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Data Mining and Knowledge Discovery

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17-09-2022

Transfer how much: a fine-grained measure of the knowledge transferability of user behavior sequences in social network

Authors: Nuo Li, Bin Guo, Yan Liu, Yasan Ding, En Xu, Lina Yao, Zhiwen Yu

Published in: Data Mining and Knowledge Discovery | Issue 6/2022

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Abstract

Various user behaviors are providing valuable information for user interest modeling in online information platforms. For the phenomenon that some kinds of behavior data are insufficient to express users’ preferences, therefore, some cross-domain or multi-behavior fusion approaches are proposed to solve it. However, we have not yet understood which behaviors can be transferred and which behaviors can be better transferred to the target behavior. In this paper, we propose a novel knowledge transferability metric, TEMCS (Transfer Entropy with Multi-Concept Semantic), to measure the transferability of knowledge from the source to the target behavior sequence. The new metric not only can obtain the maximum semantics of the sequence based on the multi-concept semantic compression mechanism, but also can further achieve the dynamic information transfer between two sequences by modeling the inter-sequence coupling association founded on the transfer entropy. In particular, TEMCS is model-agnostic, calculation-simple, and requires no training on the source and target behavior sequences. Furthermore, TEMCS can be used as the weight of the difference between the source domain and target domain behavior characteristics, thereby reducing the distribution of the source domain and target domain characteristics and improving the performance of target behavior prediction. Extensive experiments on two real datasets demonstrate that our transferability metric is reasonable and effective, which not only can guide the choice of appropriate source behaviors but also can improve the performance of transfer models and multi-behavior models.

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Our code and data are available at https://github.com/linuo1/TEMCS.

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Title: Transfer how much: a fine-grained measure of the knowledge transferability of user behavior sequences in social network
Authors: Nuo Li
Bin Guo
Yan Liu
Yasan Ding
En Xu
Lina Yao
Zhiwen Yu
Publication date: 17-09-2022
Publisher: Springer US
Published in: Data Mining and Knowledge Discovery / Issue 6/2022
Print ISSN: 1384-5810
Electronic ISSN: 1573-756X
DOI: https://doi.org/10.1007/s10618-022-00857-w

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