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Towards Serendipity for Content–Based Recommender Systems
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. ...
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
2012
Proceedings of the fifth ACM international conference on Web search and data mining - WSDM '12
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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
2012
Dagstuhl Publications
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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]
2022
arXiv
pre-print
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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]
2020
Lecture Notes in Computer Science
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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
2017
International Journal of Computer Applications
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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]
2020
arXiv
pre-print
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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]
2014
Lecture Notes in Computer Science
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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
2021
Foundations of Computing and Decision Sciences
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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]
2012
Lecture Notes in Computer Science
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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]
2016
Mensch & Computer
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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
2014
Applied Mathematics & Information Sciences
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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]
2018
arXiv
pre-print
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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
2015
Proceedings of the 9th ACM Conference on Recommender Systems - RecSys '15
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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
2015
Proceedings of the 7th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management
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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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