A survey of serendipity in recommender systems

Kotkov, Denis; Wang, Shuaiqiang; Veijalainen, Jari

doi:10.1016/j.knosys.2016.08.014

Cited by 240 publications

(188 citation statements)

References 19 publications

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“…Some researchers consider novel and unexpected items serendipitous (Zhang et al, 2012), while others suggest that serendipitous items are relevant and unexpected (Maksai et al, 2015). Although there is no agreement on the definition of serendipity (Kotkov et al, 2016b), in this paper, the term serendipitous items refers to items relevant, novel and unexpected to a user (Kotkov et al, 2016a;Kotkov et al, 2016b):…”

Section: Introductionmentioning

confidence: 92%

“…Recommender systems are software tools that suggest items of use to users (Ricci et al, 2011;Kotkov et al, 2016a). An item is "a piece of information that refers to a tangible or digital object, such as a good, a service or a process that a recommender system suggests to the user in an interaction through the Web, email or text message" (Kotkov et al, 2016a).…”

Section: Introductionmentioning

confidence: 99%

“…An item is "a piece of information that refers to a tangible or digital object, such as a good, a service or a process that a recommender system suggests to the user in an interaction through the Web, email or text message" (Kotkov et al, 2016a). For example, an item could refer to a movie, a song or a new friend.…”

Section: Introductionmentioning

confidence: 99%

“…To increase the number of items that will receive high ratings most recommender systems tend to suggest items that are (1) popular, as these items are consumed by many individuals and often of high quality in many domains (Celma Herrada, 2009) and (2) similar to which the user has assigned high ratings, as these items correspond to user's preferences (Tacchini, 2012;Kotkov et al, 2016a;Kotkov et al, 2016b). As a result, users might become bored with the suggestions provided, as (1) users are likely to be familiar with popular items, while the main reason these users would use a recommender system is to find novel and relevant items (Celma Herrada, 2009) and (2) users often lose interest in using the system * The research was conducted while the author was working for the University of Jyvaskyla, Finland when they are offered only items similar to highly rated ones from their profiles (the so-called overspecialization problem) (Tacchini, 2012;Kotkov et al, 2016a;Kotkov et al, 2016b).…”

Section: Introductionmentioning

confidence: 99%

“…As a result, users might become bored with the suggestions provided, as (1) users are likely to be familiar with popular items, while the main reason these users would use a recommender system is to find novel and relevant items (Celma Herrada, 2009) and (2) users often lose interest in using the system * The research was conducted while the author was working for the University of Jyvaskyla, Finland when they are offered only items similar to highly rated ones from their profiles (the so-called overspecialization problem) (Tacchini, 2012;Kotkov et al, 2016a;Kotkov et al, 2016b). Here the term user profile refers to the set of items rated by the target user, though it might include information, such as name, ID and age in other papers.…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

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: Introductionmentioning

confidence: 92%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

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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Serendipity in the city: User evaluations of urban recommender systems

Smets

Vannieuwenhuyze

Ballon

2021

Asso for Info Science & Tech

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The contemporary city is increasingly being labeled as a smart city consisting of both physical and virtual spaces. This digital augmentation of urban life sets the scene for urban recommender systems to help citizens dealing with the abundance of digital information and corresponding choice overload, for example, by recommending the best place to have dinner based on your personal profile. There are, however, concerns that this kind of algorithmic filtering could lead to homogenization of urban experiences and a decline of social cohesion among citizens. To overcome this issue, scholars increasingly encourage the introduction of serendipity in all types of recommender systems. Nonetheless, it remains unclear how this can be achieved in practice. In this work, we study user evaluations of serendipity in urban recommender systems through a survey among 1641 citizens. More specifically, we study which characteristics of recommended items contribute to serendipitous experiences and to what extent this increases user satisfaction and conversion. Our results align with findings in other application domains in the sense that there is a strong relation between the relevance and novelty of recommendations and the corresponding experienced serendipity. Moreover, serendipitous recommendations are found to increase the chance of users following up on these recommendations.

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Developing smart city services using intent‐aware recommendation systems: A survey

Rafique

Hafid

Qadir

2023

Trans Emerging Tel Tech

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Smart cities could be defined as urban areas that use Information and Communication Technology (ICT) to solve city problems in efficient and sustainable ways. Intent-aware Recommender Systems (IARS) within ICT play a crucial role in filtering useless information according to user demands and assist in decision-making in various smart city platforms. In smart cities, the user traces on IoT, RFIDs, mobiles, and smart sensors capture actual user intent of performing an activity and enhance user satisfaction by proposing optimal services. This paper presents a detailed literature survey of the field of IARS and how it can be used for developing smart city services. First, we present the evolution of IARS with the development of computing technology. Then, we present case studies, synergies, advances, and a reference implementation architecture of IARS for smart cities. We discuss requirements for developing smart city services using IARS. Furthermore, we devise a comprehensive taxonomy of applications and techniques of IARS using different performance parameters. Finally, we elaborate on current issues, challenges, and future research directions in IARS; these directions we believe will pave the way for autonomous service provisioning in smart cities.

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A survey of serendipity in recommender systems

Cited by 240 publications

References 19 publications

A Serendipity-Oriented Greedy Algorithm for Recommendations

A Serendipity-Oriented Greedy Algorithm for Recommendations

Serendipity in the city: User evaluations of urban recommender systems

Developing smart city services using intent‐aware recommendation systems: A survey

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