Sequence classification applied to user log data An approach to identify characteristics of user sessions in a music streaming service

Visa enkel post
dc.contributor.authorEdström, Sofia
dc.contributor.authorOndrus, Josefin
dc.contributor.departmentChalmers tekniska högskola / Institutionen för data- och informationsteknik (Chalmers)sv
dc.contributor.departmentChalmers University of Technology / Department of Computer Science and Engineering (Chalmers)en
dc.date.accessioned2019-07-03T14:38:36Z
dc.date.available2019-07-03T14:38:36Z
dc.date.issued2017
dc.description.abstractApplying machine learning techniques to sequential user log data can provide insights about users that can guide companies towards making decisions that improve user experience. Recurrent neural networks have been proven to work well in combination with sequential data and recent research suggests that incorporating residual connections in recurrent structures outperforms standard recurrent structures. In this thesis, we show that residual recurrent neural networks can be applied to user log data from a complex domain in order to identify regularities in user behavior. To our knowledge, no research have been conducted with these model structures in domains other than text and image classification. A proof of concept is implemented in collaboration with Spotify where this approach is used to identify how users behave when they save music in the music streaming service. By conducting experiments with different models, we show that models with increased input complexity slightly outperform models with lower input complexity in the artificial classification task defined in this thesis. We also show that results from a more complex model can be analyzed and provide valuable insights. However, we conclude that the approach is ineffective and needs more developement in order to become sufficient.
dc.identifier.urihttps://hdl.handle.net/20.500.12380/252497
dc.language.isoeng
dc.setspec.uppsokTechnology
dc.subjectData- och informationsvetenskap
dc.subjectComputer and Information Science
dc.titleSequence classification applied to user log data An approach to identify characteristics of user sessions in a music streaming service
dc.type.degreeExamensarbete för masterexamensv
dc.type.degreeMaster Thesisen
dc.type.uppsokH
local.programmeComputer science – algorithms, languages and logic (MPALG), MSc
Ladda ner
Original bundle
Visar 1 - 1 av 1
Bild (thumbnail)
Namn:
252497.pdf
Storlek:
1.07 MB
Format:
Adobe Portable Document Format
Beskrivning:
Fulltext
Ladda ner
Samling
Examensarbeten för masterexamen