Deriving item features relevance from collaborative domain knowledge

TL;DR

This paper introduces a machine learning method that leverages collaborative domain knowledge to improve feature relevance in content-based recommender systems, especially addressing cold start challenges.

Contribution

It presents a novel wrapper feature weighting technique that embeds collaborative knowledge into content-based algorithms, enhancing recommendation quality.

Findings

Improved recommendation accuracy for cold start items

High flexibility of the proposed feature weighting method

Competitive performance compared to state-of-the-art algorithms

Abstract

An Item based recommender system works by computing a similarity between items, which can exploit past user interactions (collaborative filtering) or item features (content based filtering). Collaborative algorithms have been proven to achieve better recommendation quality then content based algorithms in a variety of scenarios, being more effective in modeling user behaviour. However, they can not be applied when items have no interactions at all, i.e. cold start items. Content based algorithms, which are applicable to cold start items, often require a lot of feature engineering in order to generate useful recommendations. This issue is specifically relevant as the content descriptors become large and heterogeneous. The focus of this paper is on how to use a collaborative models domain-specific knowledge to build a wrapper feature weighting method which embeds collaborative knowledge in a content based algorithm. We present a comparative study for different state of the art algorithms and present a more general model. This machine learning approach to feature weighting shows promising results and high flexibility.