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- W2892647029 abstract "This paper is a proposal for efficient tag recommendation to a target user in social tagging systems by generation of a user similarity list and reranking of the list. The methodology involves a user similarity list generated for every target user based on the shared personality traits or the Big5 values obtained from a mandatory one-time questionnaire during the profile creation in the social tagging system. Different users are added to the neighborhood of similar users for the target user based on the Euclidean distance between the big5 values of these users and that of the target user. The User-Item matrix is replaced by a User-Item-Tag matrix with tags for the items used by the different users forming the 3rd dimension. The tags from the top k neighbors from the similar user neighborhood of a target user for a particular resource will be recommended to the target user. The idea is to maintain a ranked list of neighbors based on their similarity score (Euclidean distance) where the position of the neighbors in the list denotes the level of similarity the neighbor shares with the target user. It is essential to maintain the ranked lists of neighbors and perform any reranking when necessary as a fairly dissimilar user at the bottom of the neighborhood list may still respond in the same way to a context and use the same tag as the target user in more than one instance. This requires revising the rank of this fairly dissimilar user up the neighborhood list to reflect the change. This paper suggests an efficient method to perform such reranking based on logarithmic and exponential scale." @default.
- W2892647029 created "2018-10-05" @default.
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- W2892647029 date "2018-01-01" @default.
- W2892647029 modified "2023-10-06" @default.
- W2892647029 title "Personality-Based User Similarity List and Reranking for Tag Recommendation in Social Tagging Systems" @default.
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- W2892647029 doi "https://doi.org/10.1007/978-981-13-1936-5_43" @default.
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