Academic Orientation Supported by Hybrid Intelligent Decision Support System - Efficient Decision Support Systems: Practice and Challenges from Current to Future - page 246

explicitly, e.g., through questionnaires, or implicitly inferred from user behavior over time.
On the contrary to collaborative recommender systems, the content based ones assume that
the user does not interact with other users. These systems exploit the information related to
the description content of the previously evaluated items such that the system learns the
relationship between a single user and the description of the new items. Let S be a set of
items to be recommended, the description of each item s i is provided by m features c j , such
as is showed in Table 1.

c 1 … c j … c m

s 1 v 11 … v 1j … v 1m

…… …… ……

s n v n1 … v nj … v nm

Table 1. Item description
In content-based recommendation methods each user's profile is built with information
related to previous items selected by the user in the past. Therefore the user's profile P of
user u is computed based on the description of previously experienced items and optionally
some explicit rating r i about them (Adomavicius 2005) .

Table 2. User Profile
The user's profile (see Table 2) is used to determine the appropriateness of the item for
recommendation purposes by matching it with the items descriptions (see Table 1). Since, as
mentioned earlier, CBRS are designed mostly to recommend text-based items, the content in
these systems is usually described with keywords which can represent, for example,
features of an item.
More formally, let ContentBasedProfile(c) be the profile of user, c, containing tastes and
preferences of this user, for example a vector of weights ( w c1 , …, w ck ) where each weight w ci
denotes the importance of keyword k i to user c .
In content-based systems, the utility function u(c,s) is usually defined as:

u(c,s) = score(ContentBasedProfile(c),Content(s)) (1)
Both ContentBasedProfile(c) of user c and Content(s) of document s can be represented as
vectors v  and v  of keyword weights. Moreover, utility function u(c,s) is usually
represented in the information retrieval literature by some scoring heuristic defined in terms
of vectors

v  and v  , such as the cosine similarity measure



K




 vv

 

vv

 

ic is , ,

 

cs i  1

ucs ,  c s v v ,   (2)

cs

vv 

K K

2 2

v v

c s

2 2

ic , is ,

i  1 i  1