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rithm that fits the demand of a mobile app?
Answering this question, this work may contribute
to the improvement of other recommendation algo-
rithms. The data collected here can be used for future
work related to the algorithms of recommendations
focused on the e-commerce for gifts. The general ob-
jective of this work is to develop a gift suggestion
algorithm that recommends the best products to the
user, based on their profile.
The secondary objectives of this work are: Investi-
gate possible gift recommendation solutions that take
into account the user profile;Substantiate the adopted
solution with other algorithms solutions.
2 THEORETICAL BASIS
The recommendation systems are intended to assist in
the suggestion of items, products, services and con-
tents, partially or fully automatically, according to the
user’s interests and needs (Burke, 2002). These sys-
tems can provide information that helps the user in
the decision making of which items to choose, which
can be, for example, books, music, movies, products,
as well as recommendation systems can, based on a
user’s profile, suggest Items to the user directly with-
out the intermediation of the same, such as gifts that
the user may have interest based on his profile.
According to Resnick, a recommendation system
has as input data that the user giver, the system then
uses them to make the recommendations and then
directs them to the relevant recipients (Resnick and
Varian, 1997). The term ”recommendation systems”
came to replace the term ”collaborative filtering”, af-
ter which collaborative filtering refers to a specific
recommendation algorithm. In general, recommenda-
tion systems are referred to systems that recommend
a list of user products or systems that help users eval-
uate products.
The systems of recommendations are classi-
fied based on how the recommendations are made.
Among the main systems of recommendations cited
are Brke (Burke, 2002), Balabanovic and Shoham
(Balabanovi and Shoham, 1997) and finally Ado-
mavicius and Tuzhilin (Adomavicius and Tuzhilin,
2005). These systems will be presented in the fol-
lowing subsections.
2.1 Content-based Recommendation
Also known as content-based filtering, this technique
consists of recommending similar items to the user to
those that the user chose in the past, that is, according
to the history of items that he has rated as favorite or
acquired in the past.
According to (Burke, 2002), each item in a set I is
defined by characteristics associated with it, a prod-
uct, for example, may have characteristics such as:
name, price, category, etc. Based on these charac-
teristics that the items can be compared and the sim-
ilarity between them, this characterization serves as
input to this recommendation system, since the items
recommended to the user that may be of his interest
are those similar to what he used on the past and are
recorded on his history.
Content-based recommendation systems originate
from information retrieval techniques and the re-
search done by (Burke, 2002), (Balabanovi and
Shoham, 1997) and (Adomavicius and Tuzhilin,
2005) on information filtering searches.
2.2 Collaborative Recommendation
The Collaborative Recommendation is the one which
the user will be recommended items that people
with similar tastes and preferences liked in the past.
In other words, it tries to predict the relevance
of items for a particular user based on the items
previously rated by other users.(Adomavicius and
Tuzhilin, 2005)
2.3 Hybrid Approach
This method proposes to combine two or more types
of data recommendation techniques. The main objec-
tive of the use of this method is what concerns some
limitation that may exist in the individual use of other
types of techniques.
As an example, the main approach that can occur
with the combination of content-based recommenda-
tion systems and the collaborative recommendation,
based on the analysis made by (Adomavicius and
Tuzhilin, 2005), are:
1. Implement the Collaborative and Content-
based Methods Separately and Combine Their
Predictions. This way you can combine the final-
ized recommendations of the two techniques and
offer the user a final recommendation. Another
possibility is the system check which of the two
techniques offered the user the best recommenda-
tions and then, select and present one of the two;
2. Integrating Some Content-based Features into
a Collaborative Approach. The system main-
tains content-based user profiles, and can compare
users to determine which ones are the most simi-
lar, and finally use collaborative filtering. Thus, in
addition to having the recommendations based on
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