![](bg6.png)
In cases some matching rules are found, the score
of each product is updated and pointers are updated
too. The computation complexity of each time will be
highly reduced in that case. Classically, with a session
of size n, there were 2
n
possible sub-sequences. Here,
the number of sub-sequences studied is thus equal to
the number of sub-sequences from the preceding ses-
sion n − 1. This value is thus at most 2
n−1
, the num-
ber of sub-sequences searched is thus divided by at
least 2. In terms of overall cost, the cost of each sub-
sequence is no more dependent on the length of the
sub-sequence but has a fixed cost: 1. The resulting
cost is thus 2
n−1
, compared to n · 2
n−1
in classical ap-
proaches. The cost is thus divided by n.
For example, with a session of size 30, the computa-
tion time is thus divided by 30. The recommendations
will thus be more likely made instantaneously.
6 CONCLUSIONS
Recommender systems have been a great success in e-
commerce applications. M-commerce is a recent ap-
plication domain that has emerged in the last decade.
M-commerce and e-commerce share many properties
and we proposed to study the exploitation of well-
known RS in the framework of e-commerce in the m-
commerce domain. We specifically focused on data
mining RS: sequential association rules. This recom-
mendation model has a high time complexity, but due
to its implementation on a server, it runs fast enough
to be a real-time RS. After having presented the con-
text of application of our research, and our focus on
privacy preservation we chose to implement the RS
on the mobile side. We have then detailed the SAR
model and showed that the computation of recom-
mendations is time consuming. We have then pro-
posed an incremental RS. This RS leads to a smaller
complexity and allows to implement SAR-based RS
on mobiles.
This work will be pursued by a study of other recom-
mendation models to propose incremental version.
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