Week 4 - Matching Models: Ranked Boolean and Vector Space - Reading Note

I read:
Christopher D. Manning, Prabhakar Raghavan, Hinrich Schutze, “Introduction to Information Retrieval”. Cambridge University Press. 2008. Sections 1.3 and 1.4 & Chapter 6.

From 1.3 & 1.4, boolean retrieval vs ranked retrieval: user preference generally relates to experience. Power users usually find complex boolean queries preferable to ranked retrieval queries.

From Chapter 6:
Approaches to determining result quality:

• Parametric and Zone indexes: basic ranking and metadata filtering
• Term Weighting - higher score the more times a term appears in a doc
• vector space scoring

Vector Space Modeling:

• represent documents and queries as vectors in a common vector space. This allows scoring, document classification, document clustering
• many specific schemes and methods

I think that I will need the lecture before I truly understand all of this material.

Question:
Why are vectors the best way to model this?