Abstract
To improve the performance of recommendation models and enhance the interpretability of results, knowledge graph is often used to add rich semantic information to the recommended items. Existing methods either use knowledge graph as an auxiliary information to mine users’ interests, or use knowledge graph to establish relationships between items via their hidden information. However, these methods usually ignore the interaction between users and items. As a result, the hidden relationship between users and items are not well explored in the item representation. To address this issue, we propose an enhancement model to learn item representation based on RippleNet (Eir-Ripp). By mining the users’ historical behavior and user characteristics, users’ preference and the correlation between users and items are extracted to complement the semantic information of items. We conduct extensive experiments to evaluate our proposal on three public data sets. Experimental results show that our model outperforms the baseline methods in terms of an up to 8.8% improvement in the recommendation.
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Notes
- 1.
The results of some baselines in this paper are different from those of the original paper because they are originally implemented using the tensorflow framework. We observe that the tensorflow and pytorch frameworks are different in the implementation of some network layers, the default parameters and random processes.
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Acknowledgment
This work was supported in part by the National Key Research and Development Program of China under Grant No. 2018YFB2100805, National Natural Science Foundation of China under the grant No. 61962017, 61562019, the Key Research and Development Program of Hainan Province under grant No. ZDYF2020008, Natural Science Foundation of Hainan Province under the grant No. 2019CXTD400, 2019RC088, and grants from State Key Laboratory of Marine Resource Utilization in South China Sea and Key Laboratory of Big Data and Smart Services of Hainan Province.
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Li, K., Ye, C., Wang, J. (2023). Eir-Ripp: Enriching Item Representation for Recommendation with Knowledge Graph. In: Li, B., Yue, L., Tao, C., Han, X., Calvanese, D., Amagasa, T. (eds) Web and Big Data. APWeb-WAIM 2022. Lecture Notes in Computer Science, vol 13423. Springer, Cham. https://doi.org/10.1007/978-3-031-25201-3_10
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