill. Nugroho, D. S. (2010). Analsis dan Implementasi Perbandingan Metode Cosine Similarity dan Correlation Based Similarity Pada Recommender System Berbasis Item-Based Collaborative Filtering. Bandung: Telkom University. Ricci, F., Rokach, L., Shapira, B., & Kantor, P. B. (2011). Recommender Systems Handbook. New York: Springer. Rodriguez, D. (2011). Recommender Systems. In J. Leskovec, A. Rajaraman, & J. D. Ullman, Mining of Massive Datasets. United Kingdom: Cambridge University Press. Sania, R., Maharani, W., & K, A. P. (2010). Analisis Perbandingan Metode Pearson dan Sperman Correlation pada Recommender System. Konferensi Nasional Sistem dan Informatika, 99-105. Shapira, B., & Rokach, L. (2010). Retrieved Desember 24, 2014, from Ben-Gurion University: medlib.tau.ac.il/teldan-2010/bracha.ppt Su, X., & Khoshgoftaar, T. M. (2009). A Survey of Collaborative Filtering Techniques. Hindawi Publishing Corporation: Advance in Artificial Intelligence. Sugiyono. (2010). Metode Penelitian Pendidikan. Bandung: ALFABETA. Willmott, C. J., & Matsuura, K. (2005). Advantages of the Mean Absolute Error (MAE) the Root Mean Square Error (RMSE) in Assessing Average Model Performance. Climate Research Vol.30, 79-82

Possible System Architecture for Travel Recommender

Travel recommender systems have been developed to meet the needs of users in the field of tourism. This system has several versions depending on the characteristics of the country, users and filtering techniques used. The development of recommendation filtering system techniques is very rapid so that the recommendation system has high enough complexity, but it also must have high usability. This paper discusses how the travel recommender system architecture is built by examining data structures, processing procedures and interaction design. The goal is to obtain the best usability in implementing a travel recommendation system. The system is built using the example case of finding the right tourist spot in Yogyakarta, Indonesia. This system applies several filtering techniques such as knowledge-based filtering, content-based filtering, and collaborative filtering. The evaluation results show that the system architecture optimized gets a usability level acceptable

IMPROVING THE DEPENDABILITY OF DESTINATION RECOMMENDATIONS USING INFORMATION ON SOCIAL ASPECTS

Prior knowledge of the social aspects of prospective destinations can be very influential in making travel destination decisions, especially in instances where social concerns do exist about specific destinations. In this paper, we describe the implementation of an ontology-enabled Hybrid Destination Recommender System (HDRS) that leverages an ontological description of five specific social attributes of major Nigerian cities, and hybrid architecture of content-based and case-based filtering techniques to generate personalised top-n destination recommendations. An empirical usability test was conducted on the system, which revealed that the dependability of recommendations from Destination Recommender Systems (DRS) could be improved if the semantic representation of social attributes information of destinations is made a factor in the destination recommendation process

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

USING FILTERS IN TIME-BASED MOVIE RECOMMENDER SYSTEMS

On a very high level, a movie recommendation system is one which uses data about the user, data about the movie and the ratings given by a user in order to generate predictions for the movies that the user will like. This prediction is further presented to the user as a recommendation. For example, Netflix uses a recommendation system to predict movies and generate favorable recommendations for users based on their profiles and the profiles of users similar to them. In user-based collaborative filtering algorithm, the movies rated highly by the similar users of a particular user are considered as recommendations to that user. But users’ preferences vary with time, which often affects the efficacy of the recommendation, especially in a movie recommendation system. Because of the constant variation of the preferences, there has been research on using time of rating or watching the movie as a significant factor for recommendation. If time is considered as an attribute in the training phase of building a recommendation model, the model might get complex. Most of the research till now does this in the training phase, however, we study the effect of using time as a factor in the post training phase and study it further by applying a genre-based filtering mechanism on the system. Employing this in the post training phase reduces the complexity of the method and also reduces the number of irrelevant recommendations

Selection of Software Product Line Implementation Components Using Recommender Systems: An Application to Wordpress

In software products line (SPL), there may be features which can be implemented by different components, which means there are several implementations for the same feature. In this context, the selection of the best components set to implement a given configuration is a challenging task due to the high number of combinations and options which could be selected. In certain scenarios, it is possible to find information associated with the components which could help in this selection task, such as user ratings. In this paper, we introduce a component-based recommender system, called (REcommender System that suggests implementation Components from selecteD fEatures), which uses information associated with the implementation components to make recommendations in the domain of the SPL configuration. We also provide a RESDEC reference implementation that supports collaborative-based and content-based filtering algorithms to recommend (i.e., implementation components) regarding WordPress-based websites configuration. The empirical results, on a knowledge base with 680 plugins and 187 000 ratings by 116 000 users, show promising results. Concretely, this indicates that it is possible to guide the user throughout the implementation components selection with a margin of error smaller than 13% according to our evaluation.Ministerio de Economía y Competitividad RTI2018-101204-B-C22Ministerio de Economía y Competitividad TIN2014-55894-C2-1-RMinisterio de Economía y Competitividad TIN2017-88209-C2-2-RMinisterio de Economía, Industria y Competitividad MCIU-AEI TIN2017-90644-RED