2 research outputs found
Time-weighted Attentional Session-Aware Recommender System
Session-based Recurrent Neural Networks (RNNs) are gaining increasing
popularity for recommendation task, due to the high autocorrelation of user's
behavior on the latest session and the effectiveness of RNN to capture the
sequence order information. However, most existing session-based RNN
recommender systems still solely focus on the short-term interactions within a
single session and completely discard all the other long-term data across
different sessions. While traditional Collaborative Filtering (CF) methods have
many advanced research works on exploring long-term dependency, which show
great value to be explored and exploited in deep learning models. Therefore, in
this paper, we propose ASARS, a novel framework that effectively imports the
temporal dynamics methodology in CF into session-based RNN system in DL, such
that the temporal info can act as scalable weights by a parallel attentional
network. Specifically, we first conduct an extensive data analysis to show the
distribution and importance of such temporal interactions data both within
sessions and across sessions. And then, our ASARS framework promotes two novel
models: (1) an inter-session temporal dynamic model that captures the long-term
user interaction for RNN recommender system. We integrate the time changes in
session RNN and add user preferences as model drifting; and (2) a novel
triangle parallel attention network that enhances the original RNN model by
incorporating time information. Such triangle parallel network is also
specially designed for realizing data argumentation in sequence-to-scalar RNN
architecture, and thus it can be trained very efficiently. Our extensive
experiments on four real datasets from different domains demonstrate the
effectiveness and large improvement of ASARS for personalized recommendation
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