Can a Recommender System Induce Serendipitous Encounters?

Iaquinta, Leo; Gemmis, Marco de; Lops, Pasquale; Semeraro, Giovanni; Molino, Piero

doi:10.5772/8905

Cited by 30 publications

(29 citation statements)

References 14 publications

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“…Other settings either let an algorithm rank all the items in the system or a limited number of them assuming that items unknown by a user are irrelevant. This assumption is not suitable for evaluation of serendipity, as serendipitous items are novel by definition (Iaquinta et al, 2010;Adamopoulos and Tuzhilin, 2014;Kotkov et al, 2016a). The experiments were conducted using Lenskit framework (Ekstrand et al, 2011).…”

Section: Methodsmentioning

confidence: 99%

A Serendipity-Oriented Greedy Algorithm for Recommendations

Kotkov

Veijalainen

Wang

2017

Proceedings of the 13th International Conference on Web Information Systems and Technologies

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Abstract: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 find anyway, leading to low satisfaction. To overcome this problem, a recommender system should suggest novel, relevant and unexpected, i.e. serendipitous items. In this paper, we propose a serendipity-oriented algorithm, which improves serendipity through feature diversification and helps overcome the overspecialization problem. To evaluate our algorithm and compare it with others, we employ a serendipity metric that captures each component of serendipity, unlike the most common metric.

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Section: Methodsmentioning

confidence: 99%

A Serendipity-Oriented Greedy Algorithm for Recommendations

Kotkov

Veijalainen

Wang

2017

Proceedings of the 13th International Conference on Web Information Systems and Technologies

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“…A similar definition was employed by Iaquinta et al According to (Iaquinta et al, 2010), serendipitous items are interesting, unexpected and novel to a user:…”

Section: Definitionmentioning

confidence: 99%

“…It is challenging to define what serendipity is in recommender systems, what kind of items are serendipitous and why, since serendipity is a complex concept (Maksai et al, 2015;Iaquinta et al, 2010).…”

Section: Definitionmentioning

confidence: 99%

“…Currently, there is no consensus on definition of serendipity in recommender systems (Maksai et al, 2015;Iaquinta et al, 2010). It is difficult to investigate serendipity, as the concept includes an emotional dimension (Foster and Ford, 2003) and serendipitous encounters are very rare (André et al, 2009).…”

Section: Introductionmentioning

confidence: 99%

“…It is difficult to investigate serendipity, as the concept includes an emotional dimension (Foster and Ford, 2003) and serendipitous encounters are very rare (André et al, 2009). As different definitions of serendipity have been proposed (Maksai et al, 2015;Iaquinta et al, 2010), it is not clear how to measure serendipity in recommender systems (Murakami et al, 2008;Zhang et al, 2012).…”

Section: Introductionmentioning

confidence: 99%

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Challenges of Serendipity in Recommender Systems

Kotkov

Veijalainen

Wang

2016

Proceedings of the 12th International Conference on Web Information Systems and Technologies

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Abstract: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 serendipitous items, which is a challenging task, as it is unclear what makes items serendipitous to a user and how to measure serendipity. The concept is difficult to investigate, as serendipity includes an emotional dimension and serendipitous encounters are very rare. In this paper, we discuss mentioned challenges, review definitions of serendipity and serendipity-oriented evaluation metrics. The goal of the paper is to guide and inspire future efforts on serendipity in recommender systems.

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Serendipitous Recommendations Through Ontology-Based Contextual Pre-filtering

Karpus

Vagliano

Goczyła

2017

Beyond Databases, Architectures and Structures. Towards Efficient Solutions for Data Analysis and Knowledge Representation

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Context-aware Recommender Systems aim to provide users with better recommendations for their current situation. Although evaluations of recommender systems often focus on accuracy, it is not the only important aspect. Often recommendations are overspecialized, i.e. all of the same kind. To deal with this problem, other properties can be considered, such as serendipity. In this paper, we study how an ontology-based and context-aware pre-filtering technique which can be combined with existing recommendation algorithm performs in ranking tasks. We also investigate the impact of our method on the serendipity of the recommendations. We evaluated our approach through an offline study which showed that when used with well-known recommendation algorithms it can improve the accuracy and serendipity.

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Can a Recommender System Induce Serendipitous Encounters?

Cited by 30 publications

References 14 publications

A Serendipity-Oriented Greedy Algorithm for Recommendations

A Serendipity-Oriented Greedy Algorithm for Recommendations

Challenges of Serendipity in Recommender Systems

Serendipitous Recommendations Through Ontology-Based Contextual Pre-filtering

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