The Impacts of Item Features and User Characteristics on Users' Perceived Serendipity of Recommendations

Ningxia Wang; Li Chen; Yonghua Yang

doi:10.1145/3340631.3394863

The Impacts of Item Features and User Characteristics on Users' Perceived Serendipity of Recommendations

Ningxia Wang, Li Chen, Yonghua Yang

Department of Computer Science

Research output: Chapter in book/report/conference proceeding › Conference proceeding › peer-review

15 Citations (Scopus)

Abstract

Serendipity-oriented recommender systems have increasingly been recognized as useful to overcome the "filter bubble" problem of accuracy-oriented recommenders, by recommending unexpected and relevant items to users. However, most of existing systems are based on researchers' assumptions about the effect of item features on serendipity, but less from users' perspective to study what item features and even user characteristics might affect their perceived serendipity. In this paper, we have attempted to fill in this vacancy based on results of a large-scale user survey (involving over 10,000 users). We have analyzed the correlation between different types of features (i.e., numerical and categorical) with user perceptions, and furthermore identified the interaction effect from user characteristics (such as personality traits and curiosity). We finally discuss the implications of our work to augment the effectiveness of current serendipity-oriented recommender systems.

Original language	English
Title of host publication	UMAP '20: Proceedings of the 28th ACM Conference on User Modeling, Adaptation and Personalization
Publisher	Association for Computing Machinery (ACM)
Pages	266-274
Number of pages	9
ISBN (Print)	9781450368612
DOIs	https://doi.org/10.1145/3340631.3394863
Publication status	Published - Jul 2020
Event	28th ACM International Conference on User Modeling, Adaptation, and Personalization, UMAP 2020 - Genoa, Italy Duration: 14 Jul 2020 → 17 Jul 2020 https://dl.acm.org/doi/proceedings/10.1145/3340631 (Conference proceedings) https://dl.acm.org/doi/proceedings/10.1145/3386392 (Adjunct publication)

Publication series

Name	Proceedings of ACM Conference on User Modeling, Adaptation and Personalization

Conference

Conference	28th ACM International Conference on User Modeling, Adaptation, and Personalization, UMAP 2020
Country/Territory	Italy
City	Genoa
Period	14/07/20 → 17/07/20
Internet address	https://dl.acm.org/doi/proceedings/10.1145/3340631 (Conference proceedings) https://dl.acm.org/doi/proceedings/10.1145/3386392 (Adjunct publication)

User-Defined Keywords

curiosity
item features
recommender systems
serendipity
user personality
user survey

Access to Document

10.1145/3340631.3394863

Cite this

Wang, N., Chen, L., & Yang, Y. (2020). The Impacts of Item Features and User Characteristics on Users' Perceived Serendipity of Recommendations. In UMAP '20: Proceedings of the 28th ACM Conference on User Modeling, Adaptation and Personalization (pp. 266-274). (Proceedings of ACM Conference on User Modeling, Adaptation and Personalization). Association for Computing Machinery (ACM). https://doi.org/10.1145/3340631.3394863

Wang, Ningxia ; Chen, Li ; Yang, Yonghua. / The Impacts of Item Features and User Characteristics on Users' Perceived Serendipity of Recommendations. UMAP '20: Proceedings of the 28th ACM Conference on User Modeling, Adaptation and Personalization. Association for Computing Machinery (ACM), 2020. pp. 266-274 (Proceedings of ACM Conference on User Modeling, Adaptation and Personalization).

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title = "The Impacts of Item Features and User Characteristics on Users' Perceived Serendipity of Recommendations",

abstract = "Serendipity-oriented recommender systems have increasingly been recognized as useful to overcome the {"}filter bubble{"} problem of accuracy-oriented recommenders, by recommending unexpected and relevant items to users. However, most of existing systems are based on researchers' assumptions about the effect of item features on serendipity, but less from users' perspective to study what item features and even user characteristics might affect their perceived serendipity. In this paper, we have attempted to fill in this vacancy based on results of a large-scale user survey (involving over 10,000 users). We have analyzed the correlation between different types of features (i.e., numerical and categorical) with user perceptions, and furthermore identified the interaction effect from user characteristics (such as personality traits and curiosity). We finally discuss the implications of our work to augment the effectiveness of current serendipity-oriented recommender systems.",

keywords = "curiosity, item features, recommender systems, serendipity, user personality, user survey",

author = "Ningxia Wang and Li Chen and Yonghua Yang",

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Wang, N, Chen, L & Yang, Y 2020, The Impacts of Item Features and User Characteristics on Users' Perceived Serendipity of Recommendations. in UMAP '20: Proceedings of the 28th ACM Conference on User Modeling, Adaptation and Personalization. Proceedings of ACM Conference on User Modeling, Adaptation and Personalization, Association for Computing Machinery (ACM), pp. 266-274, 28th ACM International Conference on User Modeling, Adaptation, and Personalization, UMAP 2020, Genoa, Italy, 14/07/20. https://doi.org/10.1145/3340631.3394863

The Impacts of Item Features and User Characteristics on Users' Perceived Serendipity of Recommendations. / Wang, Ningxia; Chen, Li; Yang, Yonghua.
UMAP '20: Proceedings of the 28th ACM Conference on User Modeling, Adaptation and Personalization. Association for Computing Machinery (ACM), 2020. p. 266-274 (Proceedings of ACM Conference on User Modeling, Adaptation and Personalization).

Research output: Chapter in book/report/conference proceeding › Conference proceeding › peer-review

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AB - Serendipity-oriented recommender systems have increasingly been recognized as useful to overcome the "filter bubble" problem of accuracy-oriented recommenders, by recommending unexpected and relevant items to users. However, most of existing systems are based on researchers' assumptions about the effect of item features on serendipity, but less from users' perspective to study what item features and even user characteristics might affect their perceived serendipity. In this paper, we have attempted to fill in this vacancy based on results of a large-scale user survey (involving over 10,000 users). We have analyzed the correlation between different types of features (i.e., numerical and categorical) with user perceptions, and furthermore identified the interaction effect from user characteristics (such as personality traits and curiosity). We finally discuss the implications of our work to augment the effectiveness of current serendipity-oriented recommender systems.

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Wang N, Chen L, Yang Y. The Impacts of Item Features and User Characteristics on Users' Perceived Serendipity of Recommendations. In UMAP '20: Proceedings of the 28th ACM Conference on User Modeling, Adaptation and Personalization. Association for Computing Machinery (ACM). 2020. p. 266-274. (Proceedings of ACM Conference on User Modeling, Adaptation and Personalization). doi: 10.1145/3340631.3394863

The Impacts of Item Features and User Characteristics on Users' Perceived Serendipity of Recommendations

Abstract

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User-Defined Keywords

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