65,344 research outputs found

    Matrix Factorization Techniques for Context-Aware Collaborative Filtering Recommender Systems: A Survey

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    open access articleCollaborative Filtering Recommender Systems predict user preferences for online information, products or services by learning from past user-item relationships. A predominant approach to Collaborative Filtering is Neighborhood-based, where a user-item preference rating is computed from ratings of similar items and/or users. This approach encounters data sparsity and scalability limitations as the volume of accessible information and the active users continue to grow leading to performance degradation, poor quality recommendations and inaccurate predictions. Despite these drawbacks, the problem of information overload has led to great interests in personalization techniques. The incorporation of context information and Matrix and Tensor Factorization techniques have proved to be a promising solution to some of these challenges. We conducted a focused review of literature in the areas of Context-aware Recommender Systems utilizing Matrix Factorization approaches. This survey paper presents a detailed literature review of Context-aware Recommender Systems and approaches to improving performance for large scale datasets and the impact of incorporating contextual information on the quality and accuracy of the recommendation. The results of this survey can be used as a basic reference for improving and optimizing existing Context-aware Collaborative Filtering based Recommender Systems. The main contribution of this paper is a survey of Matrix Factorization techniques for Context-aware Collaborative Filtering Recommender Systems

    Intelligent techniques for recommender systems

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    This thesis focuses on the data sparsity issue and the temporal dynamic issue in the context of collaborative filtering, and addresses them with imputation techniques, low-rank subspace techniques and optimizations techniques from the machine learning perspective. A comprehensive survey on the development of collaborative filtering techniques is also included

    A Survey of Matrix Completion Methods for Recommendation Systems

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    In recent years, the recommendation systems have become increasingly popular and have been used in a broad variety of applications. Here, we investigate the matrix completion techniques for the recommendation systems that are based on collaborative filtering. The collaborative filtering problem can be viewed as predicting the favorability of a user with respect to new items of commodities. When a rating matrix is constructed with users as rows, items as columns, and entries as ratings, the collaborative filtering problem can then be modeled as a matrix completion problem by filling out the unknown elements in the rating matrix. This article presents a comprehensive survey of the matrix completion methods used in recommendation systems. We focus on the mathematical models for matrix completion and the corresponding computational algorithms as well as their characteristics and potential issues. Several applications other than the traditional user-item association prediction are also discussed

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

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    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

    Discovering the Impact of Knowledge in Recommender Systems: A Comparative Study

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    Recommender systems engage user profiles and appropriate filtering techniques to assist users in finding more relevant information over the large volume of information. User profiles play an important role in the success of recommendation process since they model and represent the actual user needs. However, a comprehensive literature review of recommender systems has demonstrated no concrete study on the role and impact of knowledge in user profiling and filtering approache. In this paper, we review the most prominent recommender systems in the literature and examine the impression of knowledge extracted from different sources. We then come up with this finding that semantic information from the user context has substantial impact on the performance of knowledge based recommender systems. Finally, some new clues for improvement the knowledge-based profiles have been proposed.Comment: 14 pages, 3 tables; International Journal of Computer Science & Engineering Survey (IJCSES) Vol.2, No.3, August 201
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