User model interoperability: a survey

Carmagnola, Francesca; Cena, Federica; Gena, Cristina

doi:10.1007/s11257-011-9097-5

Cited by 89 publications

(49 citation statements)

References 71 publications

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“…Regarding this aspect, iCITY has recently integrated an interoperability module that allows it to export tags to another social adaptive application, CHIP , a system that suggests artworks and virtual tours of the Rijksmuseum of Amsterdam. This is an example of re-use of user interaction data (tags) generated by one application into another one in a similar domain for solving the cold-start problem and providing cross-systems recommendations [Carmagnola et al, 2011b].…”

Section: Resultsmentioning

confidence: 99%

The evaluation of a social adaptive website for cultural events

Gena

Cena

Vernero

et al. 2012

User Model User-Adap Inter

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Section: Resultsmentioning

confidence: 99%

The evaluation of a social adaptive website for cultural events

Gena

Cena

Vernero

et al. 2012

User Model User-Adap Inter

Self Cite

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“…For instance, users might become dissatisfied with accurate recommendations when they have no trust in the recommender system's operator [342], their privacy is not ensured [300], they need to wait too long for recommendations [300], or they find the user interfaces unappealing [343]. Other factors that affect user satisfaction are confidence in a recommender system [263], data security [344], diversity [345], user tasks [87], item's lifespan [346] and novelty [347], risk of accepting recommendations [348], robustness against spam and fraud [349], transparency and explanations [350], time to first recommendation [225], and interoperability [351].…”

Section: Focus On Accuracymentioning

confidence: 99%

Research-paper recommender systems: a literature survey

et al. 2015

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In the last 16 years, more than 200 research articles were published about research-paper recommender systems. We reviewed these articles and present some descriptive statistics in this paper, as well as a discussion about the major advancements and shortcomings and an overview of the most common recommendation concepts and approaches. We found that more than half of the recommendation approaches applied content-based filtering (55 %). Collaborative filtering was applied by only 18 % of the reviewed approaches, and graph-based recommendations by 16 %. Other recommendation concepts included stereotyping, item-centric recommendations, and hybrid recommendations. The content-based filtering approaches mainly utilized papers that the users had authored, tagged, browsed, or downloaded. TF-IDF was the most frequently applied weighting scheme. In addition to simple terms, n-grams, topics, and citations were utilized to model users' information needs. Our review revealed some shortcomings of the current research. First, it remains unclear which recommendation concepts and approaches are the most promising. For instance, researchers reported different results on the performance of contentbased and collaborative filtering. Sometimes content-based filtering performed better than collaborative filtering and Linnaeus University, Kalmar, Sweden sometimes it performed worse. We identified three potential reasons for the ambiguity of the results. (A) Several evaluations had limitations. They were based on strongly pruned datasets, few participants in user studies, or did not use appropriate baselines. (B) Some authors provided little information about their algorithms, which makes it difficult to re-implement the approaches. Consequently, researchers use different implementations of the same recommendations approaches, which might lead to variations in the results. (C) We speculated that minor variations in datasets, algorithms, or user populations inevitably lead to strong variations in the performance of the approaches. Hence, finding the most promising approaches is a challenge. As a second limitation, we noted that many authors neglected to take into account factors other than accuracy, for example overall user satisfaction. In addition, most approaches (81 %) neglected the user-modeling process and did not infer information automatically but let users provide keywords, text snippets, or a single paper as input. Information on runtime was provided for 10 % of the approaches. Finally, few research papers had an impact on research-paper recommender systems in practice. We also identified a lack of authority and long-term research interest in the field: 73 % of the authors published no more than one paper on research-paper recommender systems, and there was little cooperation among different co-author groups. We concluded that several actions could improve the research landscape: developing a common evaluation framework, agreement on the information to include in research papers, a stronger focus on non-accuracy aspect...

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“…Each source"s user model can have its unique data representation and formats, leading to a need in translation/conflict resolution/mediation methods that could integrate them all into a unified model. Such methods were surveyed in depth in a recent study [21].…”

Section: Mapping Social Web Services Contribution To Classical Recommmentioning

confidence: 99%

Recommender Systems and the Social Web

Tiroshi

Kuflik

Kay

et al. 2012

Lecture Notes in Computer Science

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Abstract. In the past, classic recommender systems relied solely on the user models they were able to construct by themselves and suffered from the "cold start" problem. Recent decade advances, among them internet connectivity and data sharing, now enable them to bootstrap their user models from external sources such as user modeling servers or other recommender systems. However, this approach has only been demonstrated by research prototypes. Recent developments have brought a new source for bootstrapping recommender systems: social web services. The variety of social web services, each with its unique user model characteristics, could aid bootstrapping recommender systems in different ways. In this paper we propose a mapping of how each of the classical user modeling approaches can benefit from nowadays active services" user models, and also supply an example of a possible application.

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User model interoperability: a survey

Cited by 89 publications

References 71 publications

The evaluation of a social adaptive website for cultural events

The evaluation of a social adaptive website for cultural events

Research-paper recommender systems: a literature survey

Recommender Systems and the Social Web

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