LIMITED CONTENT ANALYSIS - Content-based techniques have a natural limit
in the number and type of features that are associated, whether automatically or
manually, with the objects they recommend. Domain knowledge is often needed,
e.g., for movie recommendations the system needs to know the actors and directors, and sometimes, domain ontologies are also needed. No content-based
recommendation system can provide suitable suggestions if the analyzed content
does not contain enough information to discriminate items the user likes from
items the user does not like. Some representations capture only certain aspects
of the content, but there are many others that would influence a user’s experience. For instance, often there is not enough information in the word frequency
to model the user interests in jokes or poems, while techniques for affective computing would be most appropriate. Again, for Web pages, feature extraction techniques from text completely ignore aesthetic qualities and additional multimedia
information.
To sum up, both automatic and manually assignment of features to items could
not be sufficient to define distinguishing aspects of items that turn out to be necessary for the elicitation of user interests.