Academic Orientation Supported by Hybrid Intelligent Decision Support System - Efficient Decision Support Systems: Practice and Challenges from Current to Future - page 245

 Ability to filter items based on quality and taste, not only on its features.

 Ability to provide serendipitous recommendations. Other systems never recommend

products which are outside the box , i.e., recommended products are not very different to
the ones positively rated by the customer.

 Adaptability, its quality is improved along the time. When the number of customers

and rates increases these systems work better.

Fig. 2. Processes performed in the collaborative recommendation scheme
Despite the general good performance of these systems, they present several weaknesses
and limitations:
 The cold-start problem : This problem is presented with both users and products. When a
new user access to the system, it has not any information about him/her. Therefore, the
system cannot compare him/her with the users of the database and cannot provide
recommendations. When a new item is added and it has not been assessed by any user
yet, it cannot be recommended . This is the problem we focus on this paper, so it will be
further detailed below.
 The "grey sheep" problem: This system does not work properly with "grey sheep" users,
which are in the frontier of two groups of users.
 Historical data set: Being an adaptive system can be an advantage but can be also a
disadvantage when the system is starting and the historical data set is small.
Obviously, no recommender system can work without some initial information but the
quality and efficiency of the system depends on the ability to predict successful
recommendations with the minimum amount of information about users and items. Some
solutions to this problem require knowledge about the items, or content based information,
for example, a movie recommender system needs to know attributes like actors, the genre,
etc. This kind of knowledge is not always available or is scarce. Other proposed solutions
are only partial solutions because improve the recommendations when the data about the
user is small but do not work when this set is empty (new user).

2.2 Content-based recommender systems
Content-Based Recommender Systems (CBRS) recommend similar items to those ones that
the user liked in the past by comparing various candidate items with items previously rated
by the user and the bestmatching item(s) are recommended (Adomavicius 2005, Jung 2004,
Symeonidis 2007). The content-based approach to recommendation has its roots in
information retrieval and information filtering research (Adomavicius 2005, Martínez 2007a,
Pazzani 1999). CBRS need user profiles that contain information about users' tastes,
preferences, and needs. Profiles can be built using information collected from users