Improving Serendipity and Accuracy in Cross-Domain Recommender Systems
Kotkov, D., Wang, S., & Veijalainen, J. (2017). Improving Serendipity and Accuracy in Cross-Domain Recommender Systems. In V. Monfort, K.-H. Krempels, T. A. Majchrzak, & P. Traverso (Eds.), WEBIST 2016 : the 12th International conference on web information systems and technologies. Revised Selected Papers (pp. 105-119). Springer International Publishing AG. Lecture Notes in Business Information Processing, 292. https://doi.org/10.1007/978-3-319-66468-2_6
Julkaistu sarjassa
Lecture Notes in Business Information ProcessingPäivämäärä
2017Tekijänoikeudet
© Springer International Publishing AG 2017. This is a final draft version of an article whose final and definitive form has been published by Springer. Published in this repository with the kind permission of the publisher.
Cross-domain recommender systems use information from source domains
to improve recommendations in a target domain, where the term domain
refers to a set of items that share attributes and/or user ratings. Most works on
this topic focus on accuracy but disregard other properties of recommender systems.
In this paper, we attempt to improve serendipity and accuracy in the target
domain with datasets from source domains. Due to the lack of publicly available
datasets, we collect datasets from two domains related to music, involving
user ratings and item attributes. We then conduct experiments using collaborative
filtering and content-based filtering approaches for the purpose of validation. According
to our results, the source domain can improve serendipity in the target
domain for both approaches. The source domain decreases accuracy for contentbased
filtering and increases accuracy for collaborative filtering. The improvement
of accuracy decreases with the growth of non-overlapping items in different
domains.
...
Julkaisija
Springer International Publishing AGEmojulkaisun ISBN
978-3-319-66467-5Konferenssi
International conference on web information systems and technologiesKuuluu julkaisuun
WEBIST 2016 : the 12th International conference on web information systems and technologies. Revised Selected PapersISSN Hae Julkaisufoorumista
1865-1348Asiasanat
Julkaisu tutkimustietojärjestelmässä
https://converis.jyu.fi/converis/portal/detail/Publication/27260260
Metadata
Näytä kaikki kuvailutiedotKokoelmat
Rahoittaja(t)
Suomen AkatemiaRahoitusohjelmat(t)
Akatemiahanke, SALisätietoja rahoituksesta
The research at the University of Jyväskylä was performed in the MineSocMed project, partially supported by the Academy of Finland, grant #268078. The communication of this research was supported by Daria Wadsworth.Samankaltainen aineisto
Näytetään aineistoja, joilla on samankaltainen nimeke tai asiasanat.
-
Cross-Domain Recommendations with Overlapping Items
Kotkov, Denis; Wang, Shuaiqiang; Veijalainen, Jari (SCITEPRESS, 2016)In recent years, there has been an increasing interest in cross-domain recommender systems. However, most existing works focus on the situation when only users or users and items overlap in different domains. In this ... -
Comparing ranking-based collaborative filtering algorithms to a rating-based alternative in recommender systems context
Koskela, Pentti (2017)Suuri sisältövalikoima eri internet palveluissa, kuten verkkokaupoissa, voi aiheuttaa liian suurta informaatiomäärää, mikä heikentää asiakaskokemusta. Suosittelujärjestelmät ovat teknologioita, jotka tukevat asiakkaan ... -
Serendipity in recommender systems
Kotkov, Denis (University of Jyväskylä, 2018)The number of goods and services (such as accommodation or music streaming) offered by e-commerce websites does not allow users to examine all the available options in a reasonable amount of time. Recommender systems are ... -
How does serendipity affect diversity in recommender systems? A serendipity-oriented greedy algorithm
Kotkov, Denis; Veijalainen, Jari; Wang, Shuaiqiang (Springer Wien, 2020)Most recommender systems suggest items that are popular among all users and similar to items a user usually consumes. As a result, the user receives recommendations that she/he is already familiar with or would find anyway, ... -
Challenges of Serendipity in Recommender Systems
Kotkov, Denis; Veijalainen, Jari; Wang, Shuaiqiang (SCITEPRESS, 2016)Most recommender systems suggest items similar to a user profile, which results in boring recommendations limited by user preferences indicated in the system. To overcome this problem, recommender systems should suggest ...
Ellei toisin mainittu, julkisesti saatavilla olevia JYX-metatietoja (poislukien tiivistelmät) saa vapaasti uudelleenkäyttää CC0-lisenssillä.