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Home / Proceedings / Proceedings of the AAAI Conference on Artificial Intelligence / EAAI-20

Discrete Social Recommendation

February 1, 2023

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Authors

Chenghao Liu

Singapore Management University


Xin Wang

Tsinghua University


Tao Lu

Zhejiang University


Wenwu Zhu

Tsinghua University


Jianling Sun

Zhejiang University


Steven Hoi

Singapore Management University


DOI:

10.1609/aaai.v33i01.3301208


Abstract:

Social recommendation, which aims at improving the performance of traditional recommender systems by considering social information, has attracted broad range of interests. As one of the most widely used methods, matrix factorization typically uses continuous vectors to represent user/item latent features. However, the large volume of user/item latent features results in expensive storage and computation cost, particularly on terminal user devices where the computation resource to operate model is very limited. Thus when taking extra social information into account, precisely extracting K most relevant items for a given user from massive candidates tends to consume even more time and memory, which imposes formidable challenges for efficient and accurate recommendations. A promising way is to simply binarize the latent features (obtained in the training phase) and then compute the relevance score through Hamming distance. However, such a two-stage hashing based learning procedure is not capable of preserving the original data geometry in the real-value space and may result in a severe quantization loss. To address these issues, this work proposes a novel discrete social recommendation (DSR) method which learns binary codes in a unified framework for users and items, considering social information. We further put the balanced and uncorrelated constraints on the objective to ensure the learned binary codes can be informative yet compact, and finally develop an efficient optimization algorithm to estimate the model parameters. Extensive experiments on three real-world datasets demonstrate that DSR runs nearly 5 times faster and consumes only with 1/37 of its real-value competitor’s memory usage at the cost of almost no loss in accuracy.

Topics: AAAI

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HOW TO CITE:

Chenghao Liu||Xin Wang||Tao Lu||Wenwu Zhu||Jianling Sun||Steven Hoi Discrete Social Recommendation Proceedings of the AAAI Conference on Artificial Intelligence (2019) 208-215.

Chenghao Liu||Xin Wang||Tao Lu||Wenwu Zhu||Jianling Sun||Steven Hoi Discrete Social Recommendation AAAI 2019, 208-215.

Chenghao Liu||Xin Wang||Tao Lu||Wenwu Zhu||Jianling Sun||Steven Hoi (2019). Discrete Social Recommendation. Proceedings of the AAAI Conference on Artificial Intelligence, 208-215.

Chenghao Liu||Xin Wang||Tao Lu||Wenwu Zhu||Jianling Sun||Steven Hoi. Discrete Social Recommendation. Proceedings of the AAAI Conference on Artificial Intelligence 2019 p.208-215.

Chenghao Liu||Xin Wang||Tao Lu||Wenwu Zhu||Jianling Sun||Steven Hoi. 2019. Discrete Social Recommendation. "Proceedings of the AAAI Conference on Artificial Intelligence". 208-215.

Chenghao Liu||Xin Wang||Tao Lu||Wenwu Zhu||Jianling Sun||Steven Hoi. (2019) "Discrete Social Recommendation", Proceedings of the AAAI Conference on Artificial Intelligence, p.208-215

Chenghao Liu||Xin Wang||Tao Lu||Wenwu Zhu||Jianling Sun||Steven Hoi, "Discrete Social Recommendation", AAAI, p.208-215, 2019.

Chenghao Liu||Xin Wang||Tao Lu||Wenwu Zhu||Jianling Sun||Steven Hoi. "Discrete Social Recommendation". Proceedings of the AAAI Conference on Artificial Intelligence, 2019, p.208-215.

Chenghao Liu||Xin Wang||Tao Lu||Wenwu Zhu||Jianling Sun||Steven Hoi. "Discrete Social Recommendation". Proceedings of the AAAI Conference on Artificial Intelligence, (2019): 208-215.

Chenghao Liu||Xin Wang||Tao Lu||Wenwu Zhu||Jianling Sun||Steven Hoi. Discrete Social Recommendation. AAAI[Internet]. 2019[cited 2023]; 208-215.


ISSN: 2374-3468


Published by AAAI Press, Palo Alto, California USA
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