74 research outputs found
Impressions in Recommender Systems: Present and Future
Impressions are a novel data source providing researchers and practitioners with more details about user interactions and their context. In particular, an impression contain the items shown on screen to users, alongside users' interactions toward such items. In recent years, interest in impressions has thrived, and more papers use impressions in recommender systems. Despite this, the literature does not contain a comprehensive review of the current topics and future directions. This work summarizes impressions in recommender systems under three perspectives: recommendation models, datasets with impressions, and evaluation methodologies. Then, we propose several future directions with an emphasis on novel approaches. This work is part of an ongoing review of impressions in recommender systems
A Change-Detection based Framework for Piecewise-stationary Multi-Armed Bandit Problem
The multi-armed bandit problem has been extensively studied under the
stationary assumption. However in reality, this assumption often does not hold
because the distributions of rewards themselves may change over time. In this
paper, we propose a change-detection (CD) based framework for multi-armed
bandit problems under the piecewise-stationary setting, and study a class of
change-detection based UCB (Upper Confidence Bound) policies, CD-UCB, that
actively detects change points and restarts the UCB indices. We then develop
CUSUM-UCB and PHT-UCB, that belong to the CD-UCB class and use cumulative sum
(CUSUM) and Page-Hinkley Test (PHT) to detect changes. We show that CUSUM-UCB
obtains the best known regret upper bound under mild assumptions. We also
demonstrate the regret reduction of the CD-UCB policies over arbitrary
Bernoulli rewards and Yahoo! datasets of webpage click-through rates.Comment: accepted by AAAI 201
Learning Contextual Bandits in a Non-stationary Environment
Multi-armed bandit algorithms have become a reference solution for handling
the explore/exploit dilemma in recommender systems, and many other important
real-world problems, such as display advertisement. However, such algorithms
usually assume a stationary reward distribution, which hardly holds in practice
as users' preferences are dynamic. This inevitably costs a recommender system
consistent suboptimal performance. In this paper, we consider the situation
where the underlying distribution of reward remains unchanged over (possibly
short) epochs and shifts at unknown time instants. In accordance, we propose a
contextual bandit algorithm that detects possible changes of environment based
on its reward estimation confidence and updates its arm selection strategy
respectively. Rigorous upper regret bound analysis of the proposed algorithm
demonstrates its learning effectiveness in such a non-trivial environment.
Extensive empirical evaluations on both synthetic and real-world datasets for
recommendation confirm its practical utility in a changing environment.Comment: 10 pages, 13 figures, To appear on ACM Special Interest Group on
Information Retrieval (SIGIR) 201
Towards the Use of Clustering Algorithms in Recommender Systems
Recommender Systems have been intensively used in Information Systems in the last decades, facilitating the choice of items individually for each user based on your historical. Clustering techniques have been frequently used in commercial and scientific domains in data mining tasks and visualization tools. However, there is a lack of secondary studies in the literature that analyze the use of clustering algorithms in Recommender Systems and their behavior in different aspects. In this work, we present a Systematic Literature Review (SLR), which discusses the different types of information systems with the use of the clustering algorithm in Recommender Systems, which typically involves three main recommendation approaches found in literature: collaborative filtering, content-based filtering, and hybrid recommendation. In the end, we did a quantitative analysis using K-means clustering for finding patterns between clustering algorithms, recommendation approaches, and some datasets used in their publications
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