IR-chapter7: computing scores in a complete search system

FastCosineScore

constructing a heap to pick out top K components
Inexact top K document retrieval
index elimination
considering documents containing terms whose idf exceeds a preset threshold
considering documents containing many(even all) terms
champion list
precompute r documents with the highest weights for each term.
r does not to be the same for every term.(rarer term, larger)
static quality scoring and ordering
net-score
global champion list, expansion two lists sorted by g(d) value
impact ordering
sorted by common ordering: document-at-a-time scoring
sorted by uncommon ordering: term-at-a-time scoring
ordered by a decreasing tf value,advantage:
1.stop after considering a prefix of posting list
2.consedering query terms in decreasing order of idf.
cluster pruning
pick ,compute nearest, cluster, computing cosine similarity from q to each leader, then the closest L and its follower
variation - b1,b2

tiered indexes
motivation: A has fewer than K documents
solution: we set a tf threshold of 20 for tier 1 and 10 for tier 2, meaning that the tier 1 index only has postings entries with tf values exceeding 20, and the tier 2 index only has postings entries with tf values exceeding 10.

tiered indexes

designing parsing and scoring function
query parser - translate the user-specified keywords into a query with various operators
scoring function - manual configuration or machine-learned scoring
putting it all together

a complete search system

results snippets: snippets of text accompanying each document in the results list for a query.

Google: the semantics of a conjunctive query that only retrieves documents containing all or most query terms.