SCoR: A Synthetic Coordinate based Recommender System
Introduction
Recommender systems try to predict the preferences of users for specific items, based on an analysis of previous consumer preferences.
The problem of content recommendation can be described as follows. Given a set U of users, a set I of items (e.g. movies) and a set R of ratings (evaluations) of users for items, we need to estimate (predict) the rating for a user-item pair which is not in R.
Methodology
SCoR is a Synthetic Coordinate based Recommendation system which is shown to outperform the most popular algorithmic techniques in the field, approaches like matrix factorization and collaborative filtering.
It assigns synthetic coordinates to nodes (users and items), so that the distance between a user and an item provides an accurate prediction of the user’s preference for that item. The proposed framework has several benefits. It is parameter free, thus requiring no fine tuning to achieve high performance, and is more resistance to the cold-start problem compared to other algorithms
It provides important annotations of the dataset, such as the physical detection of users and items with common and unique characteristics as well as the identification of outliers.
Table 1. RMSE of the ten recommender systems for the four datasets [1].
Downloads
Jar executable of SCoR algorithm.
Related Publications
[1]. H. Papadakis, C. Panagiotakis and P. Fragopoulou, SCoR: A Synthetic Coordinate based Recommender System, Expert Systems with Applications, vol. 79, pp.8-19, 2017.