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Table 6.3 Computational complexity of recommendation algorithms for news
Algorithm
Complexity
Most popular
O(
MN
)
Most recent
O (
N
)
Random
O ( N )
User-based CF
O(
M
(
M
1
)
N
)
MN 2
Item-based CF
O (
)
2
ALS CF
O ( MN k
)
SGD CF
O(
S
)
MN 2
)
M refers to the number of users while N refers to the number of items. S represents an unknown
variable which depends on the configuration with which (user, item) pairs are selected
Content-based filtering
O (
instance, the system switches between implementations of collaborative filtering,
content-based filtering, and other methods. Second, we may keep the algorithmfixed.
The multi-armed bandit switches parameters in this scenario. For instance, we select
item-based collaborative filtering. This algorithm expects inputs including similarity
function. Pearson's correlation coefficient and cosine similarity represent examples
of such similarity functions. The multi-armed bandit may then switch these. Finally,
we may limit the data we use to learn a model representing interaction patterns.
For instance, we may argue that with time passing the relevancy of news dimin-
ishes. Thus, we may consider various time frames. For instance, we may learn a
model based on interactions which occurred up to 3h, up to 6h, and up to a day ago.
The multi-armed bandit may switch which data to use. Lommatzsch [ 42 ] describes
a sophisticated way to allay negative effects induced by exploration. The proposed
method evaluates all configurations in a slightly delayed time. In other words, instead
of averaging performances over time, the method re-issues every request to all con-
figurations. Thus, the system assesses performances more reliably. Consequently, the
system learns to select the most promising configuration more quickly. Results show
that algorithms performances strongly depend on contextual factors. As a result,
individual algorithms cannot dominate other algorithms consistently.
6.5.5 Scalability
As discussed in Sect. 6.4 , recommending news articles entails technical requirements.
In particular, systems must deal with a large volume of requests arriving in high rates.
Consequently, recommendation algorithms have to scale at such conditions.
Table 6.3 refers each algorithms to an estimated complexity. Note that intelligent
ways of situating data and similar tools may decrease the actual complexity. The table
ought to illustrate differences between individual methods. For instance, random and
most recent methods operate independent from the user dimension. The complexity
 
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