Constructing & Evaluating Context-Aware Recommender System in a case study with webshop carts and AB-testing

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dc.contributor.authorLundgren, Alexander
dc.contributor.authorLindberg, Linus
dc.contributor.departmentChalmers tekniska högskola / Institutionen för data- och informationsteknik, Datavetenskap (Chalmers)sv
dc.contributor.departmentChalmers University of Technology / Department of Computer Science and Engineering, Computing Science (Chalmers)en
dc.date.accessioned2019-07-03T13:36:08Z
dc.date.available2019-07-03T13:36:08Z
dc.date.issued2014
dc.description.abstractA potential customer spends around five minutes on a webshop and visits roughly ten pages. To maximise sales it is crucial that the most relevant products is presented within those limits. The solution is to use a recommender system that predicts and recommends items on a personal level to the customer using collaborative filtering. Therefore, the project aim was to construct a working prototype for 3Bits and Lindex with an AB-testing phase to validate performance. Such evaluation of the collaborative filtering paradigm is not very common and as it only focus on on-going carts with binary implicit feedback, it does not take other information sources into account. To thoroughly evaluate the performance in this particular configuration five testphases was used: experimental prototype cross-validation tests, prototype testing using historical data sets, prototype testing using contextual pre-filtering, prototype crossvalidation of synthetic generated data and finally AB-testing. A lot of literature and data was studied in order to be able to construct the evaluation tests. Furthermore, to enable AB-testing 3Bits had to integrate the prototype into Lindex web page in a way that maintained the professional level. The evaluation shows a high accuracy compared to recommendations based on the most frequent occurring items. Furthermore, results from AB-tests of the projectalgorithm against the old recommendation services at Lindex showed contradicting results. Additionally, the evaluation also showed that incorporating contextual pre-filtering to the prototype did not increase performance.
dc.identifier.urihttps://hdl.handle.net/20.500.12380/212556
dc.language.isoeng
dc.setspec.uppsokTechnology
dc.subjectData- och informationsvetenskap
dc.subjectInformations- och kommunikationsteknik
dc.subjectComputer and Information Science
dc.subjectInformation & Communication Technology
dc.titleConstructing & Evaluating Context-Aware Recommender System in a case study with webshop carts and AB-testing
dc.type.degreeExamensarbete för masterexamensv
dc.type.degreeMaster Thesisen
dc.type.uppsokH
local.programmeComputer science – algorithms, languages and logic (MPALG), MSc
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