Serendipity in recommender systems
Julkaistu sarjassa
Jyväskylä studies in computingTekijät
Päivämäärä
2018Oppiaine
TietojenkäsittelytiedeThe 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 auxiliary
systems designed to help users find interesting goods or services (items) on a
website when the number of available items is overwhelming. Traditionally, recommender systems have been optimized for accuracy, which indicates how often
a user consumed the items recommended by system. To increase accuracy, recommender systems often suggest items that are popular and suitably similar to
items these users have consumed in the past. As a result, users often lose interest in using these systems, as they either know about the recommended items
already or can easily find these items themselves. One way to increase user satisfaction and user retention is to suggest serendipitous items. These items are items
that users would not find themselves or even look for, but would enjoy consuming. Serendipity in recommender systems has not been thoroughly investigated.
There is not even a consensus on the concept’s definition. In this dissertation,
serendipitous items are defined as relevant, novel and unexpected to a user.
In this dissertation, we (a) review different definitions of the concept and
evaluate them in a user study, (b) assess the proportion of serendipitous items in a
typical recommender system, (c) review ways to measure and improve serendipity, (d) investigate serendipity in cross-domain recommender systems (systems
that take advantage of multiple domains, such as movies, songs and books) and
(e) discuss challenges and future directions concerning this topic.
We applied a Design Science methodology as the framework for this study
and developed four artifacts: (1) a collection of eight variations of serendipity
definition, (2) a measure of the serendipity of suggested items, (3) an algorithm
that generates serendipitous suggestions, (4) a dataset of user feedback regarding
serendipitous movies in the recommender system MovieLens. These artifacts are
evaluated using suitable methods and communicated through publications.
...
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Julkaisija
University of JyväskyläISBN
978-951-39-7438-1ISSN Hae Julkaisufoorumista
1456-5390Asiasanat
serendipisyys recommender systems serendipity relevance novelty unexpectedness personalization evaluation recommendation algorithms evaluation metrics offline experiments user study serendipity metrics Käyttäjätutkimus suosittelujärjestelmät verkkopalvelut verkkokauppa täsmämarkkinointi algoritmit arviointi sattuma relevanssi
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Samankaltainen aineisto
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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 ... -
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, ... -
A Serendipity-Oriented Greedy Algorithm for Recommendations
Kotkov, Denis; Veijalainen, Jari; Wang, Shuaiqiang (SCITEPRESS Science And Technology Publications, 2017)Most recommender systems suggest items to a user that are popular among all users and similar to items the user usually consumes. As a result, a user receives recommendations that she/he is already familiar with or would ... -
Recommending Serendipitous Items using Transfer Learning
Pandey, Gaurav; Kotkov, Denis; Semenov, Alexander (ACM Press, 2018)Most recommender algorithms are designed to suggest relevant items, but suggesting these items does not always result in user satisfaction. Therefore, the efforts in recommender systems recently shifted towards serendipity, ... -
Improving Serendipity and Accuracy in Cross-Domain Recommender Systems
Kotkov, Denis; Wang, Shuaiqiang; Veijalainen, Jari (Springer International Publishing AG, 2017)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 ...
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