Conquering the rating bound problem in neighborhood-based collaborative filtering: a function recovery approach

TL;DR

This paper introduces a novel function recovery approach to address the rating bound problem in neighborhood-based collaborative filtering, significantly improving rating estimation accuracy in recommender systems.

Contribution

It formalizes rating estimation as a scalar function recovery problem and proposes an optimization method using low-order norm minimization to overcome rating bounds.

Findings

Achieves 37% improvement in rating estimation accuracy.

Effectively overcomes the rating bound problem.

Validated on Douban, Goodreads, and MovieLens datasets.

Abstract

As an important tool for information filtering in the era of socialized web, recommender systems have witnessed rapid development in the last decade. As benefited from the better interpretability, neighborhood-based collaborative filtering techniques, such as item-based collaborative filtering adopted by Amazon, have gained a great success in many practical recommender systems. However, the neighborhood-based collaborative filtering method suffers from the rating bound problem, i.e., the rating on a target item that this method estimates is bounded by the observed ratings of its all neighboring items. Therefore, it cannot accurately estimate the unobserved rating on a target item, if its ground truth rating is actually higher (lower) than the highest (lowest) rating over all items in its neighborhood. In this paper, we address this problem by formalizing rating estimation as a task of recovering a scalar rating function. With a linearity assumption, we infer all the ratings by optimizing the low-order norm, e.g., the $l_1/2$-norm, of the second derivative of the target scalar function, while remaining its observed ratings unchanged. Experimental results on three real datasets, namely Douban, Goodreads and MovieLens, demonstrate that the proposed approach can well overcome the rating bound problem. Particularly, it can significantly improve the accuracy of rating estimation by 37% than the conventional neighborhood-based methods.