{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,16]],"date-time":"2025-10-16T06:58:28Z","timestamp":1760597908532},"publisher-location":"California","reference-count":0,"publisher":"International Joint Conferences on Artificial Intelligence Organization","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2019,8]]},"abstract":"<jats:p>On E-commerce platforms, understanding the relationships (e.g., substitute and complement) among products from user's explicit feedback, such as users' online transactions, is of great importance to boost extra sales. However, the significance of such relationships is usually neglected by existing recommender systems. In this paper, we propose a semisupervised deep embedding model, namely, Substitute Products Embedding Model (SPEM), which models the substitutable relationships between products by preserving the second-order proximity, negative first-order proximity and semantic similarity in a product co-purchasing graph based on user's purchasing behaviours. With SPEM, the learned representations of two substitutable products align closely in the latent embedding space. Extensive experiments on real-world datasets are conducted, and the results verify that our model outperforms state-of-the-art baselines.<\/jats:p>","DOI":"10.24963\/ijcai.2019\/598","type":"proceedings-article","created":{"date-parts":[[2019,7,28]],"date-time":"2019-07-28T03:46:05Z","timestamp":1564285565000},"page":"4306-4312","source":"Crossref","is-referenced-by-count":14,"title":["Inferring Substitutable Products with Deep Network Embedding"],"prefix":"10.24963","author":[{"given":"Shijie","family":"Zhang","sequence":"first","affiliation":[{"name":"School of Information Technology and Electrical Engineering, The University of Queensland, Australia"}]},{"given":"Hongzhi","family":"Yin","sequence":"additional","affiliation":[{"name":"School of Information Technology and Electrical Engineering, The University of Queensland, Australia"}]},{"given":"Qinyong","family":"Wang","sequence":"additional","affiliation":[{"name":"School of Information Technology and Electrical Engineering, The University of Queensland, Australia"}]},{"given":"Tong","family":"Chen","sequence":"additional","affiliation":[{"name":"School of Information Technology and Electrical Engineering, The University of Queensland, Australia"}]},{"given":"Hongxu","family":"Chen","sequence":"additional","affiliation":[{"name":"School of Information Technology and Electrical Engineering, The University of Queensland, Australia"}]},{"given":"Quoc Viet Hung","family":"Nguyen","sequence":"additional","affiliation":[{"name":"School of Information and Communication Technology, Griffith University, Australia"}]}],"member":"10584","event":{"number":"28","sponsor":["International Joint Conferences on Artificial Intelligence Organization (IJCAI)"],"acronym":"IJCAI-2019","name":"Twenty-Eighth International Joint Conference on Artificial Intelligence {IJCAI-19}","start":{"date-parts":[[2019,8,10]]},"theme":"Artificial Intelligence","location":"Macao, China","end":{"date-parts":[[2019,8,16]]}},"container-title":["Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence"],"original-title":[],"deposited":{"date-parts":[[2019,7,28]],"date-time":"2019-07-28T03:50:23Z","timestamp":1564285823000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.ijcai.org\/proceedings\/2019\/598"}},"subtitle":[],"proceedings-subject":"Artificial Intelligence Research Articles","short-title":[],"issued":{"date-parts":[[2019,8]]},"references-count":0,"URL":"https:\/\/doi.org\/10.24963\/ijcai.2019\/598","relation":{},"subject":[],"published":{"date-parts":[[2019,8]]}}}