@inproceedings{f0406f5009754d51af8b4ca368997413,
title = "AMBR: boosting the performance of personalized recommendation via learning from multi-behavior data",
abstract = "The performance of personalized recommendation can be further improved by exploiting multiple user behaviors (e.g., browsing, adding-to-cart, product purchasing) to predict items of user interests. However, the challenge lies in how to accurately model the relations among multiple user behaviors. The commonly adopted cascade relation over-simplifies the problem and cannot model the real user behavior patterns. In this paper, we propose a novel multi-behavior recommendation algorithm called AMBR (Attentive Multi-Behavior Recommendation), which can well capture the complicated relations among multiple behaviors. AMBR integrates the representation learning module and the matching function learning module into one framework. By utilizing the modern neural network techniques, AMBR is more flexible in modeling the relations of multiple behaviors without presuming a fixed cascade relation. Finally, we also conduct a set of experiments based on two real-world datasets, and the results show that our AMBR algorithm significantly outperforms other state-of-the-art algorithms by over 8.6%, 9.3% in terms of HR and NDCG.",
keywords = "Multi-behavior data, Neural networks, Personalized recommendation",
author = "Chen Wang and Shilu Lin and Zhicong Zhong and Yipeng Zhou and Di Wu",
year = "2020",
doi = "10.1007/978-3-030-63836-8_33",
language = "English",
isbn = "9783030638351",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer, Springer Nature",
pages = "395--406",
editor = "Haiqin Yang and Kitsuchart Pasupa and Leung, {Andrew Chi-Sing} and Kwok, {James T.} and Chan, {Jonathan H.} and Irwin King",
booktitle = "Neural Information Processing - 27th International Conference, ICONIP 2020, Proceedings",
address = "United States",
note = "International Conference on Neural Information Processing (27th : 2020), ICONIP 2020 ; Conference date: 18-11-2020 Through 22-11-2020",
}