Personalized web search using clickthrough data and web page rating

Personalization of Web search is to carry out retrieval for each user incorporating his/her interests. We propose a novel technique to construct personalized information retrieval model from the users' clickthrough data and Web page ratings. This model builds on the userbased collaborative filtering technology and the top-N resource recommending algorithm, which consists of three parts: user profile, user-based collaborative filtering, and the personalized search model. Firstly, we conduct user's preference score to construct the user profile from clicked sequence score and Web page rating. Then it attains similar users with a given user by user-based collaborative filtering algorithm and calculates the recommendable Web page scoring value. Finally, personalized informaion retrieval be modeled by three case applies (rating information for the user himself; at least rating information by similar users; not make use of any rating information). Experimental results indicate that our technique significantly improves the search performance. © 2012 ACADEMY PUBLISHER

Web Recommended System Library Book Selection Using Item Based Collaborative Filtering Method

Collaborative filtering is generally used as a recommendation system. There is a huge growth in the amount of data on the web. This recommendation system helps users to choose books on the web, which is the most suitable for users. Collaborative collects previous user information about an item such as books, films, music, ideas, and so on. To recommend the best items. The recommendation system serves as a bridge gap between the user and the application or website by providing many options from which users make their choice of interests. Personalized recommendations help users get a list of items that interest them on the web. Most recommendation systems use collaborative filtering techniques to produce recommendations to users. In this final project uses items based collaborative filtering method. In the item based collaborative filtering method, calculate similarity using the adjusted cosine similarity equation, after obtaining the similarity value between books, then look for the predicted value of a product that has not been rated by the user by using the weighted sum equation. In this final project, the two equations above are used and to measure the accuracy of the predictions produced by this technique. Keywords: Recommended System, Item-Based Collaborative Filterin

Collaborative recommendations with content-based filters for cultural activities via a scalable event distribution platform

Nowadays, most people have limited leisure time and the offer of (cultural) activities to spend this time is enormous. Consequently, picking the most appropriate events becomes increasingly difficult for end-users. This complexity of choice reinforces the necessity of filtering systems that assist users in finding and selecting relevant events. Whereas traditional filtering tools enable e.g. the use of keyword-based or filtered searches, innovative recommender systems draw on user ratings, preferences, and metadata describing the events. Existing collaborative recommendation techniques, developed for suggesting web-shop products or audio-visual content, have difficulties with sparse rating data and can not cope at all with event-specific restrictions like availability, time, and location. Moreover, aggregating, enriching, and distributing these events are additional requisites for an optimal communication channel. In this paper, we propose a highly-scalable event recommendation platform which considers event-specific characteristics. Personal suggestions are generated by an advanced collaborative filtering algorithm, which is more robust on sparse data by extending user profiles with presumable future consumptions. The events, which are described using an RDF/OWL representation of the EventsML-G2 standard, are categorized and enriched via smart indexing and open linked data sets. This metadata model enables additional content-based filters, which consider event-specific characteristics, on the recommendation list. The integration of these different functionalities is realized by a scalable and extendable bus architecture. Finally, focus group conversations were organized with external experts, cultural mediators, and potential end-users to evaluate the event distribution platform and investigate the possible added value of recommendations for cultural participation

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

Leather Product Recommendation System using Collaborative Filtering Algorithm

Currently, e-commerce and marketplaces are constantly evolving to satisfy consumers' needs more efficiently and conveniently. Technological developments make e- commerce smarter to serve users by providing recommendations according to user needs. Various types of products are traded in the marketplace, including leather products. Therefore, this study aims to build a recommendation system for leather products. By using the Collaborative Filtering algorithm, the system will provide recommendations for leather products to users based on patterns formed from the history of rating or user ratings. This research results in a web-based recommendation system to help users find leather products by implementing Collaborative Filtering. The experimental results on 50 leather products and 644 ratings given by 30 respondents showed an average value of Mean Absolute Error (MAE) from the application of Collaborative Filtering of 1,929. This MAE value indicates that Collaborative Filtering can recommend skin products well according to user expectations

Analisis dan Implementasi Item-Based Clustering Hybrid Method Recommender System

ABSTRAKSI: Recommender system adalah sebuah aplikasi yang mencari dan memberikan rekomendasi berupa item-item dengan memprediksi rating-ratingnya berdasarkan persamaan karakteristik user dalam memberikan informasi. Tugas Akhir ini mengimplementasikan dan menganalisis Item-Based Clusering Hybrid Method (ICHM) yang merupakan recommender system yang menggabungkan pendekatan antara collobaratve filtering dengan content based filtering. Penggabungan pendekatan content based filtering dengan collaborative filtering pada metode ICHM bertujuan untuk menanggulangi kekurangan yang ada pada kedua pendekatan sebelumnya. Sistem rekomendasi dibangun berbasis web dengan menggunakan Java Servlet Pages (JSP) dan Apache Tomcat sebagai web server . Tugas akhir ini menganalisis akurasi prediksi rating yang dihasilkan oleh sistem rekomendasi dengan metode ICHM. Parameter yang digunakan dalam analisis adalah jumlah cluster dan koefisien c sebagai penggabungan nilai similarity. Sistem rekomendasi dengan metode ICHM memiliki keunggulan dapat memprediksi item yang belum dirating sama sekali. Selain itu jumlah cluster mempengaruhi nilai similarity berdasarkan content item serta komposisi penggabungan berdasarkan koefisien c mempengaruhi hasil prediksi rating untuk active user. Akurasi prediksi yang dihasilkan oleh metode ICHM cenderung semakin meningkat dengan bertambahnya jumlah cluster.Kata Kunci : recommender system, hybrid, metode ICHM, item-based clustering hybrid methodABSTRACT: Recommender is an application that cam search and recommend items by predicting their ratings based on the similarity of user’s characteristic in giving the information. In this fiinal project, author implements Item-Based Clustering Hybrid Method (ICHM) which is one of hybrid recommender system that combines the collaborative filtering and content based filtering. The purpose of combination between content based filtering and collaborative filtering in ICHM for overcoming each filtering shortcomings. The analysis carried out to the accuracy of rating prediction result given by the recommender system. The number of clusters and the value of coefficient c as variable in combining similarity value are used to be the parameters of analysis. ICHM recommender system has an advantage that can predict new items that have no rating at all. Different number of clusters gives different value of similarity based on the item’s content and the combination compotition based on coefficient c affect the result of rating prediction. The increasing number of clusters in ICHM gives the prediction accuracy tend to increase.Keyword: recommender system, hybrid, ICHM, item-based clustering hybrid metho

Hybrid group recommendations for a travel service

Recommendation techniques have proven their usefulness as a tool to cope with the information overload problem in many classical domains such as movies, books, and music. Additional challenges for recommender systems emerge in the domain of tourism such as acquiring metadata and feedback, the sparsity of the rating matrix, user constraints, and the fact that traveling is often a group activity. This paper proposes a recommender system that offers personalized recommendations for travel destinations to individuals and groups. These recommendations are based on the users' rating profile, personal interests, and specific demands for their next destination. The recommendation algorithm is a hybrid approach combining a content-based, collaborative filtering, and knowledge-based solution. For groups of users, such as families or friends, individual recommendations are aggregated into group recommendations, with an additional opportunity for users to give feedback on these group recommendations. A group of test users evaluated the recommender system using a prototype web application. The results prove the usefulness of individual and group recommendations and show that users prefer the hybrid algorithm over each individual technique. This paper demonstrates the added value of various recommendation algorithms in terms of different quality aspects, compared to an unpersonalized list of the most-popular destinations