Trust-Enhanced Personalized Knowledge Sharing Via Search Inputs, Social Bookmarks and Tags
Date Issued
2009
Date
2009
Author(s)
Lin, Fan-Chieh
Abstract
Information overload has been notoriously posing tremendous obstacles to more efficient and effective (online) resource utilization. Under the dominance of information pull, in which users have to actively find what they need, services being able to push what we would be interested in or in demand of are often expected.n this paper, we propose an approach, with which social tags and keywords extracted from search inputs are coordinated in terms of VSM to profile users, online contents/documents, and the textual terms themselves. The system stores pairs of users’ search inputs and (Internet) bookmarks selected from the search results, and treats the resulting pairs as valuable resources that are worthy of recommendation. By computing user-user, user-document, and user-tag relatedness values from VSM-based profiles, the system aims to achieve personalized knowledge sharing by recommending the previously-mentioned search input-output pairs that are expected to be related to and catering for users’ needs.n addition to (profile) similarity, we also take interpersonal “trust” into consideration while defining “relateness.” By adopting an innovative contact management mechanism—People Tagging, we allow users to express their preferences for recommendations and their willingness to trust others in specific domains, therefore making recommendations more relevant.astly, based on commonly-seen social network function, for each individual we weight tagging records from people accepted as friends/buddies and provide customized tag relatedness based on an asymmetric tag co-occurrence measure. With these features we expect to achieve higher level of personalization.
Subjects
Knowledge Sharing
Personalization
VSM
Search
Social Tagging and Bookmarking
Hybrid Recommendation (Algorithm)
People Tagging
Social Network
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