Locally Expandable Allocation of Folksonomy Tags in a Directed Acyclic Graph

Eda, Takeharu; Yoshikawa, Masatoshi; Yamamuro, Masashi

doi:10.1007/978-3-540-85481-4_13

Cited by 9 publications

(9 citation statements)

References 11 publications

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“…Many studies have focused on the determination of relationship among words [13], [23]. We use the approach proposed by Eda et al [13] because it finds related words based on co-occurrence among users with similar interests, which matches our objective.…”

Section: Related Wordsmentioning

confidence: 99%

“…We use the approach proposed by Eda et al [13] because it finds related words based on co-occurrence among users with similar interests, which matches our objective. However, they assume that users can be identified on their system.…”

Section: Related Wordsmentioning

confidence: 99%

“…In this study, we use X = 80, which is the same as [13]; however, this is an arbitrary value. Let P(t y |d x ) (resp.…”

Section: Related Wordsmentioning

confidence: 99%

“…Our approach to obtain related terms automatically is based on the approach proposed by Eda et al [13]; however we modify their approach to avoid user identification. The proposed strategy to detect related terms involves the relationships, which we refer to as relativity, between a pair of words.…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Personalized Web Page Recommendation Based on Preference Footprint to Browsed Pages

Serizawa

Kamei

Hayashi³

et al. 2016

IEICE Trans. Inf. & Syst.

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SUMMARYIn this paper, a new scheme for personalized web page recommendation using multi-user search engine query information is proposed. Our contribution is a scheme that improves the accuracy of personalization for various types of contents (e.g., documents, images and music) without increasing user burden. The proposed scheme combines "preference footprints" for browsed pages with collaborative filtering. We acquire user interest using words that are relevant to queries submitted by users, attach all user interests to a page as a footprint when it is browsed, and evaluate the relevance of web pages in relation to words in footprints. The performance of the scheme is evaluated experimentally. The results indicate that the proposed scheme improves the precision and recall of previous schemes by 1%-24% and 80%-107%, respectively.

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Section: Related Wordsmentioning

confidence: 99%

Section: Related Wordsmentioning

confidence: 99%

“…In this study, we use X = 80, which is the same as [13]; however, this is an arbitrary value. Let P(t y |d x ) (resp.…”

Section: Related Wordsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Personalized Web Page Recommendation Based on Preference Footprint to Browsed Pages

Serizawa

Kamei

Hayashi³

et al. 2016

IEICE Trans. Inf. & Syst.

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show abstract

“…Several approaches have been introduced, dealing with the construction of hierarchical structures of tags [18,19], generating user profiles based on collaborative tagging [9,20], and collaborative filtering in general [21].…”

Section: Related Workmentioning

confidence: 99%

Start Trusting Strangers? Bootstrapping and Prediction of Trust

Skopik

Schall

Dustdar

2009

Lecture Notes in Computer Science

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Abstract. Web-based environments typically span interactions between humans and software services. The management and automatic calculation of trust are among the key challenges of the future service-oriented Web. Trust management systems in large-scale systems, for example, social networks or service-oriented environments determine trust between actors by either collecting manual feedback ratings or by mining their interactions. However, most systems do not support bootstrapping of trust. In this paper we propose techniques and algorithms enabling the prediction of trust even when only few or no ratings have been collected or interactions captured. We introduce the concepts of mirroring and teleportation of trust facilitating the evolution of cooperation between various actors. We assume a user-centric environment, where actors express their opinions, interests and expertises by selecting and tagging resources. We take this information to construct tagging profiles, whose similarities are utilized to predict potential trust relations. Most existing similarity approaches split the three-dimensional relations between users, resources, and tags, to create and compare general tagging profiles directly. Instead, our algorithms consider (i) the understandings and interests of actors in tailored subsets of resources and (ii) the similarity of resources from a certain actor-group's point of view.

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Crowdsourcing Task Marketplaces

Schall

2012

SpringerBriefs in Computer Science

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Locally Expandable Allocation of Folksonomy Tags in a Directed Acyclic Graph

Cited by 9 publications

References 11 publications

Personalized Web Page Recommendation Based on Preference Footprint to Browsed Pages

Personalized Web Page Recommendation Based on Preference Footprint to Browsed Pages

Start Trusting Strangers? Bootstrapping and Prediction of Trust

Crowdsourcing Task Marketplaces

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