From Vector Space to Neural Ranking: A Comparative Study of Modern Information Retrieval Models

Abstract

Information retrieval has changed dramatically over the past decades. Early systems relied on simple keyword matching, but modern search engines must understand meaning, context, and user intent. This paper examines three major families of retrieval models that have shaped this evolution: vector space models, probabilistic retrieval, and neural retrieval. Vector space models represent documents and queries as weighted term vectors and rank them by similarity, providing a simple yet effective way to handle partial matches. Probabilistic models, such as BM25, treat relevance as a probability and rank documents according to how likely they are to satisfy a query, offering a stronger theoretical foundation for ranking. Neural retrieval goes further by learning dense semantic representations that can capture meaning beyond exact word overlap, enabling more accurate matching and reranking. We review key works including Salton et al.’s foundational vector space model, Robertson and Zaragoza’s probabilistic relevance framework, and recent neural approaches such as Dense Passage Retrieval and large language model-based retrieval surveys. The discussion shows that modern search systems rarely rely on a single model. Instead, they combine fast lexical retrieval with powerful neural rerankers to balance speed and accuracy. This hybrid approach reflects the current state of the field and points toward future research directions. Keywords: information retrieval; BM25; vector space model; probabilistic retrieval; neural retrieval; reranking