Design and implementation of a recommender system for online vocabulary learning

 |  Login

Show simple item record

dc.contributor Aalto-yliopisto fi
dc.contributor Aalto University en
dc.contributor.advisor Laaksonen, Jorma
dc.contributor.advisor Stén, Liisa
dc.contributor.author Forti, Filippo
dc.date.accessioned 2016-11-02T09:45:15Z
dc.date.available 2016-11-02T09:45:15Z
dc.date.issued 2016-10-27
dc.identifier.uri https://aaltodoc.aalto.fi/handle/123456789/23363
dc.description.abstract Recommender systems are used to select, within a wide catalog, a limited number of products (such as films, books, or general items) which are presented to the user based on their preferences. This master's thesis work has been carried out in the company Kielikone. The company owns an online learning service called Sanakirja.fi, which helps in studying and teaching foreign languages through the use of wordlists. Wordlists are collections of terms, and the users can play with them in order to memorize words. The goal of this thesis is the analysis and development of recommendation algorithms adapted to infer the users' taste and identify categories for each wordlists in order to achieve targeted recommendation. There exist various types of recommendation systems, which differ mainly in the way how the recommendations for the users are produced. In this master's thesis work we focus on the integration of three techniques, namely, collaborative filtering, content-based recommendations and the combination of both, in a hybrid approach. We show that by creating a text classifier we are able to categorize wordlists based on the topic of the words contained in them. Based on the literature, we implemented also a collaborative filtering algorithm, which is able to obtain accurate recommendation based on the users' implicit ratings. In the end, we combined these two approaches to obtain wordlist recommendations, which are related to the topics the user has expressed his interest in, and having at the same time a good number of diversified and serendipitous results. en
dc.format.extent 72
dc.language.iso en en
dc.title Design and implementation of a recommender system for online vocabulary learning en
dc.type G2 Pro gradu, diplomityö fi
dc.contributor.school Perustieteiden korkeakoulu fi
dc.subject.keyword recommendation system en
dc.subject.keyword collaborative filtering en
dc.subject.keyword matrix factorization en
dc.subject.keyword content-based recommendation en
dc.subject.keyword word learning en
dc.identifier.urn URN:NBN:fi:aalto-201611025464
dc.programme.major Human-Computer Interaction and Design fi
dc.programme.mcode SCI3020 fi
dc.type.ontasot Master's thesis en
dc.type.ontasot Diplomityö fi
dc.contributor.supervisor Kaski, Samuel
dc.programme Master's Programme in ICT Innovation fi


Files in this item

Files Size Format View

There are no files associated with this item.

This item appears in the following Collection(s)

Show simple item record

Search archive


Advanced Search

article-iconSubmit a publication

Browse

My Account