Adaptive Recommendation System Strategies: An Exploration of
Online Machine Learning Algorithms
Fenglin Lu
Aberdeen Institute of Data Science and Artificial Intelligence, South China Normal University, Foshan, Guangdong, China
Keywords: MAB, Reinforcement Learning, Adaptive Recommendation System.
Abstract: This paper investigates the application of online machine learning algorithms in adaptive recommendation
systems, focusing on the integration of reinforcement learning and multi-armed bandit (MAB) frameworks to
enhance user experience and platform efficiency. As digital environments become increasingly dynamic,
traditional recommendation systems face challenges such as the exploration-exploitation dilemma and the
cold start problem. To address these issues, adaptive strategies that leverage the real-time decision-making
capabilities of reinforcement learning and the optimization potential of MAB algorithms are explored. The
research demonstrates how these technologies improve personalization and operational efficiency by
dynamically adjusting to user preferences and behaviors. This paper serves as a valuable resource for
individuals seeking to comprehend and explore the utilization of MAB algorithms in recommendation systems,
while also shedding light on potential avenues for future research in the domain of online recommendation
systems.
1 INTRODUCTION
Recommendation systems have become integral to
our lives as a byproduct of the rapid development of
information technology. According to Chen (2002),
with the fast-paced advancement of online and mobile
technologies, the internet, while providing a wealth of
information to users, also leads to problems caused by
information overload. This refers to the massive
amount of information that prevents users from
quickly and accurately pinpointing the information
they need. Therefore, timely capturing user
preferences, delivering content of interest, and
enhancing user satisfaction with recommendation
systems are crucial. These factors drive substantial
traffic to recommendation systems, creating a
positive feedback loop. Personalized
recommendation technology is a key method to
address information overload, offering users
appropriate recommendation lists to improve
satisfaction. In this process, recommendation systems
continuously collect and store user attribute
information, manage item feature information, and
record user interaction behaviors to provide the most
satisfactory recommendation list possible.
Additionally, personalized recommendation systems
can adapt to changes in user interests and preferences,
significantly enhancing user experience.
However, the application of personalized
recommendation systems faces two major challenges:
the exploration-exploitation dilemma and the cold
start problem. When facing a new user, due to the lack
of historical data, it is challenging to effectively
recommend products, resulting in suboptimal
performance. The introduction of any new user or
product experiences a transition from no data to rich
data, and effectively resolving the data deficiency at
the initial stage is known as the "cold start problem
(Jiang, 2024)." Over the long term, the
recommendation system must balance whether to
frequently recommend the best-known items to users
or to explore different items for potentially better
outcomes. The multi-armed bandit (MAB) algorithms
are specifically designed to allow recommendation
systems to make adaptive choices, maximizing
returns and optimal feedback through each selection.
Common recommendation systems include various
online news websites, video platforms, and short
video apps on mobile devices. These platforms
collect user preference information based on
browsing history to recommend videos or other
content that might interest the user.
Lu, F.
Adaptive Recommendation System Strategies: An Exploration of Online Machine Learning Algorithms.
DOI: 10.5220/0012949200004508
Paper published under CC license (CC BY-NC-ND 4.0)
In Proceedings of the 1st International Conference on Engineering Management, Information Technology and Intelligence (EMITI 2024), pages 447-451
ISBN: 978-989-758-713-9
Proceedings Copyright © 2024 by SCITEPRESS – Science and Technology Publications, Lda.
447

2 INTRODUCTION TO
REINFORCEMENT LEARNING
THEORY
In the field of machine learning, traditional learning
methods are mainly divided into two types:
supervised learning and unsupervised learning.
Although powerful, these methods have limitations
when dealing with dynamic decision-making
problems in practical applications. In particular, they
cannot actively select training samples or
dynamically adjust strategies based on the results of
previous actions. Unlike these methods,
reinforcement learning offers a distinct learning
paradigm focused on sequential decision-making
problems. It enables learners not only to make choices
but also to adjust their future actions based on the
outcomes of these choices, aiming to maximize long-
term objectives.
2.1 Overview of Reinforcement
Learning
Reinforcement learning is about learning behavior
and decision-making, where an agent needs to make
a series of decisions through interaction with the
environment. In this process, the agent takes actions
and receives feedback from the environment,
typically in the form of rewards, with the goal of
maximizing cumulative rewards. This learning mode
makes reinforcement learning particularly suitable
for applications that require continuous decision-
making, such as games, robot control, and online
recommendation systems(Ke et al., 2023).
In recommendation systems, the interaction
between users and the recommendation system is
inherently sequential. Recommending the best items
is not just a prediction problem but a complex multi-
step decision-making problem. This makes the
recommendation problem very suitable for modeling
through reinforcement learning, where the
recommendation algorithm continuously learns how
to adjust its recommendation strategy based on user
feedback to enhance user satisfaction and overall
system performance. In this context, the
recommendation algorithm acts as a reinforcement
learning agent, with the rest, including users and
items, constituting the environment of the agent. This
is what constitutes a Markov decision process.
2.2 Markov Decision Process
The Markov Decision Process (MDP) is a
fundamental framework of reinforcement learning
and a primary research direction for addressing
sequential decision-making issues. It is mainly used
to solve optimal decision-making problems in
stochastic dynamic systems. Markov properties,
which imply that the next state transition depends
solely on the current state and not on the sequence of
events that preceded it, determine future state
transitions along with current actions and states. The
main elements of a Markov decision process include
the decision period (T), system states (S), action sets
(A), state transition probabilities (P), and reward
functions (R). Each MDP can be represented using
these five elements.
1. Decision Epochs (T): The decision-making
process can be categorized based on the length of
decision epochs into finite and infinite decision
processes. Depending on whether the decisions are
made at discrete intervals or continuously, it can be
further divided into discrete and continuous decision
problems. For discrete finite Markov decision
processes, the decision epoch refers to the time period
between the start and the end of a decision.
2. State Space (S): The set of all possible states at
any decision point within the system, where each state
is characterized by its unique properties.
3. Action Space (A): For any state 𝑠∈𝑆 at any
decision point, the set of all possible actions that can
be taken is defined as the action space.
4. State Transition Probabilities (P): For a given
state and action, the system state 𝑠 transitions to
another state 𝑠

with a certain probability
𝑝



= 𝑝

𝑠

= 𝑠

∣𝑠

= 𝑠, 𝑎

= 𝑎

(1)
The transition probabilities, along with the current
state and action, determine the next state of the
system.
5. Reward Function (R): In an MDP, the reward
function is an expectation indicating the reward
received when taking an action in a particular state.
Additionally, a discount factor is defined to discount
future rewards, which can be described in terms of
costs, profits, or other benefits.
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3 OFFLINE
RECOMMENDATIONS AND
ONLINE LEARNING
3.1 Offline Recommendations
The process of reinforcement learning includes
iterative collection of experience through interaction
with the environment, usually using the latest
learning strategies, and then using these experiences
to improve strategies. However, in the early stages of
development, due to the limitations of computer
power, real-time interaction was somewhat
impractical. Given this context, offline learning has
garnered attention in the field of recommendation
systems. Offline recommendation does not require
interaction with the environment during the learning
process, significantly reducing the computational
power requirements. Offline recommendation
systems use historical offline data from users to learn
the corresponding static recommendation models.
Typically, under a given set of users, items, and a
certain amount of user-item rating matrix, various
methods are used to estimate the unknown ratings in
the rating matrix, and then recommend items with
high ratings to users(Wang, 2021). The ratings are
divided into implicit and explicit ratings. Implicit
ratings are based on user behaviors that reflect their
preferences, such as clicks, bookmarks, browsing
duration, etc. Explicit ratings are much more
straightforward, based on user ratings of items that
indicate their preference level, such as star ratings for
movies, satisfaction ratings for products, etc., usually
presented in a certain numerical range, with higher
numbers indicating greater user preference.
3.2 Online Learning and Real-Time
Recommendations
While offline learning reduces the threshold for
computational power, it often does not achieve ideal
results in highly dynamic fields, such as news and
advertising recommendations. One significant reason
is that offline learning is somewhat powerless against
the cold start problem and cannot promptly track
changes in user preferences. As computer
performance improves and user experience demands
further increase, the need for research into online
learning in the field of recommendation systems has
gradually expanded. Due to the fluidity of user
preferences, recommendation systems must not only
focus on users' current interests but also explore new
content that users might find interesting. This is the
previously mentioned exploration-exploitation
problem. Reinforcement learning adapts well to such
issues, and the multi-armed bandit algorithm, as a
simple reinforcement learning method, utilizes the
sequential interaction characteristics of reinforcement
learning while avoiding the complexity and
computational intensity of other reinforcement
learning algorithms. It is an excellent way to handle
online recommendation issues. Online learning, a
subclass of reinforcement learning, can effectively
overcome the drawbacks of traditional batch learning.
When new data arrives, the model can be updated
immediately, offering timely effectiveness(Liu,
2022). The bandit algorithm belongs to partially
feedback-based online learning.
4 REINFORCEMENT LEARNING
AND THE MULTI-ARMED
BANDIT PROBLEM
The Multi-Armed Bandit (MAB) problem is a classic
issue in reinforcement learning, focusing on how to
make the best decisions among a series of choices to
maximize the total return. This problem originates
from the gambling world, where a gambler faces
multiple slot machines (each representing an "arm"),
each with different payout rates. The gambler's goal
is to maximize the total reward through a series of
lever pulls, deciding which machine to play, how
often to play each machine, in what order, and
whether to continue with the current machine or try
different ones. Addressing this problem involves a
crucial dilemma, previously mentioned as the
exploration-exploitation problem(Sun, 2023). The
decision is whether to pull the arm with the highest
expected return or to explore other machines to gather
more information about potential returns.
In sectors like recommendation systems, medical
treatment allocation, and financial investments, the
MAB problem provides a framework to help
algorithms balance exploration (seeking more
information to improve future decisions) and
exploitation (using current knowledge to maximize
immediate returns). This balance is achieved through
different strategies. One approach widely adopted is
the ε -greedy algorithm, which balances between
exploring new options and exploiting known
preferences. This algorithm initially explores with a
probability ε and selects the best-known option with
a probability of 1-ε. Although simple, the ε-greedy
algorithm can effectively improve recommendation
performance in dynamic environments.
Adaptive Recommendation System Strategies: An Exploration of Online Machine Learning Algorithms
449

Another prominent algorithm is the Upper
Confidence Bound (UCB) method, which calculates
an upper confidence bound for each arm's expected
reward and selects the arm with the highest upper
confidence bound 𝑎

. The formula for the confidence
bound is as follows:
𝑈𝐶𝐵

𝑎


= 𝜇̂

+




(2)
where 𝜇̂

represents the average reward (expected
return) of action
, 𝑛

is the number of times action
has been chosen, and 𝑡 is the current total number
of actions taken. The square root term represents the
exploration factor, which decreases as the number of
times the action is chosen increases, indicating a
preference for actions that have not been explored
frequently. This naturally balances exploration and
exploitation.
Thompson Sampling is a probabilistic algorithm
that adjusts the Beta distribution for each action
𝑎 after each time step, employing the sampled value
𝜃

(𝑡) from that distribution. The Beta distribution
parameters for an action
at time are updated to
Beta
(
𝑆

(𝑡)+1,𝐹

(𝑡)+1
)
(3)
where 𝑆

(𝑡) denotes the number of successes and
𝐹

(
𝑡
)
the number of failures of action
up to time .
This +1 approach to both success and failure counts
ensures a non-zero probability, maintaining a
smoother distribution even in the absence of any trials
or evidence. The algorithm progressively learns and
approaches the true success probability of each action
by updating the Beta distribution parameters of each
action (arm). It effectively handles uncertainty and
adapts to changes in the environment.
5 APPLICATION OF MULTI-
ARMED BANDITS IN
RECOMMENDATION
SYSTEMS
The study of the MAB problem is not limited to
theoretical models but is also extensively applied in
the design and optimization of algorithms in practical
applications. For instance, in online advertising,
MAB algorithms can determine which ad layout is
most likely to attract user clicks, while in
recommendation systems, they help the system
decide when and what content to recommend to a
specific user to maximize user satisfaction and
platform revenue. These algorithms are also used in
dynamic pricing, resource allocation, and other
decision-making areas.
5.1 e-Commerce Platform Product
Recommendation
On e-commerce platforms, as the variety of products
increases and the market environment continuously
evolves, product iteration becomes more frequent.
Although the market has long been dominated by
best-selling items, niche products, also known as non-
bestsellers, are gradually revealing their unique
market value. Although these products do not have
high individual sales, their diverse types can
aggregate into significant volumes, known as the long
tail phenomenon. The long tail effect emphasizes
"personalization," "customer power," and "small
profits but large market(Zhang, 2022)." In the
Internet era, online stores are not limited by physical
space and can store and display a large number of
niche products. Coupled with technologies such as
search engines, consumers can more easily find and
purchase these products that are difficult to obtain
through traditional channels. This market dynamic
poses new challenges for recommendation systems:
how to recommend those few potentially interesting
long-tail products among a vast array of items.
The MAB algorithm can assist recommendation
systems in selecting items to display to users,
achieving a balance between exploring new items and
exploiting popular ones through continuous learning
and adjustment. This helps promote the sale of
popular products while enhancing the exposure and
recommendation of long-tail items, thereby
increasing overall sales revenue(Cesa-Bianchi &
Lugosi, 2012). Zeng et al. designed a Thompson
Sampling-based MAB algorithm, which, combined
with users' historical behavior and item metadata,
significantly improved the effectiveness of
recommendations.
5.2 News Article Recommendation
On news websites, each article can be considered an
arm, with the click-through rate as the reward. For
example, the Yahoo! homepage news
recommendation system uses a Multi-Armed Bandit
(MAB) framework based on the Upper Confidence
Bound (UCB) algorithm. By dynamically adjusting
the display strategy of articles, the system can balance
between the timeliness of news and user interests.
This improvement has increased the overall click-
through rate by 12.5%(Li et al., 2010). This example
EMITI 2024 - International Conference on Engineering Management, Information Technology and Intelligence
450

highlights the effectiveness of MAB algorithms in
responding to user behavior in real time.
5.3 Music/Video Recommendation
On music or video streaming platforms, each song or
video can be considered an arm, with user's play
completion rate or like rate as the reward. By
applying context-aware MAB algorithms, music
recommendation systems can integrate various
contextual factors such as the user's current mood,
activities, and more. This enables more accurate
predictions of the user's music preferences, leading to
the generation of more personalized and dynamic
recommendation lists. Consequently, it enhances user
satisfaction with the platform and increases their
usage duration(Wang et al., 2014).
5.4 Online Advertising Placement
In the online advertising field, each advertisement can
be seen as an arm, with the click-through rate (CTR)
or conversion rate (CVR) as the reward. Research by
Yang (2018) has demonstrated the effectiveness of
Thompson Sampling in predicting the value of
advertising opportunities, thereby aiding advertisers
in increasing overall revenue. This highlights the
efficiency of MAB algorithms in optimizing the
allocation of advertising resources.
6 CONCLUSIONS
This thesis has explored the intricate role of online
machine learning algorithms within the context of
adaptive recommendation systems. Through the
detailed study of reinforcement learning and multi-
armed bandit (MAB) problems, it has been
demonstrated how these techniques significantly
enhance the performance and adaptability of
recommendation systems across various domains,
including e-commerce, news aggregation,
music/video streaming, and online advertising. The
implementation of MAB algorithms has addressed
the critical exploration-exploitation dilemma
effectively. By optimizing the selection process
through algorithms such as Ꜫ-greedy, UCB, and
Thompson Sampling, recommendation systems can
balance between exploring new options and
exploiting known user preferences, thereby
improving user engagement and platform
profitability. The application of these advanced
algorithms has led to a more personalized user
experience, as systems can offer recommendations
that align closely with individual user preferences and
behavioral patterns. A key challenge of the MAB
problem is designing strategies that can quickly adapt
to environmental changes and achieve optimal
performance over the long term. This requires
thorough theoretical analysis and experimental
verification of different strategies to ensure they
perform well in various scenarios. Additionally, since
online recommendation systems heavily rely on user
data to operate, how to adequately protect user
privacy is also a direction that needs further research.
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