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considerations that need to go into a recommendation. For this reason, Guttman (1999) describes Tête-à-Tête as
“product and merchant brokering” system rather than a recommender system. However, under the definition given
above, Tête-à-Tête does fit since its main output is a recommendation (a top-ranked item) that is generated on a
personalized basis.
The flexibility of utility-based systems is also to some degree a failing. The user must construct a complete
preference function, and must therefore weigh the significance of each possible feature. Often this creates a
significant burden of interaction. Tête-à-Tête uses a small number of “stereotype” preference functions to get the
user started, but ultimately the user needs to look at, weigh, and select a preference function for each feature that
describes an item of interest. This might be feasible for items with only a few characteristics, such as price, quality
and delivery date, but not for more complex and subjective domains like movies or news articles. PersonaLogic does
not require the user to input a utility function, but instead derives the function through an interactive questionnaire.
While the complete explicit utility function might be a boon to some users, for example, technical users with specific
purchasing requirements, it is likely to overwhelm a more casual user with a less-detailed knowledge. Large moves in
the product space, for example, from “sports cars” to “family cars” require a complete re-tooling of the preference
function, including everything from interior space to fuel economy. This makes a utility-based system less
appropriate for the casual browser.
Knowledge-based recommender systems are prone to the drawback of all knowledge-based systems: the need for
knowledge acquisition. There are three types of knowledge that are involved in such a system:
Catalog knowledge: Knowledge about the objects being recommended and their features. For example, the Entree
recommender should know that “Thai” cuisine is a kind of “Asian” cuisine.
Functional knowledge: The system must be able to map between the user’s needs and the object that might satisfy
those needs. For example, Entree knows that a need for a romantic dinner spot could be met by a restaurant that is
“quiet with an ocean view.”
User knowledge: To provide good recommendations, the system must have some knowledge about the user. This
might take the form of general demographic information or specific information about the need for which a
recommendation is sought. Of these knowledge types, the last is the most challenging, as it is, in the worst case, an
instance of the general user-modeling problem (Towle & Quinn, 2000).
Despite this drawback, knowledge-based recommendation has some beneficial characteristics. It is appropriate for
casual exploration, because it demands less of the user than utility-based recommendation. It does not involve a start-
up period during which its suggestions are low quality. A knowledge-based recommender cannot “discover” user
niches, the way collaborative systems can. On the other hand, it can make recommendations as wide-ranging as its
knowledge base allows.
Table II summarizes the five recommendation techniques that we have discussed here, pointing out the pros and
cons of each. Collaborative and demographic techniques have the unique capacity to identify cross-genre niches and
can entice users to jump outside of the familiar. Knowledge-based techniques can do the same but only if such
associations have been identified ahead of time by the knowledge engineer.
All of the learning-based techniques (collaborative, content-based and demographic) suffer from the ramp-up
problem in one form or another. The converse of this problem is the stability vs. plasticity problem for such learners.
Once a user’s profile has been established in the system, it is difficult to change one’s preferences. A steak-eater who
becomes a vegetarian will continue to get steakhouse recommendations from a content-based or collaborative
recommender for some time, until newer ratings have the chance to tip the scales. Many adaptive systems include
some sort of temporal discount to cause older ratings to have less influence, but they do so at the risk of losing
information about interests that are long-term but sporadically exercised (Billsus & Pazzani, 2000; Schwab, et al.
2001). For example, a user might like to read about major earthquakes when they happen, but such occurrences are
sufficiently rare that the ratings associated with last year’s earthquake are gone by the time the next big one hits.
Knowledge- and utility-based recommenders respond to the user’s immediate need and do not need any kind of
retraining when preferences change.
The ramp-up problem has the side-effect of excluding casual users from receiving the full benefits of collaborative
and content-based recommendation. It is possible to do simple market-basket recommendation with minimal user
input: Amazon.com’s “people who bought X also bought Y” but this mechanism has few of the advantages
commonly associated with the collaborative filtering concept. The learning-based technologies work best for
dedicated users who are willing to invest some time making their preferences known to the system. Utility- and
knowledge-based systems have fewer problems in this regard because they do not rely on having historical data
about a user’s preferences. Utility-based systems may present difficulties for casual users who might be unwilling to
tailor a utility function simply to browse a catalog.