- Many users find it easier to submit natural language
queries,
i.e., queries that are simply (long) lists of good keywords or phrases,
instead of build complex Boolean queries.
- Similarity measures that consider collection statistics can be
used
to prepare rankings of retrieved documents, that attempt to present
relevant documents before others.
- Expanding user queries, with terms from relevant retrieved
documents or other sources (e.g., morphological or thesaurus
processing), can often improve effectiveness, especially with good term
selection or screening, weighting, and similarity computation.
- Reweighting based on relevance feedback data can improve the
effectiveness of document ranking - essentially training the
system
regarding the terms that relate to an information need.
- Data structures and implementations for ranking and relevance
feedback are derived from an overall model (e.g., vector, probabilistic)
and tuned to classes of, and individual instances of, document
collections.