Mediation of user models for enhanced personalization in recommender systems

Berkovsky, Shlomo; Kuflik, Tsvi; Ricci, Francesco

doi:10.1007/s11257-007-9042-9

Cited by 173 publications

(136 citation statements)

References 55 publications

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“…Research on generic user modeling services [17], mediating user models [18], identifying users across system boundaries [19] and cross-system user modeling and personalization [20,21] further supports the re-use of user profiles in different application contexts. In this paper, we introduce a service that generates user profiles by exploiting Twitter and allows for applying these profiles in other applications.…”

Section: Related Workmentioning

confidence: 99%

TUMS: Twitter-Based User Modeling Service

Tao

Abel

Gao

et al. 2012

Lecture Notes in Computer Science

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Abstract. Twitter is today's most popular micro-blogging service on the Social Web. As people discuss various fresh topics, Twitter messages (tweets) can tell much about the current interests and concerns of a user. In this paper, we introduce TUMS, a Twitter-based User Modeling Service, that infers semantic user profiles from the messages people post on Twitter. It features topic detection and entity extraction for tweets and allows for further enrichment by linking tweets to news articles that describe the context of the tweets. TUMS is made publicly available as a Web application. It allows end-users to overview Twitter-based profiles in a structured way and allows them to see in which topics or entities a user was interested in a specific point in time. Furthermore, it provides Twitter-based user profiles in RDF format and allows applications to incorporate these profiles in order to adapt their functionality to the current interests of a user. TUMS is available via: http://wis.ewi.tudelft.nl/tums/

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Section: Related Workmentioning

confidence: 99%

TUMS: Twitter-Based User Modeling Service

Tao

Abel

Gao

et al. 2012

Lecture Notes in Computer Science

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“…On Facebook, users can explicitly declare their interests through profile features, association with groups and fan pages or through status line updates. Such attributes once extracted can be mediated [16] to ratings on items, for example: a user linking her profile to "Levis" fan page is essentially rating the brand and its products as favorable. The same process can be used for tweets (the name for a Twitter post), however methods such as sentiment analysis [17] are required in order to resolve the precise rating, since an open text sentence regarding "Levis" for example, can be a statement of endorsement or of hate.…”

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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“…Some evaluations had shown that in certain conditions, user modeling data mediation improved the quality of recommendations, especially in the cold start of a recommender system [10].…”

Section: Related Workmentioning

confidence: 99%

Cross-domain Recommendations for Personalized Semantic Services

Moe¹,

Aung²

2012

IJCATR

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An increasing amount of work has been published in various areas related to the Recommender System. Among them, cross-domain recommendation is an emerging research topic and in this field, it is important to investigate how to manage personalization and how to consider customer's contextual features to keep more user satisfaction and accuracy. This paper tends to provide cross-domain recommendations for personalized semantic services using Taxonomic CCBR, directed acyclic graph by FordFulkerson algorithm and TOPSIS method. Taxonomic CCBR helps the system get the accurate problem by engaging a user in a series of questions and answers from the user's partial definition of the problem. Semantic concepts between different domains are considered by using weighted directed acyclic graph to find meaningful solutions. Then TOPSIS method is used to get the results more precisely considering contextual features such as season, place, etc. which have not been addressed in the current cross-domain recommender systems.

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Mediation of user models for enhanced personalization in recommender systems

Cited by 173 publications

References 55 publications

TUMS: Twitter-Based User Modeling Service

TUMS: Twitter-Based User Modeling Service

Recommender Systems and the Social Web

Cross-domain Recommendations for Personalized Semantic Services

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