1 research outputs found
Learning Complex Users' Preferences for Recommender Systems
Recommender systems (RSs) have emerged as very useful tools to help customers
with their decision-making process, find items of their interest, and alleviate
the information overload problem. There are two different lines of approaches
in RSs: (1) general recommenders with the main goal of discovering long-term
users' preferences, and (2) sequential recommenders with the main focus of
capturing short-term users' preferences in a session of user-item interaction
(here, a session refers to a record of purchasing multiple items in one
shopping event). While considering short-term users' preferences may satisfy
their current needs and interests, long-term users' preferences provide users
with the items that they may interact with, eventually. In this thesis, we
first focus on improving the performance of general RSs. Most of the existing
general RSs tend to exploit the users' rating patterns on common items to
detect similar users. The data sparsity problem (i.e. the lack of available
information) is one of the major challenges for the current general RSs, and
they may fail to have any recommendations when there are no common items of
interest among users. We call this problem data sparsity with no feedback on
common items (DSW-n-FCI). To overcome this problem, we propose a
personality-based RS in which similar users are identified based on the
similarity of their personality traits.Comment: 269 pages, 43 figures, 26 table