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Towards Serendipity for Content–Based Recommender Systems

Nur Izyan Yasmin Saat, Shahrul Azman Mohd Noah, Masnizah Mohd
2018 International Journal on Advanced Science, Engineering and Information Technology  
Recommender systems are intelligent applications build to predict the rating or preference that a user would give to an item.  ...  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.  ...  • Serendipity-oriented modification -enhance the systems by modifying the accuracy-oriented system and adapt it for a serendipity purpose.  ... 
doi:10.18517/ijaseit.8.4-2.6807 fatcat:sjg7a23bcvflnldjc2xz2wq3my

Auralist

Yuan Cao Zhang, Diarmuid Ó Séaghdha, Daniele Quercia, Tamas Jambor
2012 Proceedings of the fifth ACM international conference on Web search and data mining - WSDM '12  
An ideal recommendation system should mimic the actions of a trusted friend or expert, producing a personalised collection of recommendations that balance between the desired goals of accuracy, diversity  ...  Recommendation systems exist to help users discover content in a large body of items.  ...  We also thankÒscar Celma for making the dataset publicly available, Stephen Clark for his support, and Toby Moncaster and Jon Crowcroft for their comments. We finally thank the anonymous reviewers.  ... 
doi:10.1145/2124295.2124300 dblp:conf/wsdm/ZhangSQJ12 fatcat:ajzweapzfvaqvfbsqbxcxm763e

User-Aware Music Retrieval

Markus Schedl, Sebastian Stober, Emilia Gómez, Nicola Orio, Cynthia C.S. Liem, Marc Herbstritt
2012 Dagstuhl Publications  
Today's user-adaptive systems often incorporate both aspects.  ...  Eventually, the stateof-the-art in building such systems is reviewed, taking in particular aspects of similarity and serendipity into account.  ...  Hence, presenting novel recommendations is a vital requirement for a personalized recommender system. Serendipity Serendipity is a requirement often mentioned in the context of recommender systems.  ... 
doi:10.4230/dfu.vol3.11041.135 dblp:conf/dagstuhl/SchedlSGOL12 fatcat:weo2yddzzrfwjkgmfk7zrs3apy

Generating Self-Serendipity Preference in Recommender Systems for Addressing Cold Start Problems [article]

Yuanbo Xu, Yongjian Yang, En Wang
2022 arXiv   pre-print
In this paper, we devise a novel serendipity-oriented recommender system (Generative Self-Serendipity Recommender System, GS^2-RS) that generates users' self-serendipity preferences to enhance the recommendation  ...  Classical accuracy-oriented Recommender Systems (RSs) typically face the cold-start problem and the filter-bubble problem when users suffer the familiar, repeated, and even predictable recommendations,  ...  for the Filter-Bubble problem Personalized Recommendation is an important factor in recommender systems because a boring, homogenous recommendation is not expected for each individual.  ... 
arXiv:2204.12651v1 fatcat:jlwmxw46fbeevnzxknnfqsabei

Serendipitous Learning Fostered by Brain State Assessment and Collective Wisdom [chapter]

Stefano A. Cerri, Philippe Lemoisson
2020 Lecture Notes in Computer Science  
in generic Information Systems or search engines (e.g.: Google) as well as in other social media (e.g.: recommender systems) offering information retrieval solutions based on the proximity of available  ...  systems): 1. assessing brain states in order to understand and forecast serendipitous human learning events triggered by emotions; 2. enhancing collective wisdom, since Human-Computer Interactions do  ...  of values, as it is the case for most recommender systems.  ... 
doi:10.1007/978-3-030-60735-7_14 fatcat:qezns2gz5baodkxoa2dvhyf6bq

About Performance Evaluation of the Movie Recommendation Systems

Shreya Agrawal, Pooja Jain
2017 International Journal of Computer Applications  
For more and more usage of any system, it is necessary to know about the efficiency of the system and for this reason performance evaluation of a Recommendation system is done.  ...  Movie recommendation systems are now becoming very popular both commercially and also in the research community, where many approaches have been proposed for providing recommendations.  ...  Serendipity In content-based filtering recommendation system there is no serendipitous items, that is, serendipity is the capability of the system to give a product which appear surprisingly interesting  ... 
doi:10.5120/ijca2017912739 fatcat:dthbja57fratxlaapsf3gzgafi

Modelling serendipity in a computational context [article]

Joseph Corneli, Anna Jordanous, Christian Guckelsberger, Alison Pease, Simon Colton
2020 arXiv   pre-print
We conclude that it is feasible to equip computational systems with the potential for serendipity, and that this could be beneficial in varied computational creativity/AI applications, particularly those  ...  Most existing research that considers serendipity in a computing context deals with serendipity as a service; here we relate theories of serendipity to the development of autonomous systems and computational  ...  We are grateful to the Society for the study of Artificial Intelligence and Simulation of Behaviour (AISB) for supporting two workshops where participants engaged with this material, and  ... 
arXiv:1411.0440v8 fatcat:rn5cb6cosbcj7nlson3h7jk3tq

Context-Aware Music Recommendation with Serendipity Using Semantic Relations [chapter]

Mian Wang, Takahiro Kawamura, Yuichi Sei, Hiroyuki Nakagawa, Yasuyuki Tahara, Akihiko Ohsuga
2014 Lecture Notes in Computer Science  
A goal for the creation and improvement of music recommendation is to retrieve users' preferences and select the music adapting to the preferences.  ...  Finally, preliminary experiments confirm balance of accuracy and serendipity of the music recommendation.  ...  We would like to thank Professor Shinichi Honiden in National Institute of Informatics/University of Tokyo and his group for offering a place for discussing, studying and providing instructions.  ... 
doi:10.1007/978-3-319-06826-8_2 fatcat:5bxleg3o45eclilwfelnygk7s4

Assessment Methods for Evaluation of Recommender Systems: A Survey

Madhusree Kuanr, Puspanjali Mohapatra
2021 Foundations of Computing and Decision Sciences  
But an evaluation of the recommender system is an important factor as it involves the trust of the user in the system.  ...  Various incompatible assessment methods are used for the evaluation of recommender systems, but the proper evaluation of a recommender system needs a particular objective set by the recommender system.  ...  Ubiquitous computing technologies and context aware recommender systems for ubiquitous learning.  ... 
doi:10.2478/fcds-2021-0023 fatcat:e2ocoh7hhnastms6q3a6pbgoyu

Bisociative Music Discovery and Recommendation [chapter]

Sebastian Stober, Stefan Haun, Andreas Nürnberger
2012 Lecture Notes in Computer Science  
Surprising a user with unexpected and fortunate recommendations is a key challenge for recommender systems.  ...  As application domain we focus on music recommendation using MusicGalaxy, an adaptive user-interface for exploring music collections.  ...  The authors would like to thank the members of the EU BISON project for many fruitful discussions on the topic of bisociation.  ... 
doi:10.1007/978-3-642-31830-6_33 fatcat:qmpwy7phurcdrjkbujbgskfne4

Towards Interactive Recommender Systems with the Doctor-in-the-Loop [article]

Andreas Holzinger, André Calero Valdez, Martina Ziefle
2016 Mensch & Computer  
Recommender Systems are a perfect example for automatic Machine Learning (aML) – which is the fastest growing field in computer science generally and health informatics specifically.  ...  Important future research aspects are in the combined use of both human intelligence and computer intelligence, in the context of hybrid multi-agent recommender systems which can also make use of the power  ...  Acknowledgements We would like to thank the anonymous reviewers for their constructive comments on an earlier version of this manuscript.  ... 
doi:10.18420/muc2016-ws11-0001 dblp:conf/mc/HolzingerVZ16 fatcat:xtdpkh7qk5hynko5guhyxs4nrq

Increasing Serendipity of Recommender System with Ranking Topic Model

Zhibo Xiao, Feng Che, Enuo Miao, Mingyu Lu
2014 Applied Mathematics & Information Sciences  
We proposed a ranking topic model based semantic recommendation framework which helps to introduce serendipity to the system.  ...  Since there is little work on how to evaluate the serendipity degree of recommender system, we proposed two measure to evaluate this metric.  ...  Without doubt, serendipity paper is what researchers expected from an academic paper recommender system.  ... 
doi:10.12785/amis/080463 fatcat:fs32u6k6d5djrl3xbopmomsmuy

Sequeval: A Framework to Assess and Benchmark Sequence-based Recommender Systems [article]

Diego Monti, Enrico Palumbo, Giuseppe Rizzo, Maurizio Morisio
2018 arXiv   pre-print
For this reason, it is possible to easily integrate and evaluate novel recommendation techniques. sequeval is publicly available as an open source tool and it aims to become a focal point for the community  ...  to assess sequence-based recommender systems.  ...  Recommenders We have included in sequeval four baseline recommenders, which represent an adaptation of classical non-personalized baselines to the sequence-based scenario.  ... 
arXiv:1810.04956v2 fatcat:noxzfjb2kfc2xbeaaznhrdc2wy

Predicting Online Performance of News Recommender Systems Through Richer Evaluation Metrics

Andrii Maksai, Florent Garcin, Boi Faltings
2015 Proceedings of the 9th ACM Conference on Recommender Systems - RecSys '15  
We investigate how metrics that can be measured offline can be used to predict the online performance of recommender systems, thus avoiding costly A-B testing.  ...  Using the model, we quantify the trade-off between different metrics and propose to use it to tune the parameters of recommender algorithms without the need for online testing.  ...  right adaptations in a live recommender system.  ... 
doi:10.1145/2792838.2800184 dblp:conf/recsys/MaksaiGF15 fatcat:tv4gugb5ljgmdaufz5v35a4riq

Preference Dissemination by Sharing Viewpoints - Simulating Serendipity

Guillaume Surroca, Philippe Lemoisson, Clément Jonquet, Stefano Cerri
2015 Proceedings of the 7th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management  
Our results outline the most appropriate strategies for incidental learning, bringing us closer to understanding and modeling the processes involved in Serendipity.  ...  An implementation of the Viewpoints formalism kernel is available.  ...  The rest of this article is organized as follows: section 2 presents the background and inspiration for our approach by introducing the notion of Serendipity in computer systems.  ... 
doi:10.5220/0005636204020409 dblp:conf/ic3k/SurrocaLJC15 fatcat:byxnoxmfgnefrmvs53z4kkflny
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