4,861 research outputs found
Practical Deep Reinforcement Learning Approach for Stock Trading
Stock trading strategy plays a crucial role in investment companies. However,
it is challenging to obtain optimal strategy in the complex and dynamic stock
market. We explore the potential of deep reinforcement learning to optimize
stock trading strategy and thus maximize investment return. 30 stocks are
selected as our trading stocks and their daily prices are used as the training
and trading market environment. We train a deep reinforcement learning agent
and obtain an adaptive trading strategy. The agent's performance is evaluated
and compared with Dow Jones Industrial Average and the traditional min-variance
portfolio allocation strategy. The proposed deep reinforcement learning
approach is shown to outperform the two baselines in terms of both the Sharpe
ratio and cumulative returns
Counterfactual Explanation for Fairness in Recommendation
Fairness-aware recommendation eliminates discrimination issues to build
trustworthy recommendation systems.Explaining the causes of unfair
recommendations is critical, as it promotes fairness diagnostics, and thus
secures users' trust in recommendation models. Existing fairness explanation
methods suffer high computation burdens due to the large-scale search space and
the greedy nature of the explanation search process. Besides, they perform
score-based optimizations with continuous values, which are not applicable to
discrete attributes such as gender and race. In this work, we adopt the novel
paradigm of counterfactual explanation from causal inference to explore how
minimal alterations in explanations change model fairness, to abandon the
greedy search for explanations. We use real-world attributes from Heterogeneous
Information Networks (HINs) to empower counterfactual reasoning on discrete
attributes. We propose a novel Counterfactual Explanation for Fairness
(CFairER) that generates attribute-level counterfactual explanations from HINs
for recommendation fairness. Our CFairER conducts off-policy reinforcement
learning to seek high-quality counterfactual explanations, with an attentive
action pruning reducing the search space of candidate counterfactuals. The
counterfactual explanations help to provide rational and proximate explanations
for model fairness, while the attentive action pruning narrows the search space
of attributes. Extensive experiments demonstrate our proposed model can
generate faithful explanations while maintaining favorable recommendation
performance
Search-Based Fairness Testing: An Overview
Artificial Intelligence (AI) has demonstrated remarkable capabilities in
domains such as recruitment, finance, healthcare, and the judiciary. However,
biases in AI systems raise ethical and societal concerns, emphasizing the need
for effective fairness testing methods. This paper reviews current research on
fairness testing, particularly its application through search-based testing.
Our analysis highlights progress and identifies areas of improvement in
addressing AI systems biases. Future research should focus on leveraging
established search-based testing methodologies for fairness testing.Comment: IEEE International Conference on Computing (ICOCO 2023), Langkawi
Island, Malaysia, pp. 89-94, October 202
Understanding or Manipulation: Rethinking Online Performance Gains of Modern Recommender Systems
Recommender systems are expected to be assistants that help human users find
relevant information automatically without explicit queries. As recommender
systems evolve, increasingly sophisticated learning techniques are applied and
have achieved better performance in terms of user engagement metrics such as
clicks and browsing time. The increase in the measured performance, however,
can have two possible attributions: a better understanding of user preferences,
and a more proactive ability to utilize human bounded rationality to seduce
user over-consumption. A natural following question is whether current
recommendation algorithms are manipulating user preferences. If so, can we
measure the manipulation level? In this paper, we present a general framework
for benchmarking the degree of manipulations of recommendation algorithms, in
both slate recommendation and sequential recommendation scenarios. The
framework consists of four stages, initial preference calculation, training
data collection, algorithm training and interaction, and metrics calculation
that involves two proposed metrics. We benchmark some representative
recommendation algorithms in both synthetic and real-world datasets under the
proposed framework. We have observed that a high online click-through rate does
not necessarily mean a better understanding of user initial preference, but
ends in prompting users to choose more documents they initially did not favor.
Moreover, we find that the training data have notable impacts on the
manipulation degrees, and algorithms with more powerful modeling abilities are
more sensitive to such impacts. The experiments also verified the usefulness of
the proposed metrics for measuring the degree of manipulations. We advocate
that future recommendation algorithm studies should be treated as an
optimization problem with constrained user preference manipulations.Comment: 33 pages, 11 figures, 4 tables, ACM Transactions on Information
System
Looks Can Be Deceiving: Linking User-Item Interactions and User's Propensity Towards Multi-Objective Recommendations
Multi-objective recommender systems (MORS) provide suggestions to users
according to multiple (and possibly conflicting) goals. When a system optimizes
its results at the individual-user level, it tailors them on a user's
propensity towards the different objectives. Hence, the capability to
understand users' fine-grained needs towards each goal is crucial. In this
paper, we present the results of a user study in which we monitored the way
users interacted with recommended items, as well as their self-proclaimed
propensities towards relevance, novelty and diversity objectives. The study was
divided into several sessions, where users evaluated recommendation lists
originating from a relevance-only single-objective baseline as well as MORS. We
show that despite MORS-based recommendations attracted less selections, its
presence in the early sessions is crucial for users' satisfaction in the later
stages. Surprisingly, the self-proclaimed willingness of users to interact with
novel and diverse items is not always reflected in the recommendations they
accept. Post-study questionnaires provide insights on how to deal with this
matter, suggesting that MORS-based results should be accompanied by elements
that allow users to understand the recommendations, so as to facilitate their
acceptance.Comment: Accepted as a short paper at ACM RecSys 2023 conference. See
https://doi.org/10.1145/3604915.360884
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