Abstract:
Most existing recommender systems employ collaborative filtering
(CF) techniques in making projections about which items an eservice
user is likely to be interested in, i.e. they identify correlations
between users and recommend items which similar users have liked in
the past. Traditional CF techniques, however, have difficulties when confronted
with sparse rating data, and cannot cope at all with time-specific
items, like events, which typically receive their ratings only after they
have finished. Content-based (CB) algorithms, which consider the internal
structure of items and recommend items similar to those a user liked
in the past can partly make up for that drawback, but the collaborative
feature is totally lost on them. In this paper, modelling user and
item similarities as fuzzy relations, which allow to flexibly reflect the
graded/uncertain information in the domain, we develop a novel hybrid
CF-CB approach whose rationale is concisely summed up as "recommending
future items if they are similar to past ones that similar users
have liked", and which surpasses related work in the same spirit.
