In the diploma thesis, we analyzed and implemented a recommendation system for personalized electronic catalogue. We have described the tools used and the operation of our programs for data transformation and e-catalogue creation. We also wrote down the advantages offered by e-catalogues over traditional ones and the findings written by researchers on a similar problem.
To create the e-catalogue, we first described where we obtained the data set and how we modified and limited it for our needs. Next, we described the purpose and implementation of the methods used, as well as solutions to problems that arise when creating e-catalogues. At the end, we combined the predictions of the methods and showed the efficiency of the created recommendation system.
Our personalized e-catalogue results show precision, recall and F-score information. We were particularly interested in recall data, as it tells us how many products we need to recommend in order to satisfy all user interests. In addition to the results of the recommender system, we implemented tests and demonstrated the effectiveness of each method.
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