Content-based approach
Its roots in information retrieval and
information filtering
Focus on recommending items containing textual information, such as
documents,
Web sites (URLs), and news messages etc
Improvement by the use
of user profiles that contain
information about users’ tastes, preferences, and needs
The process of Content-based
recommendation
Construction of per-user content-based profile
TF : Term Frequency, IDF : Inverse Document Frequency
N : Number of the documents
ni : How many times keyword ki is seen in the document
fi,j : Number of times keyword ki is seen in the document dj
Similarity measurement
u(c,s) : the
utility function
ContentBasedProfile(c) = (wc1, …, wck) : the profile of user c
cosine similarity
measure
Limitations of content-based approach
Limited Content Analysis
automatic feature
extraction is much harder to multimedia data
cannot distinguish
between a well-written article and a badly written one
Overspecialization
no experience, no
recommendation
New User Problem