Information extraction (IE)
- The task of automatically extracting structured information from unstructured and/or semi-structured machine-readable documents.
- Processing human language texts by means of natural language processing (NLP)
- Recent activities in multimedia document processing like automatic annotation and content extraction out of images/audio/video could be seen as information extraction.
20. InformationExtraction - 4p.pdf
20. InformationExtraction.pdf
21. Recommenders - 4p.pdf
21. Recommenders.pdf
Recommender system
- Subclass of information filtering system
- Seeks to predict the "rating" or "preference" that a user would give to an item
- Utilized in movies, music, news, books, research articles,
search queries, social tags, and products in general
- Recommender systems for experts, collaborators, jokes,
restaurants, garments, financial services, life insurance,
romantic partners (online dating), and Twitter pages
- First mentioned in a technical report as a "digital bookshelf" in 1990
by Jussi Karlgren at Columbia University
- Research groups led by Pattie Maes at MIT, Will Hill at Bellcore,
and Paul Resnick, also at MIT whose work with GroupLens
was awarded the 2010 ACM Software Systems Award.
1) Collaborative filtering approaches
build a model from a user's past behaviour
(previously purchased or selected items
and/or numerical ratings given to those items)
This model is then used to predict items (or ratings for items)
2) Content-based filtering approaches
utilize a series of discrete characteristics of an item
in order to recommend additional items with similar properties
3) Hybrid Recommender Systems
Weighted: scores are combined numerically
Switching: chooses and applies the selected one
Mixed: recommendations from different recommenders are presented together
Feature Combination: features derived from different knowledge sources
are combined together and given to a single recommendation algorithm
Feature Augmentation: one recommendation technique is used to compute
a feature or set of features, which is then part of the input to the next technique.
Cascade: recommenders are given strict priority,
with the lower priority ones breaking ties in the scoring of the higher ones
Meta-level: one recommendation technique is applied and produces some sort of model,
which is then the input used by the next technique