POSIT: Promotion of Semantic Item Tail via Adversarial Learning

TL;DR

This paper introduces an adversarial learning approach to promote less popular, semantically similar items in recommendation systems, enhancing diversity and overall performance.

Contribution

It presents a novel adversarial framework that promotes disadvantaged items using semantic similarity, improving recommendation diversity and effectiveness.

Findings

Increases recommendation coverage of less popular items

Improves overall recommendation performance

Effective across multiple datasets and baselines

Abstract

In many recommendations, a handful of popular items (e.g., movies / television shows, news, etc.) can be dominant in recommendations for many users. However, we know that in a large catalog of items, users are likely interested in more than what is popular. The dominance of popular items may mean that users will not see items that they would probably enjoy. In this paper, we propose a technique to overcome this problem using adversarial machine learning. We define a metric to translate the user-level utility metric in terms of an advantage/disadvantage over items. We subsequently used that metric in an adversarial learning framework to systematically promote disadvantaged items. Distinctly, our method integrates a small-capacity model to produce semantically meaningful weights, leading to an algorithm that identifies and promotes a semantically similar item within the learning process. In the empirical study, we evaluated the proposed technique on three publicly available datasets and seven competitive baselines. The result shows that our proposed method not only improves the coverage, but also, surprisingly, improves the overall performance.