Temporal diversity in recommender systems

Lathia, Neal; Hailes, Stephen; Capra, Licia; Amatriain, Xavier

doi:10.1145/1835449.1835486

Cited by 222 publications

(117 citation statements)

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“…However, more recent studies (Zhang & Hurley, 2008;Zhou et al, 2010) have observed that this type of approach might lead to a situation where the recommendations offered are 'monothematic' and repetitive and therefore end up being boring to the user. As a way to mitigate this effect and enhance user satisfaction, recent approaches to recommender systems have started investigating and incorporating diversity into their design (Lathia, Hailes, Capra, & Amatriain, 2010;Ozturk & Han, 2014;Vargas & Castells, 2011).…”

Section: Exposure Diversity Through Software Designmentioning

confidence: 99%

“…In other words, in discovery services, such as news media and social media, providing diverse choices (instead of merely personalized ones) could be considered an element of quality; a route to intensified engagement with the user and even a selling point. In a similar sense, Zhang and Hurley (2008) and Lathia et al (2010) have argued that the lack of variation can also have a frustrating effect on users, while Schönbach (2007) argued that people actually buy news media because they are interested in what he calls 'reliable surprises'.…”

Section: Exposure Diversity Through Software Designmentioning

confidence: 99%

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Exposure diversity as a design principle for recommender systems

Helberger

Karppinen

D'Acunto

2016

Information, Communication & Society

203

137

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Exposure diversity as a design principle for recommender systemsHelberger, N.; Karpinnen, K.; D'Acunto, L. General rightsIt is not permitted to download or to forward/distribute the text or part of it without the consent of the author(s) and/or copyright holder(s), other than for strictly personal, individual use, unless the work is under an open content license (like Creative Commons). Disclaimer/Complaints regulationsIf you believe that digital publication of certain material infringes any of your rights or (privacy) interests, please let the Library know, stating your reasons. In case of a legitimate complaint, the Library will make the material inaccessible and/or remove it from the website. Please Ask the Library: http://uba.uva.nl/en/contact, or a letter to: Library of the University of Amsterdam, Secretariat, Singel 425, 1012 WP Amsterdam, The Netherlands. You will be contacted as soon as possible. ABSTRACTPersonalized recommendations in search engines, social media and also in more traditional media increasingly raise concerns over potentially negative consequences for diversity and the quality of public discourse. The algorithmic filtering and adaption of online content to personal preferences and interests is often associated with a decrease in the diversity of information to which users are exposed. Notwithstanding the question of whether these claims are correct or not, this article discusses whether and how recommendations can also be designed to stimulate more diverse exposure to information and to break potential 'filter bubbles' rather than create them. Combining insights from democratic theory, computer science and law, the article makes suggestions for design principles and explores the potential and possible limits of 'diversity sensitive design'. IntroductionRecommendation systems increasingly influence our information choices: the information that is ultimately being presented to us has been filtered through the lens of our personal preferences, our previous choices and the preferences of our friends. There are also commercial and strategic decisions behind the algorithms that determine which information we will see, which information is prioritized and which information is excluded (Bozdag, 2013;Foster, 2012;Schulz, Dreyer, & Hagemeier, 2012;Webster, 2010). Search engines and social networks, in particular, increasingly rely on recommender systems, which are a class of information filtering systems that study patterns of user behaviour to determine what someone will prefer from among a collection of 'information'. By doing so, recommender systems essentially personalize the list of content that is offered to a user. With the emerging trends of higher interactivity and user orientation, the use of recommender systems is not limited to search engines and social networks, as media organizations are also increasingly incorporating recommenders into their own services.The impact of personalized recommendations on the realization of media and information diversity is currently a central questi...

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Section: Exposure Diversity Through Software Designmentioning

confidence: 99%

Section: Exposure Diversity Through Software Designmentioning

confidence: 99%

Exposure diversity as a design principle for recommender systems

Helberger

Karppinen

D'Acunto

2016

Information, Communication & Society

203

137

View full text Add to dashboard Cite

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“…The advantage of focusing on perceived diversity is that we can directly capture the users' opinions. Lathia et al (2010) found that perceived diversity is positively related to user satisfaction in the long term when using a recommender system. Regarding the importance of perceived diversity, this paper will analyse how end users perceive the diversityincreasing items in recommendation lists.…”

Section: Introductionmentioning

confidence: 97%

Effects of the Placement of Diverse Items in Recommendation Lists

Jannach

Gedikli

et al. 2012

Proceedings of the 14th International Conference on Enterprise Information Systems

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Abstract:Over the last fifteen years, a large amount of research in recommender systems was devoted to the development of algorithms that focus on improving the accuracy of recommendations. More recently, it has been proposed that accuracy is not the only factor that contributes to the quality of recommender systems. Among others, the diversity of recommendation lists has been considered as one of the most important factors. Therefore a number of algorithms were proposed to generate the recommendation list with diverse items. However, limited research has been done regarding how to position those diverse items in the list. In this paper we therefore investigate how to organize the diverse items to achieve a higher perceived recommendation quality. The results of an experimental study show that the perceived diversity of a recommendation list depends on the placement of diverse items. Placing the diverse items dispersedly or together at the bottom of the list can increase the perceived diversity. In addition, we found that in the movie domain including diverse items in the recommendation list does not hurt user satisfaction, which means that recommender systems providers have some flexibility to add some extra items to the lists, for example to increase the serendipity of the recommendations.

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“…As an example, it is preferable that both sci-fi and romance movies be included in the RecList if the target previously watched movies from the sci-fi and romance genres. For an interpretation from another perspective, Lathia et al [40] introduces temporal diversity, and suggests that recommended items for the same target be changed as time goes by. Novelty: Novelty refers to the degree of discovery difficulty of an item recommended to a target.…”

Section: Accuracymentioning

confidence: 99%

A Survey on Recommendation Methods Beyond Accuracy

Han

Yamana

2017

IEICE Trans. Inf. & Syst.

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SUMMARYIn recommending to another individual an item that one loves, accuracy is important, however in most cases, focusing only on accuracy generates less satisfactory recommendations. Studies have repeatedly pointed out that aspects that go beyond accuracy-such as the diversity and novelty of the recommended items-are as important as accuracy in making a satisfactory recommendation. Despite their importance, there is no global consensus about definitions and evaluations regarding beyondaccuracy aspects, as such aspects closely relate to the subjective sensibility of user satisfaction. In addition, devising algorithms for this purpose is difficult, because algorithms concurrently pursue the aspects in trade-off relation (i.e., accuracy vs. novelty). In the aforementioned situation, for researchers initiating a study in this domain, it is important to obtain a systematically integrated view of the domain. This paper reports the results of a survey of about 70 studies published over the last 15 years, each of which addresses recommendations that consider beyond-accuracy aspects. From this survey, we identify diversity, novelty, and coverage as important aspects in achieving serendipity and popularity unbiasedness-factors that are important to user satisfaction and business profits, respectively. The five major groups of algorithms that tackle the beyond-accuracy aspects are multi-objective, modified collaborative filtering (CF), clustering, graph, and hybrid; we then classify and describe algorithms as per this typology. The off-line evaluation metrics and user studies carried out by the studies are also described. Based on the survey results, we assert that there is a lot of room for research in the domain. Especially, personalization and generalization are considered important issues that should be addressed in future research (e.g., automatic per-user-trade-off among the aspects, and properly establishing beyond-accuracy aspects for various types of applications or algorithms).

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Temporal diversity in recommender systems

Cited by 222 publications

References 10 publications

Exposure diversity as a design principle for recommender systems

Exposure diversity as a design principle for recommender systems

Effects of the Placement of Diverse Items in Recommendation Lists

A Survey on Recommendation Methods Beyond Accuracy

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