Türkiye otel verileri üzerinde kümeleme ve öneri sistemi

As in most sectors, the development of an intelligent recommendation system in tourism becomes an important issue. Tourism agencies are putting maximum effort into suggesting the best and most valuable hotels for their customers. With the help of B2B relations between agencies and hotels, tourism agencies hold large feature datasets about hotels. Summarizing or interpretation of huge amount of data, requires the implementation of data analysis methodologies. Also, the tourism data is unique in terms of geography and culture. Thus, every new dataset requires a dedicated analytical process. Furthermore, raw data is in the form of a sparse binary matrix of hotel features, it poses a technical challenge for any analytical process. This thesis presents a comparison of different clustering and dimension reduction methodologies for real-world hotel data of this nature. The dataset represents 61% of the hotels in Turkey. Hotel clustering is the first step to acquire the hotel recommendations. To generate matching recommendations for customers, multiple level system is designed. Another layer of this cascade structure is clustering of the users according to their previous hotels moreover clusters of hotels. Our approach has resulted with linkage of two type of collaborative filtering which called as hybrid recommendation system. Thereby the suggested system provides personalized hotel recommendations based on the hotel's amenities and visitor patterns. The first challenge in this work is the nature of the raw data set. The hotel features that we work with are all binary variables. While there are plenty of metrics, algorithms, and techniques dedicated to discovering knowledge from numerical variables, the methods for processing binary ones are limited. Thus, one of our contributions is to propose an experimental methodology for discovering the best clusters for the hotels, which are explained with binary features. In this methodology, we transform the sparse binary data set into numerical ones by using different dimension reduction techniques. Then, well-known clustering algorithms are applied and evaluated by various success criteria metrics. The most succeeded clustering algorithm has been decided as OPTICS. Due to the nature of the algorithm, noise labeled hotels has been created by the results of the algorithm. Cosine similarity between hotel features is calculated and the result has been used for elimination of the noise labeled hotels. Since this is the first step in the process, rather than focusing on the interpretation of preliminary clusters, we have focused on solving analytical problems such as determining the number of the best clusters, identifying the most distinguished clusters, and simply selecting the best algorithm for hotel clustering. During the recommendation engine design, we used collaborative filtering method which consist of item and user features. Thereby the engine can be considered as a hybrid system. According to designed similarity matrix between users, the target user (customer) receives a bunch of last visited hotels. Similarity between users has been decided based on their clusters of already visited hotels. Thereby every user has at least one similar user, and these users have at least one order in our analysis table. While creating recommendation arrays, the results checked with sales test table which consist only latest orders. If the test value for target customer matches with one of the members of recommendation array, the recommendation considered as success. Even though the binary labeled success criteria (success or fail) which is very loose method, the recommendation engine has not achieved the expected success ratio. At last, data preparation steps, used models and recommendation system results has been discussed and the reasons of the success rates are explained in this thesis.