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Predicting Session Length in Media Streaming
RISE SICS, Stockholm, Sweden.
Pandora.
Pandora.
Pandora.
2017 (engelsk)Inngår i: SIGIR 2017 - Proceedings of the 40th International ACM SIGIR Conference on Research and Development in Information Retrieval, Association for Computing Machinery (ACM), 2017, s. 977-980Konferansepaper, Publicerat paper (Fagfellevurdert)
Abstract [en]

Session length is a very important aspect in determining a user's satisfaction with a media streaming service. Being able to predict how long a session will last can be of great use for various downstream tasks, such as recommendations and ad scheduling. Most of the related literature on user interaction duration has focused on dwell time for websites, usually in the context of approximating post-click satisfaction either in search results, or display ads.

In this work we present the first analysis of session length in a mobile-focused online service, using a real world data-set from a major music streaming service. We use survival analysis techniques to show that the characteristics of the length distributions can differ significantly between users, and use gradient boosted trees with appropriate objectives to predict the length of a session using only information available at its beginning. Our evaluation on real world data illustrates that our proposed technique outperforms the considered baseline.

sted, utgiver, år, opplag, sider
Association for Computing Machinery (ACM), 2017. s. 977-980
Emneord [en]
User Behavior, Survival Analysis, Dwell Time, Session Length
HSV kategori
Identifikatorer
URN: urn:nbn:se:kth:diva-250037DOI: 10.1145/3077136.3080695ISI: 000454711900127Scopus ID: 2-s2.0-85029395373ISBN: 978-1-4503-5022-8 (tryckt)OAI: oai:DiVA.org:kth-250037DiVA, id: diva2:1307118
Konferanse
40th International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR 2017; Tokyo, Shinjuku; Japan; 7 August 2017 through 11 August 2017
Forskningsfinansiär
Swedish Foundation for Strategic Research , RIT10-0043
Merknad

QC 20190426

Tilgjengelig fra: 2019-04-25 Laget: 2019-04-25 Sist oppdatert: 2022-06-26bibliografisk kontrollert
Inngår i avhandling
1. Scalable Machine Learning through Approximation and Distributed Computing
Åpne denne publikasjonen i ny fane eller vindu >>Scalable Machine Learning through Approximation and Distributed Computing
2019 (engelsk)Doktoravhandling, med artikler (Annet vitenskapelig)
Abstract [en]

Machine learning algorithms are now being deployed in practically all areas of our lives. Part of this success can be attributed to the ability to learn complex representations from massive datasets. However, computational speed increases have not kept up with the increase in the sizes of data we want to learn from, leading naturally to algorithms that need to be resource-efficient and parallel. As the proliferation of machine learning continues, the ability for algorithms to adapt to a changing environment and deal with uncertainty becomes increasingly important.

In this thesis we develop scalable machine learning algorithms, with a focus on efficient, online, and distributed computation. We make use of approximations to dramatically reduce the computational cost of exact algorithms, and develop online learning algorithms to deal with a constantly changing environment under a tight computational budget. We design parallel and distributed algorithms to ensure that our methods can scale to massive datasets.

We first propose a scalable algorithm for graph vertex similarity calculation and concept discovery. We demonstrate its applicability to multiple domains, including text, music, and images, and demonstrate its scalability by training on one of the largest text corpora available. Then, motivated by a real-world use case of predicting the session length in media streaming, we propose improvements to several aspects of learning with decision trees. We propose two algorithms to estimate the uncertainty in the predictions of online random forests. We show that our approach can achieve better accuracy than the state of the art while being an order of magnitude faster. We then propose a parallel and distributed online tree boosting algorithm that maintains the correctness guarantees of serial algorithms while providing an order of magnitude speedup on average. Finally, we propose an algorithm that allows for gradient boosted trees training to be distributed across both the data point and feature dimensions. We show that we can achieve communication savings of several orders of magnitude for sparse datasets, compared to existing approaches that can only distribute the computation across the data point dimension and use dense communication.

sted, utgiver, år, opplag, sider
KTH Royal Institute of Technology, 2019. s. 120
Serie
TRITA-EECS-AVL ; 2019:43
Emneord
Online Learning, Distributed Computing, Graph Similarity, Decision Trees, Gradient Boosting
HSV kategori
Identifikatorer
urn:nbn:se:kth:diva-250038 (URN)978-91-7873-181-7 (ISBN)
Disputas
2019-05-28, Sal B, Kistagången 16, våningsplan 2, Electrum 1, KTH Kista, Kista, 14:00 (engelsk)
Opponent
Veileder
Forskningsfinansiär
Swedish Foundation for Strategic Research , RIT10-0043Swedish Foundation for Strategic Research , BD15-0006
Merknad

QC 20190426

Tilgjengelig fra: 2019-04-26 Laget: 2019-04-25 Sist oppdatert: 2022-12-12bibliografisk kontrollert

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