Database Reference
In-Depth Information
NOTE
One of the most difficult parts of building a recommendation engine is
determining how to quantify and weight non-numeric explicit data.
Determining the right balance and scale is as much art as science and
requires some experimentation.
In the case of implicit data, when used singularly (that is, only purchase
history or only click history), it represents a Boolean data type. It is not
necessary to represent the negative cases in these models because
missing data translates to false. When multiple implicit data points are
combined, it is necessary to scale them appropriately.
Table 12.2
Examples of Explicit and Implicit Data
Explicit
Implicit
Ratings
Feedback
Demographics
Psychographics (personality/lifestyle/attitude)
Ephemeral need (need for a moment)
Purchase history
Clicks
Browse history
Clustering, unlike collaborative filtering, focuses instead on an item's
taxonomies, attributes, description, or properties. It does not need
behavioral or interaction data, and is often a good choice when the data
required for collaborative filtering is not available.
To generate recommendations, Mahout supports collaborative filtering and
clustering. To understand what each of these are, we can look at the three
common recommendation engine implementations:
1.
User-to-user collaborative filtering
: In a user-to-user
recommendation implementation, clusters or neighborhoods of similar
users are formed based on some user behavior (for instance, purchasing
an item or attending a movie). Because similar users are clustered
together, these clusters are then used to generate recommendations.
2.
Item-to-item collaborative filtering
: The item-to-item
recommendation implementation works in a similar manner to that of
