dc.contributor.author | Tao, Ye | |
dc.contributor.author | Wang, Can | |
dc.contributor.author | Yao, Lina | |
dc.contributor.author | Li, Weimin | |
dc.contributor.author | Yu, Yonghong | |
dc.date.accessioned | 2020-11-13T06:23:30Z | |
dc.date.available | 2020-11-13T06:23:30Z | |
dc.date.issued | 2020 | |
dc.identifier.isbn | 978-1-7281-6926-2 | |
dc.identifier.doi | 10.1109/ijcnn48605.2020.9207708 | |
dc.identifier.uri | http://hdl.handle.net/10072/399282 | |
dc.description.abstract | Recommendation system plays an important role in online web applications. Sequential recommender further models user short-term preference through exploiting information from latest user-item interaction history. Most of the sequential recommendation methods neglect the importance of ever-changing item popularity. We propose the model from the intuition that items with most user interactions may be popular in the past but could go out of fashion in recent days. To this end, this paper proposes a novel sequential recommendation approach dubbed TRec, TRec learns item trend information from implicit user interaction history and incorporates item trend information into next item recommendation tasks. Then a self-attention mechanism is used to learn better node representation. Our model is trained via pairwise rank-based optimization. We conduct extensive experiments with seven baseline methods on four benchmark datasets, The empirical result shows our approach outperforms other state-of-the-art methods while maintains a superiorly low runtime cost. Our study demonstrates the importance of item trend information in recommendation system designs, and our method also possesses great efficiency which enables it to be practical in real-world scenarios. | |
dc.description.peerreviewed | Yes | |
dc.description.sponsorship | Griffith University | |
dc.publisher | IEEE | |
dc.relation.ispartofconferencename | 2020 International Joint Conference on Neural Networks (IJCNN 2020) | |
dc.relation.ispartofconferencetitle | 2020 International Joint Conference on Neural Networks (IJCNN) | |
dc.relation.ispartofdatefrom | 2020-07-19 | |
dc.relation.ispartofdateto | 2020-07-24 | |
dc.relation.ispartoflocation | Glasgow, United Kingdom | |
dc.subject.fieldofresearch | Neural networks | |
dc.subject.fieldofresearchcode | 461104 | |
dc.title | TRec: Sequential Recommender Based On Latent Item Trend Information | |
dc.type | Conference output | |
dc.type.description | E1 - Conferences | |
dcterms.bibliographicCitation | Tao, Y; Wang, C; Yao, L; Li, W; Yu, Y, TRec: Sequential Recommender Based On Latent Item Trend Information, 2020 International Joint Conference on Neural Networks (IJCNN), 2020 | |
dc.date.updated | 2020-11-13T06:21:03Z | |
dc.description.version | Accepted Manuscript (AM) | |
gro.rights.copyright | © 2020 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. | |
gro.hasfulltext | Full Text | |
gro.griffith.author | Wang, Can | |