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Home / Proceedings / Proceedings of the AAAI Conference on Artificial Intelligence, 32

Personalized Time-Aware Tag Recommendation

March 15, 2023

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Published Date: 2018-02-08

Registration: ISSN 2374-3468 (Online) ISSN 2159-5399 (Print)

Copyright: Published by AAAI Press, Palo Alto, California USA Copyright © 2018, Association for the Advancement of Artificial Intelligence All Rights Reserved.

Authors

Keqiang Wang

East China Normal University


Yuanyuan Jin

East China Normal University


Haofen Wang

Shenzhen Gowild Robotics Co. Ltd


Hongwei Peng

East China Normal University


Xiaoling Wang

East China Normal University


DOI:

10.1609/aaai.v32i1.11259


Abstract:

Personalized tag recommender systems suggest a list of tags to a user when he or she wants to annotate an item. They utilize users’ preferences and the features of items. Tensorfactorization techniques have been widely used in tag recommendation. Given the user-item pair, although the classic PITF (Pairwise Interaction Tensor Factorization) explicitly models the pairwise interactions among users, items and tags, it overlooks users’ short-term interests and suffers from data sparsity. On the other hand, given the user-item-time triple, time-aware approaches like BLL (Base-Level Learning) utilize the time effect to capture the temporal dynamics and the most popular tags on items to handle cold start situation of new users. However, it works only on individual level and the target resource level, which cannot find users’ potential interests. In this paper, we propose an unified tag recommendation approach by considering both time awareness and personalization aspects, which extends PITF by adding weightsto user-tag interaction and item-tag interaction respectively. Compared to PITF, our proposed model can depict temporal factor by temporal weights and relieve data sparsity problem by referencing the most popular tags on items. Further, our model brings collaborative filtering (CF) to time-aware models, which can mine information from global data and help improving the ability of recommending new tags. Different from the power-form functions used in the existing time aware recommendation models, we use the Hawkes process with the exponential intensity function to improve the model’s efficiency. The experimental results show that our proposed model outperforms the state of the art tag recommendation methods in accuracy and has better ability to recommend new tags.

Topics: AAAI

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HOW TO CITE:

Keqiang Wang||Yuanyuan Jin||Haofen Wang||Hongwei Peng||Xiaoling Wang Personalized Time-Aware Tag Recommendation Proceedings of the AAAI Conference on Artificial Intelligence, 32 (2018) .

Keqiang Wang||Yuanyuan Jin||Haofen Wang||Hongwei Peng||Xiaoling Wang Personalized Time-Aware Tag Recommendation AAAI 2018, .

Keqiang Wang||Yuanyuan Jin||Haofen Wang||Hongwei Peng||Xiaoling Wang (2018). Personalized Time-Aware Tag Recommendation. Proceedings of the AAAI Conference on Artificial Intelligence, 32, .

Keqiang Wang||Yuanyuan Jin||Haofen Wang||Hongwei Peng||Xiaoling Wang. Personalized Time-Aware Tag Recommendation. Proceedings of the AAAI Conference on Artificial Intelligence, 32 2018 p..

Keqiang Wang||Yuanyuan Jin||Haofen Wang||Hongwei Peng||Xiaoling Wang. 2018. Personalized Time-Aware Tag Recommendation. "Proceedings of the AAAI Conference on Artificial Intelligence, 32". .

Keqiang Wang||Yuanyuan Jin||Haofen Wang||Hongwei Peng||Xiaoling Wang. (2018) "Personalized Time-Aware Tag Recommendation", Proceedings of the AAAI Conference on Artificial Intelligence, 32, p.

Keqiang Wang||Yuanyuan Jin||Haofen Wang||Hongwei Peng||Xiaoling Wang, "Personalized Time-Aware Tag Recommendation", AAAI, p., 2018.

Keqiang Wang||Yuanyuan Jin||Haofen Wang||Hongwei Peng||Xiaoling Wang. "Personalized Time-Aware Tag Recommendation". Proceedings of the AAAI Conference on Artificial Intelligence, 32, 2018, p..

Keqiang Wang||Yuanyuan Jin||Haofen Wang||Hongwei Peng||Xiaoling Wang. "Personalized Time-Aware Tag Recommendation". Proceedings of the AAAI Conference on Artificial Intelligence, 32, (2018): .

Keqiang Wang||Yuanyuan Jin||Haofen Wang||Hongwei Peng||Xiaoling Wang. Personalized Time-Aware Tag Recommendation. AAAI[Internet]. 2018[cited 2023]; .


ISSN: 2374-3468


Published by AAAI Press, Palo Alto, California USA
Copyright 2022, Association for the Advancement of
Artificial Intelligence 1900 Embarcadero Road, Suite
101, Palo Alto, California 94303 All Rights Reserved

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