3 research outputs found
The Technological Gap Between Virtual Assistants and Recommendation Systems
Virtual assistants, also known as intelligent conversational systems such as
Google's Virtual Assistant and Apple's Siri, interact with human-like responses
to users' queries and finish specific tasks. Meanwhile, existing recommendation
technologies model users' evolving, diverse and multi-aspect preferences to
generate recommendations in various domains/applications, aiming to improve the
citizens' daily life by making suggestions. The repertoire of actions is no
longer limited to the one-shot presentation of recommendation lists, which can
be insufficient when the goal is to offer decision support for the user, by
quickly adapting to his/her preferences through conversations. Such an
interactive mechanism is currently missing from recommendation systems. This
article sheds light on the gap between virtual assistants and recommendation
systems in terms of different technological aspects. In particular, we try to
answer the most fundamental research question, which are the missing
technological factors to implement a personalized intelligent conversational
agent for producing accurate recommendations while taking into account how
users behave under different conditions. The goal is, instead of adapting
humans to machines, to actually provide users with better recommendation
services so that machines will be adapted to humans in daily life.Comment: 6 pages, Repor
Leveraging Trust and Distrust in Recommender Systems via Deep Learning
The data scarcity of user preferences and the cold-start problem often appear
in real-world applications and limit the recommendation accuracy of
collaborative filtering strategies. Leveraging the selections of social friends
and foes can efficiently face both problems. In this study, we propose a
strategy that performs social deep pairwise learning. Firstly, we design a
ranking loss function incorporating multiple ranking criteria based on the
choice in users, and the choice in their friends and foes to improve the
accuracy in the top-k recommendation task. We capture the nonlinear
correlations between user preferences and the social information of trust and
distrust relationships via a deep learning strategy. In each backpropagation
step, we follow a social negative sampling strategy to meet the multiple
ranking criteria of our ranking loss function. We conduct comprehensive
experiments on a benchmark dataset from Epinions, among the largest publicly
available that has been reported in the relevant literature. The experimental
results demonstrate that the proposed model beats other state-of-the art
methods, attaining an 11.49% average improvement over the most competitive
model. We show that our deep learning strategy plays an important role in
capturing the nonlinear correlations between user preferences and the social
information of trust and distrust relationships, and demonstrate the importance
of our social negative sampling strategy on the proposed model
Survey for Trust-aware Recommender Systems: A Deep Learning Perspective
A significant remaining challenge for existing recommender systems is that
users may not trust the recommender systems for either lack of explanation or
inaccurate recommendation results. Thus, it becomes critical to embrace a
trustworthy recommender system. This survey provides a systemic summary of
three categories of trust-aware recommender systems: social-aware recommender
systems that leverage users' social relationships; robust recommender systems
that filter untruthful noises (e.g., spammers and fake information) or enhance
attack resistance; explainable recommender systems that provide explanations of
recommended items. We focus on the work based on deep learning techniques, an
emerging area in the recommendation research