26,067 research outputs found
Learning Personalized Risk Preferences for Recommendation
The rapid growth of e-commerce has made people accustomed to shopping online.
Before making purchases on e-commerce websites, most consumers tend to rely on
rating scores and review information to make purchase decisions. With this
information, they can infer the quality of products to reduce the risk of
purchase. Specifically, items with high rating scores and good reviews tend to
be less risky, while items with low rating scores and bad reviews might be
risky to purchase. On the other hand, the purchase behaviors will also be
influenced by consumers' tolerance of risks, known as the risk attitudes.
Economists have studied risk attitudes for decades. These studies reveal that
people are not always rational enough when making decisions, and their risk
attitudes may vary in different circumstances.
Most existing works over recommendation systems do not consider users' risk
attitudes in modeling, which may lead to inappropriate recommendations to
users. For example, suggesting a risky item to a risk-averse person or a
conservative item to a risk-seeking person may result in the reduction of user
experience. In this paper, we propose a novel risk-aware recommendation
framework that integrates machine learning and behavioral economics to uncover
the risk mechanism behind users' purchasing behaviors. Concretely, we first
develop statistical methods to estimate the risk distribution of each item and
then draw the Nobel-award winning Prospect Theory into our model to learn how
users choose from probabilistic alternatives that involve risks, where the
probabilities of the outcomes are uncertain. Experiments on several e-commerce
datasets demonstrate that our approach can achieve better performance than many
classical recommendation approaches, and further analyses also verify the
advantages of risk-aware recommendation beyond accuracy
When and where do you want to hide? Recommendation of location privacy preferences with local differential privacy
In recent years, it has become easy to obtain location information quite
precisely. However, the acquisition of such information has risks such as
individual identification and leakage of sensitive information, so it is
necessary to protect the privacy of location information. For this purpose,
people should know their location privacy preferences, that is, whether or not
he/she can release location information at each place and time. However, it is
not easy for each user to make such decisions and it is troublesome to set the
privacy preference at each time. Therefore, we propose a method to recommend
location privacy preferences for decision making. Comparing to existing method,
our method can improve the accuracy of recommendation by using matrix
factorization and preserve privacy strictly by local differential privacy,
whereas the existing method does not achieve formal privacy guarantee. In
addition, we found the best granularity of a location privacy preference, that
is, how to express the information in location privacy protection. To evaluate
and verify the utility of our method, we have integrated two existing datasets
to create a rich information in term of user number. From the results of the
evaluation using this dataset, we confirmed that our method can predict
location privacy preferences accurately and that it provides a suitable method
to define the location privacy preference
DJ-MC: A Reinforcement-Learning Agent for Music Playlist Recommendation
In recent years, there has been growing focus on the study of automated
recommender systems. Music recommendation systems serve as a prominent domain
for such works, both from an academic and a commercial perspective. A
fundamental aspect of music perception is that music is experienced in temporal
context and in sequence. In this work we present DJ-MC, a novel
reinforcement-learning framework for music recommendation that does not
recommend songs individually but rather song sequences, or playlists, based on
a model of preferences for both songs and song transitions. The model is
learned online and is uniquely adapted for each listener. To reduce exploration
time, DJ-MC exploits user feedback to initialize a model, which it subsequently
updates by reinforcement. We evaluate our framework with human participants
using both real song and playlist data. Our results indicate that DJ-MC's
ability to recommend sequences of songs provides a significant improvement over
more straightforward approaches, which do not take transitions into account.Comment: -Updated to the most recent and completed version (to be presented at
AAMAS 2015) -Updated author list. in Autonomous Agents and Multiagent Systems
(AAMAS) 2015, Istanbul, Turkey, May 201
Neural Collaborative Ranking
Recommender systems are aimed at generating a personalized ranked list of
items that an end user might be interested in. With the unprecedented success
of deep learning in computer vision and speech recognition, recently it has
been a hot topic to bridge the gap between recommender systems and deep neural
network. And deep learning methods have been shown to achieve state-of-the-art
on many recommendation tasks. For example, a recent model, NeuMF, first
projects users and items into some shared low-dimensional latent feature space,
and then employs neural nets to model the interaction between the user and item
latent features to obtain state-of-the-art performance on the recommendation
tasks. NeuMF assumes that the non-interacted items are inherent negative and
uses negative sampling to relax this assumption. In this paper, we examine an
alternative approach which does not assume that the non-interacted items are
necessarily negative, just that they are less preferred than interacted items.
Specifically, we develop a new classification strategy based on the widely used
pairwise ranking assumption. We combine our classification strategy with the
recently proposed neural collaborative filtering framework, and propose a
general collaborative ranking framework called Neural Network based
Collaborative Ranking (NCR). We resort to a neural network architecture to
model a user's pairwise preference between items, with the belief that neural
network will effectively capture the latent structure of latent factors. The
experimental results on two real-world datasets show the superior performance
of our models in comparison with several state-of-the-art approaches.Comment: Proceedings of the 2018 ACM on Conference on Information and
Knowledge Managemen
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