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On Unexpectedness in Recommender Systems

Panagiotis Adamopoulos, Alexander Tuzhilin
2014 ACM Transactions on Intelligent Systems and Technology  
On unexpectedness in recommender systems: Or how to better expect the unexpected.  ...  In particular, we propose a new concept of unexpectedness as recommending to users those items that depart from what they would expect from the system -the consideration set of each user.  ...  he expects to receive or has received in the past.  ... 
doi:10.1145/2559952 fatcat:elbnlvmogff53ej3zudjkqr7qu

Fusion-based Recommender System for Improving Serendipity

Kenta Oku, Fumio Hattori
2011 ACM Conference on Recommender Systems  
In this paper, we propose a Fusion-based Recommender System that aims to improve the serendipity of recommender systems.  ...  Serendipity, which is one of these measures, is defined as a measure that indicates how the recommender system can find unexpected and useful items for users.  ...  It is based on the assumption that the greater the extent to which the user restricts support to only the recommended item r k , the better the user supports item r k .  ... 
dblp:conf/recsys/OkuH11 fatcat:5c35c5woqfa6bn6gonh3gex3my

How good your recommender system is? A survey on evaluations in recommendation

Thiago Silveira, Min Zhang, Xiao Lin, Yiqun Liu, Shaoping Ma
2017 International Journal of Machine Learning and Cybernetics  
However, one of the current challenges in the area refers to how to properly evaluate the predictions generated by a recommender system.  ...  Recommender Systems have become a very useful tool for a large variety of domains. Researchers have been attempting to improve their algorithms in order to issue better predictions to the users.  ...  They are communicating with the academy on how to leverage the metrics in their systems.  ... 
doi:10.1007/s13042-017-0762-9 fatcat:o77u7tg4yva47nlo6vto2xeaee

Deep Content Analytics Methods To Improve Transparency And Serendipity Of Recommender Systems

Marco De Gemmis
2017 Zenodo  
on the distributional hypothesis, which states that "words that occur in the same contexts tend to have similar meanings".  ...  Those systems analyze both item descriptions (content) and user ratings to infer user profiles, which store information about preferences, exploited to suggest items similar to those users liked in the  ...  Acknowledgments This project has received funding from the European Union's Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie grant agreement No 691071.  ... 
doi:10.5281/zenodo.1000933 fatcat:acbtb6lubjcetb7otubpcmewoi

User Curiosity Factor in Determining Serendipity of Recommender System

Arseto Satriyo Nugroho, Igi Ardiyanto, Teguh Bharata Adji
2021 IJITEE (International Journal of Information Technology and Electrical Engineering)  
Since the concept of a recommendation system is still evolving today, formalizing the definition of serendipity in a recommendation system is very challenging.One known subjective factor of serendipity  ...  Too much focus on accuracy in RS may lead to an overspecialization problem, which will decrease its effectiveness.  ...  One method is by calculating the distance of recommended items to the users' expected item set [30] . The expected set by user ' will have an unexpectedness value of 0.  ... 
doi:10.22146/ijitee.67553 fatcat:mravfisbhvdwvmo7qfvczqkyfy

Beyond accuracy

Mouzhi Ge, Carla Delgado-Battenfeld, Dietmar Jannach
2010 Proceedings of the fourth ACM conference on Recommender systems - RecSys '10  
When we evaluate the quality of recommender systems (RS), most approaches only focus on the predictive accuracy of these systems.  ...  We then analyze the role of coverage and serendipity as indicators of recommendation quality, present novel ways of how they can be measured and discuss how to interpret the obtained measurements.  ...  In order to measure unexpectedness, we need a benchmark model that generates expected recommendations.  ... 
doi:10.1145/1864708.1864761 dblp:conf/recsys/GeDJ10 fatcat:vx24khtqofchllearheod6cwy4

One Recommender Fits All? An Exploration of User Satisfaction With Text-Based News Recommender Systems

Mareike Wieland, Gerret Von Nordheim, Katharina Kleinen-von Königslöw
2021 Media and Communication  
While social media extensively rely on user data for personalized recommendations, journalistic media may choose to aim to improve the user experience based on textual features such as thematic similarity  ...  The core piece of our survey is a self-programmed recommendation system that accesses a database of vectorized news articles.  ...  Acknowledgments The authors would like to thank the three anonymous reviewers for their valuable comments and suggestions.  ... 
doi:10.17645/mac.v9i4.4241 fatcat:ckz2fzt73bhhzco5enl4sjovoe

Influence of tweets and diversification on serendipitous research paper recommender systems

Chifumi Nishioka, Jörn Hauke, Ansgar Scherp
2020 PeerJ Computer Science  
In recent years, a large body of literature has accumulated around the topic of research paper recommender systems.  ...  As an evaluation metric, we use the serendipity score (SRDP), in which the unexpectedness of recommendations is inferred by using a primitive recommendation strategy.  ...  The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.  ... 
doi:10.7717/peerj-cs.273 pmid:33816924 pmcid:PMC7924691 fatcat:ppkqtvifgnhhbllnoypej4yeju

Q-Chef: The impact of surprise-eliciting systems on food-related decision-making

Kazjon Grace, Elanor Finch, Natalia Gulbransen-Diaz, Hamish Henderson
2022 CHI Conference on Human Factors in Computing Systems  
While recommendation systems are an effective approach to assist users with this culinary decision-making, they typically prioritise similarity to a query or user profile to give relevant results.  ...  We conclude with a set of suggestions for the design of future surprise-eliciting recipe systems. CCS CONCEPTS • Human-centered computing → Interaction design; Empirical studies in HCI.  ...  The authors also gratefully acknowledge the support of the Australian Research Council (Project #DE180101416).  ... 
doi:10.1145/3491102.3501862 fatcat:sr674c53ozadrm6a4eejzbkysu

Assessment Methods for Evaluation of Recommender Systems: A Survey

Madhusree Kuanr, Puspanjali Mohapatra
2021 Foundations of Computing and Decision Sciences  
The recommender system (RS) filters out important information from a large pool of dynamically generated information to set some important decisions in terms of some recommendations according to the user's  ...  But an evaluation of the recommender system is an important factor as it involves the trust of the user in the system.  ...  ACM Transactions on Information Systems (TOIS), 23(1), 103-145. [4] Adamopoulos, P., Tuzhilin, A. (2014). On unexpectedness in recommender sys- tems: Or how to better expect the unexpected.  ... 
doi:10.2478/fcds-2021-0023 fatcat:e2ocoh7hhnastms6q3a6pbgoyu

On discovering non-obvious recommendations

Panagiotis Adamopoulos
2014 Proceedings of the 7th ACM international conference on Web search and data mining - WSDM '14  
Furthermore, we propose a concept of unexpectedness in recommender systems and operationalize it by suggesting various mechanisms for specifying the expectations of the users and proposing a recommendation  ...  This paper proposes a number of studies in order to move the field of recommender systems beyond the traditional paradigm and the classical perspective of rating prediction accuracy.  ...  In particular, we formally define the concept of unexpectedness in recommender systems taking into account the actual expectations of the users and discuss how the concept of unexpectedness is differentiated  ... 
doi:10.1145/2556195.2556204 dblp:conf/wsdm/Adamopoulos14 fatcat:57euabzoyne3xodu42rcipvbku

Latent Unexpected Recommendations [article]

Pan Li, Alexander Tuzhilin
2020 arXiv   pre-print
Previous unexpected recommendation methods only focus on the straightforward relations between current recommendations and user expectations by modeling unexpectedness in the feature space, thus resulting  ...  Unexpected recommender system constitutes an important tool to tackle the problem of filter bubbles and user boredom, which aims at providing unexpected and satisfying recommendations to target users at  ...  expect from the recommender system.  ... 
arXiv:2007.13280v1 fatcat:oxnronmcp5bm7h5sllm3axkryu

Semantic Network-driven News Recommender Systems: a Celebrity Gossip Use Case

Marco Fossati, Claudio Giuliano, Giovanni Tummarello
2012 International Semantic Web Conference  
Information overload on the Internet motivates the need for filtering tools. Recommender systems play a significant role in such a scenario, as they provide automatically generated suggestions.  ...  In this paper, we propose a novel recommendation approach, based on semantic networks exploration.  ...  This work was supported by the EU project Eurosentiment, contract number 296277.  ... 
dblp:conf/semweb/FossatiGT12 fatcat:jvjcvemhkrgxzbqq6ul65guw44

Being accurate is not enough

Sean M. McNee, John Riedl, Joseph A. Konstan
2006 CHI '06 extended abstracts on Human factors in computing systems - CHI EA '06  
Most research up to this point has focused on improving the accuracy of recommender systems. We believe that not only has this narrow focus been misguided, but has even been detrimental to the field.  ...  The recommendations that are most accurate according to the standard metrics are sometimes not the recommendations that are most useful to users.  ...  Acknowledgements We would like to thank our collaborators, Cai-Nicolas Ziegler and Roberto Torres, and all of GroupLens Research.  ... 
doi:10.1145/1125451.1125659 dblp:conf/chi/McNeeRK06 fatcat:fnekwq4x2ncabjn32s3dsmtncq

Apples and Oranges: Detecting Least-Privilege Violators with Peer Group Analysis [article]

Suman Jana, Úlfar Erlingsson, Iulia Ion
2015 arXiv   pre-print
Our evaluation is based on empirically applying our analysis to over a million software items, in two different online software markets, and on a validation of our assumptions in a medium-scale user study  ...  This paper introduces software peer group analysis, a novel technique to identify least privilege violation and rank software based on the severity of the violation.  ...  We thank Karen Lees for her help in performing several experiments.  ... 
arXiv:1510.07308v1 fatcat:vkcjy2vofzge5o7ieosb3gwlze
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