Implementasi Metode Collaborative Filtering pada Aplikasi Rekomendasi Hotel dan Wisma di Kota Palangka Raya Berbasis Website

Abstrak. Hotel merupakan suatu lembaga yang menyediakan para tamu untuk menginap, di mana setiap orang dapat menginap, makan, minum dan menikmati fasilitas yang lainnya dengan melakukan transaksi pembayaran. Sedangkan wisma adalah bangunan untuk tempat tinggal, kantor atau kumpulan rumah, komplek perumahan, permukiman yang di peruntukkan untuk menunjang urusan atau kegiatan pada bidang tertentu. Karena semakin banyaknya pembangunan hotel dan wisma yang di bangun di kota Palangka Raya, sering kali menimbulkan permasalahan bagi para wisatawan yaitu dalam melakukan pencarian dan menentukan hotel dan wisma berdasarkan fasilitas jasa yang disediakan. Berdasarkan hal tersebut, dibuatlah suatu sistem yang dapat membantu memberikan rekomendasi hotel dan wisma kepada wisatawan. Metodologi yang digunakan dalam aplikasi Rekomendasi Hotel Dan Wisma Di Kota Palangka Raya ini adalah waterfall. Pengujian menggunakan metode blackbox. Hasil penelitian ini adalah sebuah aplikasi Rekomendasi Hotel Dan Wisma Di Kota Palangka Raya Berbasis Website memfasilitasi wisatawan atau pengunjung dalam mendapatkan informasi serta rekomendasi hotel dan wisma tanpa harus terlebih dahulu mengunjungi satu persatu website hotel dan wisma. Pengujian dilakukan menggunakan blackbox.   Abstract. Hotel is an institution that provides guests to stay, where everyone can stay, eat, drink and enjoy other facilities by making payment transactions. Wisma is a building for residence, office or group of houses, housing complexes, settlements that are intended to support business or activities in certain fields. Due to the increasing number of hotel and guest house constructions being built in the city of Palangka Raya, it often creates problems for tourists, namely in searching and determining hotels and guest houses based on the service facilities provided. Based on this, a system was created that could help provide hotel and guest house recommendations to tourists. The methodology used in the Hotel and Wisma Recommendation application in the City of Palangka Raya is a waterfall. Testing using the blackbox method. The results of this study are a Website-Based Hotel and Guesthouse Recommendation Application in Palangka Raya City facilitating tourists or visitors in obtaining information and recommendations for hotels and guesthouses without having to first visit the hotel and guest house websites one by one. The tests using blackbox method

Personnalisation d'analyses décisionnelles sur des données multidimensionnelles

This thesis investigates OLAP analysis personalization within multidimensional databases. OLAP analyse is modeled through a graph where nodes represent the analysis contexts and graph edges represent the user operations. The analysis context regroups the user query as well as result. It is well described by a specific tree structure that is independent on the visualization structures of data and query languages. We provided a model for user preferences on the multidimensional schema and values. Each preference is associated with a specific analysis context. Based on previous models, we proposed a generic framework that includes two personalization processes. First process, denoted query personalization, aims to enhancing user query with related preferences in order to produce a new one that generates a personalized result. Second personalization process is query recommendation that allows helping user throughout the OLAP data exploration phase. Our recommendation framework supports three recommendation scenarios, i.e., assisting user in query composition, suggesting the forthcoming query, and suggesting alternative queries. Recommendations are built progressively basing on user preferences. In order to implement our framework, we developed a prototype system that supports query personalization and query recommendation processes. We present experimental results showing the efficiency and the effectiveness of our approaches.Le travail présenté dans cette thèse aborde la problématique de la personnalisation des analyses OLAP au sein des bases de données multidimensionnelles. Une analyse OLAP est modélisée par un graphe dont les noeuds représentent les contextes d'analyse et les arcs traduisent les opérations de l'utilisateur. Le contexte d'analyse regroupe la requête et le résultat. Il est décrit par un arbre spécifique qui est indépendant des structures de visualisation des données et des langages de requête. Par ailleurs, nous proposons un modèle de préférences utilisateur exprimées sur le schéma multidimensionnel et sur les valeurs. Chaque préférence est associée à un contexte d'analyse particulier. En nous basant sur ces modèles, nous proposons un cadre générique comportant deux mécanismes de personnalisation. Le premier mécanisme est la personnalisation de requête. Il permet d'enrichir la requête utilisateur à l'aide des préférences correspondantes afin de générer un résultat qui satisfait au mieux aux besoins de l'usager. Le deuxième mécanisme de personnalisation est la recommandation de requêtes qui permet d'assister l'utilisateur tout au long de son exploration des données OLAP. Trois scénarios de recommandation sont définis : l'assistance à la formulation de requête, la proposition de la prochaine requête et la suggestion de requêtes alternatives. Ces recommandations sont construites progressivement à l'aide des préférences de l'utilisateur. Afin valider nos différentes contributions, nous avons développé un prototype qui intègre les mécanismes de personnalisation et de recommandation de requête proposés. Nous présentons les résultats d'expérimentations montrant la performance et l'efficacité de nos approches. Mots-clés: OLAP, analyse décisionnelle, personnalisation de requête, système de recommandation, préférence utilisateur, contexte d'analyse, appariement d'arbres de contexte

Collaborative Filtering with Maximum Entropy

We describe a novel maximum entropy (maxent) approach for generating online recommendations as a user navigates through a collection of documents. We show how to handle high-dimensional sparse data and represent it as a collection of ordered sequences of document requests. Our representation and the maxent approach have several advantages: (1) we can naturally model long-term interactions and dependencies in the data sequences; (2) we can query the model quickly once it is learned, which makes the method applicable to highvolume Web servers; and (3) we obtain empirically high quality recommendations. Although maxent learning is computationally infeasible if implemented in the straightforward way, we explore data clustering and several algorithmic techniques to make learning practical even in high dimensions. We present several methods for combining the predictions of maxent models learned in different clusters. We conduct offline tests using over six months worth of data from ResearchIndex, a popular online repository of over 470,000 computer science documents. We show that our maxent algorithm is arguably one of the most accurate recommenders, as compared to such techniques as correlation, mixture of Markov models, mixture of multinomial models, individual similarity-based recommenders currently available on ResearchIndex, and even various combinations of current ResearchIndex recommenders