Hybrid
recommendation systems are mix of single recommendation systems as
sub-components. This hybrid approach was
introduced to cope with a problem of conventional recommendation systems. Two main problems have been addressed by
researchers in this field, cold-start problem and stability versus plasticity
problem. Cold-start problem occurs when
learning based techniques like collaborative, content-based, and demographic
recommendation algorithms are used. Their
learning stages are based on users’ information, in most cases a user has to
input their ratings or preferences manually and therefore the collection of
this kind of information is hard to be achieved. Stability problem means that it is
sometimes hard to change established users’ profiles which have been
established after a given period of time using the systems. The former problem can be solved with the
hybrid approach because different type of recommendation technique like
knowledge based algorithm can be less affected by the problem. One of the solutions for the latter problem
is temporal discount, which make older ratings with less influence.
Therefore, various hybrid
recommendation techniques have been introduced and tested. Four major recommendation techniques
constructing hybrids are collaborative filtering (CF), content-based (CN),
demographic, and knowledge-based (KB). Unlike
the first three which make use of learning algorithms, KB exploits domain
knowledge and makes inferences about users’ needs and preferences. Hybrid recommendation systems can produce
outputs which outperforms single component systems by combining these multiple
techniques. The most common hybridizing
methodology is combining different techniques of different types, for example,
mixing CN and CF. However, it is also
possible to mix different techniques of the same type, like naive Bayes based
CN plus kNN based CN. Also, mixing same
type of techniques with different datasets can be possible.
Burke
(2002) introduced taxonomy for the hybrid recommendation systems. He classified them into seven categories,
weighted, switching, mixed, feature combination, feature augmentation, cascade,
and meta-level.
Weighted
hybrid – This hybrid combines scores from each component using linear
formula. Therefore, components must be
able to produce its recommendation score which can be linearly combinable. Also, the components have to be consistent
relative accuracy across the product space and to perform uniformly.
Switching
hybrid – The issue of this hybrid is selecting one recommender among
candidates. This selection is made
according to the situation it is experiencing.
The criterion for the selection like confidence value or external
criteria should exist and the components might have different performance with
different situations.
Mixed
hybrid – This is a hybrid which is based on the merging and presentation of
multiple ranked lists into one. Each
component of this hybrid should be able to produce recommendation lists with
ranks and the core algorithm of mixed hybrid merges them into a single ranked
list. The issue here is how the new rank
scores should be produced. One simple
example is simply adding each rank score like CF_rank (3) + CN_rank (2) à Mixed_rank (5).
Feature
combination hybrid – There exist two very different recommendation components
for this hybrid, contributing and actual recommender. The actual recommender works with data
modified by the contributing one. The
contributing one injects features of one source to the source of the other
component.
Feature
augmentation hybrid – This is similar to the feature combination hybrids but
different in that the contributor generates new features. It is more flexible
and adds smaller dimension than feature combination method.
Cascade
hybrid – This one is a tie breaker. The
secondary recommender is just a tie breaker and does refinements.
Meta-level
hybrid – For this one, contributing and actual recommenders exist but the
former one completely replaces the data for the latter one, not just part of
it.
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