A NEW SIMILARITY COEFFICIENT FOR A COLLABORATIVE FILTERING ALGORITHM

Year 2017, Volume: 59 Issue: 2, 41 - 54, 21.12.2017

Ozge Mercanoglu Sıncan Zeynep Yıldırım

Abstract

Recommender systems give the
opportunity to present automatically personalized content across many digital
marketing channels to visitors depending on visitor movements on the site. In
recent years, there has been a lot of interest in e-commerce companies in order
to offer personalized content. So, recommender systems become very popular and
many studies have been done in this regard. New works are being done day by day
to improve the results. In this paper, we propose a new memory-based
collaborative filtering algorithm. Calculation of similarities between items or
users is a critical step in memory-based CF algorithms. Therefore, we proposed
a new function for calculation of similarities based on user ratings. In this
study the more similar the user's pleasures are, the more similar it is to the
products the users choose, is adopted. The adopted idea in this study is that
the more similar the user's pleasures are, the more similar products are
chosen. We estimate the degree which a user is interested in X product. To do
this, we find other users who are interested in product X and calculate the
similarity ratios of those users to the user. We tested our algorithm in
MovieLens 100K dataset and compared to other similarity functions. We used MAE
and RMSE measures in our experiments. 

Keywords

Recommendation systems, collaborative filtering, user similarity

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