A Serendipity Model for News Recommendation

Jenders, Maximilian; Lindhauer, T.; Kasneci, Gjergji; Krestel, Ralf; Naumann, Felix

doi:10.1007/978-3-319-24489-1_9

Cited by 18 publications

(14 citation statements)

References 14 publications

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“…This type of diversity refers to the possibility of locating a story which is unexpected, but still useful for the reader. The integration of unexpectedness in recommenders is known to increase user satisfaction [19] and broaden user preferences by diversifying their interests [23,59]. From a normative point of view, unexpectedness is integral for countering negative e ects of over-tting, such as ideological/topical isolation resulting from " lter bubbles"/"echo chambers" [60].…”

Section: Metricsmentioning

confidence: 99%

Siren

Bountouridis

Harambam

Makhortykh

et al. 2019

Proceedings of the Conference on Fairness, Accountability, and Transparency

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The growing volume of digital data stimulates the adoption of recommender systems in di erent socioeconomic domains, including ecommerce, music, and news industries. While news recommenders help consumers deal with information overload and increase their engagement and satisfaction, their use also raises an increasing number of societal concerns, such as "Matthew e ects", " lter bubbles", and the overall lack of transparency. We argue that focusing on transparency for content-providers is an under-explored avenue. As such, we designed a simulation framework called SIREN 1 (SImulating Recommender E ects in online News environments), that allows news content providers to (i) select and parameterize di erent recommenders and (ii) analyze and visualize their e ects with respect to two diversity metrics with little costs. Taking the U.S. news media as a case study, we present an analysis on the recommender e ects with respect to long-tail novelty and unexpectedness using SIREN. Our analysis o ers a number of interesting ndings, such as the similar potential of certain algorithmically simple (item-based k-Nearest Neighbour) and sophisticated strategies (based on Bayesian Personalized Ranking) to increase diversity over time. Overall, we argue that simulating the e ects of recommender systems can help content providers to make more informed decisions when choosing algorithmic recommenders, and as such can help mitigate the aforementioned societal concerns.

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Section: Metricsmentioning

confidence: 99%

Siren

Bountouridis

Harambam

Makhortykh

et al. 2019

Proceedings of the Conference on Fairness, Accountability, and Transparency

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show abstract

“…Akiyama et al [22] Unexpected. Iaquinta et al [23] Relevant, novel and unexpected De Gemmis et al [24] Relevant and unexpected Jenders et al [25] Unexpected and interesting Maccatrozzo et al [14] Pleasant, relevant and unexpected…”

Section: Referencesmentioning

confidence: 99%

“…Jenders et al [25] proposed an unexpectedness model of topic combinations in articles and a traditional cosine-based similarity model that recommends serendipitous news article. The research only focus on the dissimilarity of the latent topic.…”

Section: Approaches Of Serendipity In Recommender Systemmentioning

confidence: 99%

“…Approaches Iaquinta et al [23] Hybrid based Akiyama et al [22] Distance metric Jenders et al [25] Unexpectedness model of topic combination Sugiyama & Kan [28] Model individual profile De Gemmis et al [24] Graph based with additional knowledge based from Wikipedia and WordNet. Maccatrozzo et al [14] SIRUP model, using Sylvia's curiosity theory based on fundamental theory of Berlyne.…”

Section: Referencesmentioning

confidence: 99%

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Towards Serendipity for Content–Based Recommender Systems

Saat

Noah

Mohd

2018

International Journal on Advanced Science, Engineering and Information Technology

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Recommender systems are intelligent applications build to predict the rating or preference that a user would give to an item. One of the fundamental recommendation methods in the content-based method that predict ratings by exploiting attributes about the users and items such as users' profile and textual content of items. A current issue faces by recommender systems based on this method is that the systems seem to recommend too similar items to what users have known. Thus, creating over-specialisation issues, in which a self-referential loop is created that leaves user in their own circle of finding and never get expose to new items. In order for these systems to be of significance used, it is important that not only relevant items been recommender, but the items must be also interesting and serendipitous. Having a serendipitous recommendation let users explore new items that they least expect. This has resulted in the issues of serendipity in recommender systems. However, it is difficult to define serendipity because in recommender system, there is no consensus definition for this term. Most of researchers define serendipity based on their research purposes. From the reviews, majority shows that unexpected as the important aspect in defining serendipity. Thus, in this paper, we aim to formally define the concept of serendipity in recommender systems based on the literature work done. We also reviewed few approaches that apply serendipity in the content-based methods in recommendation. Techniques that used Linked Open Data (LOD) approaches seems to be a good candidate to find relevant, unexpected and novel item in a large dataset.

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“…The measurement of serendipity in RSs has received more attention from the scientific community in recent years [12]. Some efforts delegate the measurement of this aspect to procedures that directly involve the perception of the user [10], others propose quantitative measures based on the distance between the results produced by the method to be evaluated and those produced by a primitive prediction method [15,4] and, finally, some authors propose measurements based on observations regarding the history of ratings and items popularity [5]. This latter strategy is adopted herein.…”

Section: Serendipity In Recommender Systemsmentioning

confidence: 99%

Exploring coclustering for serendipity improvement in content-based recommendation

Silva

Costa

Diaz

et al. 2018

EasyChair Preprints

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Content-based recommender systems are now widely used for item recommendations in several application domains such as entertainment, e-commerce and news. However, one of its major drawbacks is the lack of serendipity in recommendations. A recommendation is considered serendipitous when is both relevant and unexpected. There is a common understanding in literature that the search for serendipitous recommendations should be guided by partial similarities between items. From that intuition, coclustering can be exploited in order to ensure the compromise between accuracy and unexpectedness leading to serendipitous recommendations to the users, since it is a technique capable of finding partial similarity relations between items. In this paper, we propose the use of coclustering for serendipity improvement in content-based recommender systems. Experiments carried out over the MovieLens 2K dataset show that the proposed approach overcome a traditional content-based recommender in terms of serendipity.

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A Serendipity Model for News Recommendation

Cited by 18 publications

References 14 publications

Siren

Siren

Towards Serendipity for Content–Based Recommender Systems

Exploring coclustering for serendipity improvement in content-based recommendation

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