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Section 5 demonstrates the implementation and
application of the TCPRS. Finally, conclusions and
future studies are discussed in Section 6.
2 RELATED WORKS
Recommender systems use the concept of rating to
measure how much a particular item is liked by the
target user. Adomavicius and Tuzhilin, (2005) stated
that in middle 1990s researchers in the
recommendation areas started to do research
focusing on ratings structure, and the problem of
recommendation has been simplified to be the
problem of predicting ratings of items that have not
been known by a user. In the literature of this field,
content-based (CB) methods and collaborative
filtering (CF) methods are the most popular
techniques adopted in recommender systems
(Iaquinta et al., 2007). The CB methods recommend
products by comparing the content or profile of the
unknown products to those products that are
preferred by the users. Differ from CB methods, CF
methods do not involve user profiles and item
features when giving recommendation, but only rely
on the user ratings. A third approach is hybrid
methods which combine CB and CF methods and it
is becoming more popular among researchers in this
field. Iaquinta et al., (2007) involved CB methods
into CF model for calculating user similarities using
user profiles, which are built using machine learning
techniques. Su et al., (2007) built a model using
multiple experts including both CB and CF
approaches to take different strategies in different
situations. All these methods are based on the rating
structure. In order to increase the accuracy or
performance of recommender systems, many
researchers have tried with some non-ratings
techniques, such as data mining, machine learning
and intelligent agents depending on different
circumstances. In this paper, we will only focus on
the algorithms and applications of collaborative
filtering methods.
There are several kinds of CF methods, among
them the most popular approaches are user-based CF
and item-base CF. A user-based CF method is to use
the ratings of users those are most similar to the
target user for predicting the ratings of unrated
items. On the other hand, item-based CF method
uses the similarities of items for predicting ratings.
Literature shows that the current trend of
recommender system is to combine two or more
techniques together for improving the accuracy of
recommendation or overcome the limitations of
single recommender algorithm, and the combination
of user-based CF and item-base CF may achieve a
good performance in a big-user-set and big-item-set
environment.
Therefore, this study implements a personalized
recommendation system TCPRS for telecom
products/services using a hybrid approach that
combines item-based and user-based CF methods.
3 ALGORITHM DESCRIPTION
Based on literature review (Shi et al., 2008), an
algorithm which integrates Item-based and User-
based Collaborative Filtering is designed to the
TCPRS. This algorithm takes advantage of both the
horizontal and vertical information in the user-item
rating table. The algorithm is described in seven
steps as follows.
1) Generate a User-item Rating Table: Each user
is represented by a set of item-rating pairs and the
summary of all those pairs can be collected into a
user-item rating matrix.
2) Calculate Item Similarity: This step measures
the similarities between any two items. Pearson
correlation is selected for this step which measures
the similarity between two items by calculating the
linear correlation between the two vectors.
3) Item Neighbours Selection: In most CF
methods, a number of neighbours will be selected
when predicting ratings. In the TCPRS, we used the
top-N technique for neighbour selection.
4) Predict Empty Ratings using Item-based CF: In
this step, all the unrated ratings can be calculated
using item-based CF method and all the empty cells
in the user-item rating table will be filled.
5) Calculate User Similarity: Beside from
predicting the ratings based on the similarities of
items, we can also predict the ratings by analysis the
similarities between users. We also use the Pearson
correlation algorithm for calculating the user
similarity.
6) Select Top-N Similar Users
: Similar as step 3,
we need to select a number of neighbour users for
predicting ratings. The Top-N technique is used in
the TCPRS system.
7) Final Recommendation Generation: The final
step of the algorithm is to predict the ratings of
every unrated telecomm product/services for the
target users using user-based CF. The new predicted
ratings will replace the ratings predicted in Step 4,
and be regarded as the final results. Based on the
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