Search results for "serendipity"

showing 10 items of 17 documents

Challenges of Serendipity in Recommender Systems

2016

Most recommender systems suggest items similar to a user profile, which results in boring recommendations limited by user preferences indicated in the system. To overcome this problem, recommender systems should suggest serendipitous items, which is a challenging task, as it is unclear what makes items serendipitous to a user and how to measure serendipity. The concept is difficult to investigate, as serendipity includes an emotional dimension and serendipitous encounters are very rare. In this paper, we discuss mentioned challenges, review definitions of serendipity and serendipity-oriented evaluation metrics. The goal of the paper is to guide and inspire future efforts on serendipity in r…

haasteet (ongelmat)ta113Computer scienceSerendipitysuosittelujärjestelmätserendipitychallenges02 engineering and technologyRecommender systemunexpectednessnoveltyevaluation metricsWorld Wide Webrelevanssi020204 information systems0202 electrical engineering electronic engineering information engineering020201 artificial intelligence & image processingrelevancerecommender systemsProceedings of the 12th International Conference on Web Information Systems and Technologies
researchProduct

How does serendipity affect diversity in recommender systems? A serendipity-oriented greedy algorithm

2018

Most recommender systems suggest items that are popular among all users and similar to items a user usually consumes. As a result, the user receives recommendations that she/he is already familiar with or would find anyway, leading to low satisfaction. To overcome this problem, a recommender system should suggest novel, relevant and unexpected i.e., serendipitous items. In this paper, we propose a serendipity-oriented, reranking algorithm called a serendipity-oriented greedy (SOG) algorithm, which improves serendipity of recommendations through feature diversification and helps overcome the overspecialization problem. To evaluate our algorithm, we employed the only publicly available datase…

Computer science02 engineering and technologyRecommender systemDiversification (marketing strategy)Machine learningcomputer.software_genreTheoretical Computer SciencenoveltySingular value decompositionalgoritmit0202 electrical engineering electronic engineering information engineeringFeature (machine learning)serendipity-2018Greedy algorithmlearning to rankNumerical AnalysisSerendipitybusiness.industrysuosittelujärjestelmät020206 networking & telecommunicationsserendipityPopularityunexpectednessComputer Science ApplicationsComputational MathematicsComputational Theory and MathematicsRanking020201 artificial intelligence & image processingArtificial intelligencebusinesscomputerarviointiSoftware
researchProduct

A survey of serendipity in recommender systems

2016

We summarize most efforts on serendipity in recommender systems.We compare definitions of serendipity in recommender systems.We classify the state-of-the-art serendipity-oriented recommendation algorithms.We review methods to assess serendipity in recommender systems.We provide the future directions of serendipity in recommender systems. Recommender systems use past behaviors of users to suggest items. Most tend to offer items similar to the items that a target user has indicated as interesting. As a result, users become bored with obvious suggestions that they might have already discovered. To improve user satisfaction, recommender systems should offer serendipitous suggestions: items not …

Measure (data warehouse)Information Systems and ManagementInformation retrievalComputer scienceSerendipityNovelty02 engineering and technologyRecommender systemManagement Information SystemsWorld Wide WebArtificial Intelligence020204 information systems0202 electrical engineering electronic engineering information engineering020201 artificial intelligence & image processingMetric (unit)SoftwareKnowledge-Based Systems
researchProduct

Improving Serendipity and Accuracy in Cross-Domain Recommender Systems

2017

Cross-domain recommender systems use information from source domains to improve recommendations in a target domain, where the term domain refers to a set of items that share attributes and/or user ratings. Most works on this topic focus on accuracy but disregard other properties of recommender systems. In this paper, we attempt to improve serendipity and accuracy in the target domain with datasets from source domains. Due to the lack of publicly available datasets, we collect datasets from two domains related to music, involving user ratings and item attributes. We then conduct experiments using collaborative filtering and content-based filtering approaches for the purpose of validation. Ac…

Focus (computing)data collectionInformation retrievalData collectionSerendipityComputer sciencesuosittelujärjestelmätserendipity02 engineering and technologyRecommender systemDomain (software engineering)Term (time)collaborative filtering020204 information systemscross-domain recommendations0202 electrical engineering electronic engineering information engineeringCollaborative filteringcontent-based filtering020201 artificial intelligence & image processingSet (psychology)
researchProduct

Serendipity and innovation: history and evolution of transthoracic echocardiography

2017

The history of echocardiography is sprinkled with many interesting episodes and anecdotes showing that devoting your life to the pursuit of one goal is praiseworthy, and that at the same time, a little luck goes a long way. Transthoracic echocardiography (TTE) has led to dramatic improvements in cardiovascular medicine, and is now the most widely used diagnostic cardiac test after electrocardiography (ECG). The present review pays tribute to the pioneering efforts of those who believed in this innovative technology despite mounted skepticism and briefly describes the evolution of TTE from its early days to the most recent developments.

Pulmonary and Respiratory Medicinemedicine.medical_specialtyLuckSerendipitybusiness.industrymedia_common.quotation_subjectmedicineMedical physicsReview ArticlebusinessSkepticismmedia_commonTest (assessment)
researchProduct

The Repurposing of Old Drugs or Unsuccessful Lead Compounds by in Silico Approaches: New Advances and Perspectives

2015

Have you a compound in your lab, which was not successful against the designed target, or a drug that is no more attractive? The drug repurposing represents the right way to reconsider them. It can be defined as the modern and rationale approach of the traditional methods adopted in drug discovery, based on the knowledge, insight and luck, alias known as serendipity. This repurposing approach can be applied both in silico and in wet. In this review we report the molecular modeling facilities that can be of huge support in the repurposing of drugs and/or unsuccessful lead compounds. In the last decades, different methods were proposed to help the scientists in drug design and in drug repurpo…

Models Molecular0301 basic medicineLead compoundDatabases FactualChemistry PharmaceuticalIn silicoDrug repurposingNanotechnologyLigandsDrug design03 medical and health sciencesLead (geology)In silico approacheDrug DiscoveryHumansComputer SimulationRepurposingDrug discoverySerendipityDrug Discovery3003 Pharmaceutical ScienceDrug repositioningGeneral MedicineSettore CHIM/08 - Chimica FarmaceuticaData scienceDrug repositioningComputingMethodologies_PATTERNRECOGNITION030104 developmental biologyStructure basedLigand basedStructure BasedSoftwareCurrent Topics in Medicinal Chemistry
researchProduct

Heavy Flavor Production and Decay With Prompt Leptons In the Aleph Detector

1994

In 431 000 hadronicZ decays recorded with the ALEPH detector at LEP, the yields of electrons and muons in events with one or more prompt leptons have been analysed to give information on the production and decay of heavy quarks. The fractions of $$b\bar b$$ and $$c\bar c$$ events are measured to be 0.219±0.006±0.005 and 0.165±0.005±0.020, and the corresponding forward-backward asymmetries at theZ mass are measured to be 0.090±0.013±0.003 and 0.111±0.021±0.018, after QED and QCD corrections. Measurements for the semileptonic branching ratios BR $$(b \to \ell ^ - \bar vX)$$ and BR (b→cl+ vX) yield 0.114±0.003±0.004 and 0.082±0.003±0.012, respectively. The dilepton events enable measurement of…

QuarkParticle physicsPhysics and Astronomy (miscellaneous)Elementary particleheavy flavour01 natural sciencesALEPH ExperimentNuclear physics0103 physical sciences[PHYS.HEXP]Physics [physics]/High Energy Physics - Experiment [hep-ex]Information retrieval010306 general physicsEngineering (miscellaneous)ALEPH experimentQuantum chromodynamicsPhysicskwnoledge organisationMuon010308 nuclear & particles physicsBranching fractionALEPH Experiment; LEP; heavy flavourserendipityWeinberg angleLEPALEPH detectorParticle Physics - ExperimentLepton
researchProduct

The CINHEKS Research Design: Taking Stock and Moving Forward

2016

Comparative research design, at its best, in an international project focused on a complex topic, is a dynamic, iterative and on-going process. In the CINHEKS study this proved to be the case, both by design and in several ways our team did not, nor could not, anticipate. The tensions between purposeful planning, inevitable setbacks and serendipity turned out to be one of the most interesting aspects of CINHEKS and the purpose of this chapter is to take a step back, well outside methodological convention, to holistically and critically reflect on the lessons learned during the planning and execution of the CINHEKS comparative study. This chapter is an analysis of our efforts regarding the c…

International researchResearch designOperations researchSerendipity05 social sciences050301 educationConventionComparative research0502 economics and businessEconomicsEngineering ethics0503 education050203 business & managementStock (geology)
researchProduct

Investigating serendipity in recommender systems based on real user feedback

2018

Over the past several years, research in recommender systems has emphasized the importance of serendipity, but there is still no consensus on the definition of this concept and whether serendipitous items should be recommended is still not a well-addressed question. According to the most common definition, serendipity consists of three components: relevance, novelty and unexpectedness, where each component has multiple variations. In this paper, we looked at eight different definitions of serendipity and asked users how they perceived them in the context of movie recommendations. We surveyed 475 users of the movie recommender system, MovieLens regarding 2146 movies in total and compared tho…

ta113Information retrievalComputer scienceSerendipityuutuudetpalautesuosittelujärjestelmätNoveltyserendipityContext (language use)02 engineering and technologyVariation (game tree)Recommender systemunexpectednessPreferenceMovieLenssattumanovelty020204 information systems0202 electrical engineering electronic engineering information engineering020201 artificial intelligence & image processingRelevance (information retrieval)relevancerecommender systemsProceedings of the 33rd Annual ACM Symposium on Applied Computing
researchProduct

A Serendipity-Oriented Greedy Algorithm for Recommendations

2017

Most recommender systems suggest items to a user that are popular among all users and similar to items the user usually consumes. As a result, a user receives recommendations that she/he is already familiar with or would find anyway, leading to low satisfaction. To overcome this problem, a recommender system should suggest novel, relevant and unexpected, i.e. serendipitous items. In this paper, we propose a serendipity-oriented algorithm, which improves serendipity through feature diversification and helps overcome the overspecialization problem. To evaluate our algorithm and compare it with others, we employ a serendipity metric that captures each component of serendipity, unlike the most …

ta113SerendipityComputer sciencebusiness.industrysuosittelujärjestelmät020207 software engineeringserendipity02 engineering and technologyalgorithmsunexpectednessnoveltyalgoritmit0202 electrical engineering electronic engineering information engineering020201 artificial intelligence & image processingArtificial intelligencerecommender systemsGreedy algorithmbusinessGreedy randomized adaptive search procedure
researchProduct