Diversifying Relevant Phrases

TL;DR

This paper introduces a novel optimization framework for generating relevant and diverse keywords for documents, addressing the challenge of balancing relevance and diversity in online advertising.

Contribution

It formulates the keyword diversification as an NP-hard optimization problem and proposes two convex relaxation methods, including eigenvalue and semi-definite programming approaches.

Findings

The eigenvalue-based method effectively balances relevance and diversity.

Semi-definite programming provides a flexible optimization framework.

Experimental results outperform existing heuristic approaches.

Abstract

Diverse keyword suggestions for a given landing page or matching queries to diverse documents is an active research area in online advertising. Modern search engines provide advertisers with products like Dynamic Search Ads and Smart Campaigns where they extract meaningful keywords/phrases from the advertiser's product inventory. These keywords/phrases are representative of a diverse spectrum of advertiser's interests. In this paper, we address the problem of obtaining relevant yet diverse keywords/phrases for any given document. We formulate this as an optimization problem, maximizing the parameterized trade-off between diversity and relevance constrained over number of possible keywords/phrases. We show that this is a combinatorial NP-hard optimization problem. We propose two approaches based on convex relaxations varying in complexity and performance. In the first approach, we show that the optimization problem reduces to an eigen value problem. In the second approach, we show that the optimization problem reduces to minimizing a quadratic form over an l1-ball. Subsequently, we show that this is equivalent to a semi-definite optimization problem. To prove the efficacy of our proposed formulation, we evaluate it on various real-world datasets and compare it to the state-of-the-art heuristic approaches.