An Empirical Evaluation of Collaborative Filtering Methods with Implicit Feedback
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Journal Title
Journal ISSN
Volume Title
Perustieteiden korkeakoulu |
Master's thesis
Authors
Date
2018-06-18
Department
Major/Subject
Computer Science
Mcode
SCI3042
Degree programme
Master’s Programme in Computer, Communication and Information Sciences
Language
en
Pages
70
Series
Abstract
There are two primary ways of collecting preferences of users towards items. In the first method, users express their interest explicitly using features such as star ratings. On the other, their interest is inferred from their implicit interaction such as frequency of visit and purchase. Data collected on preferences of users is used by recommender systems to produce suggestions to the users. A user receives recommendations of items when other users who have similar preference express interest towards those items. This way of producing recommendations is known as collaborative filtering. In this thesis, we set out to examine if implicit feedback datasets can be comparable to explicit feedback datasets in being the source of user information to generate recommendations. To achieve this, we use a real-life dataset that contains product visit and purchase information of users. We examine how open-source tools perform in processing our dataset that contains implicit feedback. We study the availability, reliability, and usability of these tools. Using these tools and our dataset, we demonstrate how to build models that are generalizable and have predictive power that extends beyond observed user preferences of the dataset. We demonstrate the use of evaluation metrics to measure the accuracy and relevancy of recommendations generated using the models we built. We discover from the experiments, our dataset can be used to generate accurate and relevant recommendation to the users. However, we report the inadequacy of an output of such valuable qualities as accuracy and relevancy without elements of novelty, serendipity, and diversity. A recommender engine has to have the capacity to provide useful suggestions that are outside of the obvious interest range of users.Description
Supervisor
Kaski, PetteriThesis advisor
Salakka, AkiKeywords
recommender systems, collaborative filtering, implicit feedback, matrix factorization, open source tools, KNN