{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,29]],"date-time":"2026-09-29T04:54:56Z","timestamp":1790657696112,"version":"4.1.0"},"reference-count":33,"publisher":"SAGE Publications","issue":"3","license":[{"start":{"date-parts":[[2024,1,16]],"date-time":"2024-01-16T00:00:00Z","timestamp":1705363200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/journals.sagepub.com\/page\/policies\/text-and-data-mining-license"}],"content-domain":{"domain":["journals.sagepub.com"],"crossmark-restriction":true},"short-container-title":["Journal of Intelligent &amp; Fuzzy Systems: Applications in Engineering and Technology"],"published-print":{"date-parts":[[2024,3,5]]},"abstract":"<jats:p>Conversational recommender systems use natural language conversations to elicit user preferences and recommend items proactively. Existing methods based on graph neural networks have been proven to be effective in exploiting knowledge graphs. However, node positions are often treated as constants, which leads to the neglect of graph connectivity due to fuzzy processing. In addition, although the transformer has significant advantages in understanding the text, its secondary computational complexity may be incapable when dealing with long texts. In order to solve these problems, we propose an additive positional conversational recommender model called APCR. This model converts the pair product of transformer into a linear operation, and uses the Laplacian eigenvector to build a location graph. The extended graph neural network captures the topology structure of the location knowledge graph. Specifically, we design an encoder based on additive attention to break through the bottleneck of long text. Furthermore, we develop a recommendation model based on a positional graph neural network to match items with dialogue context, thereby capturing the graph topology. Extensive experiments on the REDIAL dataset show significant improvements in our proposed model over the state-of-the-art methods in recommendation and dialogue generation evaluations.<\/jats:p>","DOI":"10.3233\/jifs-230905","type":"journal-article","created":{"date-parts":[[2024,1,16]],"date-time":"2024-01-16T11:05:41Z","timestamp":1705403141000},"page":"6491-6503","update-policy":"https:\/\/doi.org\/10.1177\/sage-journals-update-policy","source":"Crossref","is-referenced-by-count":1,"title":["Conversational recommender based on additive attention and positional encoding"],"prefix":"10.1177","volume":"46","author":[{"given":"Yihao","family":"Zhang","sequence":"first","affiliation":[{"name":"Chongqing University of Technology","place":["China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yuhao","family":"Wang","sequence":"additional","affiliation":[{"name":"Chongqing University of Technology","place":["China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Pengxiang","family":"Lan","sequence":"additional","affiliation":[{"name":"Chongqing University of Technology","place":["China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Haoran","family":"Xiang","sequence":"additional","affiliation":[{"name":"Chongqing University of Technology","place":["China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Junlin","family":"Zhu","sequence":"additional","affiliation":[{"name":"Chongqing University of Technology","place":["China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Meng","family":"Yuan","sequence":"additional","affiliation":[{"name":"Beihang University","place":["China"]}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"179","published-online":{"date-parts":[[2024,1,16]]},"reference":[{"key":"e_1_3_1_2_2","doi-asserted-by":"crossref","unstructured":"LeiW. JinX. KanM.Y. RenZ. HeX. YinD. Se quicity: Simplifying taskoriented dialogue systems with single sequencetosequence architectures Meeting of the Association for Computational Linguistics 2018.","DOI":"10.18653\/v1\/P18-1133"},{"key":"e_1_3_1_3_2","unstructured":"WuC.S. MadottoA. HosseiniAslE. XiongC. SocherR. FungP. Transferable multidomain state genera tor for taskoriented dialogue systems arXiv preprint arXiv:1905.08743 2019."},{"key":"e_1_3_1_4_2","doi-asserted-by":"crossref","unstructured":"BudzianowskiP. Vuli\u0107I. Hello it\u2019s gpt2 \u2013how can i help you? towards the use of pretrained language models for task oriented dialogue systems arXiv: Computation and Language 2019.","DOI":"10.18653\/v1\/D19-5602"},{"key":"e_1_3_1_5_2","doi-asserted-by":"crossref","unstructured":"PeiJ. RenP. de RijkeM. A cooperative memory net work for personalized taskoriented dialogue systems with in complete user profiles in Proceedings of the Web Conference 2021 2021 pp. 1552\u20131561.","DOI":"10.1145\/3442381.3449843"},{"key":"e_1_3_1_6_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.specom.2020.09.005"},{"key":"e_1_3_1_7_2","unstructured":"LiR. KahouS.E. SchulzH. MichalskiV. CharlinL. PalC. Towards deep conversational recommendations Neural Information Processing Systems 2018."},{"key":"e_1_3_1_8_2","doi-asserted-by":"crossref","unstructured":"RenX. YinH. ChenT. WangH. HungN.Q.V. HuangZ. ZhangX. Crsal: Conversational recommender systems with adversarial learning ACM Transactions on Information Systems 2020.","DOI":"10.1145\/3394592"},{"key":"e_1_3_1_9_2","doi-asserted-by":"crossref","unstructured":"DengY. LiY. SunF. DingB. LamW. Unified conver sational recommendation policy learning via graphbased rein forcement learning International ACM Sigir Conference on Research and Development in Information Retrieval 2021.","DOI":"10.1145\/3404835.3462913"},{"key":"e_1_3_1_10_2","doi-asserted-by":"crossref","unstructured":"ZouJ. ChenY. KanoulasE. Towards questionbased recommender systems International ACMSigir Conference on Research and Development in Information Retrieval 2020.","DOI":"10.1145\/3397271.3401180"},{"key":"e_1_3_1_11_2","unstructured":"LiS. LeiW. WuQ. HeX. JiangP. ChuaT.S. Seam lessly unifying attributes and items: Conversational recommen dation for coldstart users arXiv: Information Retrieval 2020."},{"key":"e_1_3_1_12_2","doi-asserted-by":"crossref","unstructured":"RenX. YinH. ChenT. WangH. HuangZ. ZhengK. Learning to ask appropriate questions in conversational recom mendation International ACM Sigir Conference on Research and Development in Information Retrieval 2021.","DOI":"10.1145\/3404835.3462839"},{"key":"e_1_3_1_13_2","doi-asserted-by":"crossref","unstructured":"XuK. YangJ. XuJ. GaoS. GuoJ. WenJ.R. Adapting user preference to online feedback in multiround conversational recommendation Web Search and Data Mining 2021.","DOI":"10.1145\/3437963.3441791"},{"key":"e_1_3_1_14_2","doi-asserted-by":"crossref","unstructured":"ChenQ. LinJ. ZhangY. DingM. TangJ. CenY. YangH. Towards knowledgebased recommender dialog system Empirical Methods in Natural Language Processing 2019.","DOI":"10.18653\/v1\/D19-1189"},{"key":"e_1_3_1_15_2","unstructured":"YuJ. WenJ.R. ZhouK. BianS. ZhouY. ZhaoW.X. Improving conversational recommender systems via knowledge graph based semantic fusion Knowledge Discovery and Data Mining 2020."},{"key":"e_1_3_1_16_2","unstructured":"HuH. XuC. GengX. HeY. ChenY. MiaoJ. LiangZ. JiangD. LiangF. Learning neural templates for recom mender dialogue system Empirical Methods in Natural Language Processing 2021."},{"key":"e_1_3_1_17_2","doi-asserted-by":"crossref","unstructured":"LiS. XieR. ZhuY. AoX. ZhuangF. HeQ. User centric conversational recommendation with multiaspect user modeling 2022.","DOI":"10.1145\/3477495.3532074"},{"key":"e_1_3_1_18_2","doi-asserted-by":"crossref","unstructured":"RenZ. TianZ. LiD. RenP. YangL. XinX. LiangH. de RijkeM. ChenZ. Variational reasoning about user preferences for conversational recommendation 2022.","DOI":"10.1145\/3477495.3532077"},{"key":"e_1_3_1_19_2","doi-asserted-by":"crossref","unstructured":"LiangZ. HuH. XuC. MiaoJ. HeY. ChenY. GengX. LiangF. JiangD. Learning neural templates for recom mender dialogue system Empirical Methods in Natural Language Processing 2021.","DOI":"10.18653\/v1\/2021.emnlp-main.617"},{"key":"e_1_3_1_20_2","doi-asserted-by":"crossref","unstructured":"ZhouK. ZhaoW.X. BianS. ZhouY. WenJ.R. YuJ. Improving conversational recommender systems via knowl edge graph based semantic fusion Knowledge Discovery and Data Mining 2020.","DOI":"10.1145\/3394486.3403143"},{"key":"e_1_3_1_21_2","doi-asserted-by":"crossref","unstructured":"SpeerR. ChinJ. HavasiC. Conceptnet 5.5: An open multilingual graph of general knowledge National Conference on Artificial Intelligence 2016.","DOI":"10.1609\/aaai.v31i1.11164"},{"key":"e_1_3_1_22_2","unstructured":"VaswaniA. BrainG. ShazeerN. ParmarN. UszkoreitJ. JonesL. Gomez.A. \u0141ukasz Kaiser Attention is all you need 2022."},{"key":"e_1_3_1_23_2","doi-asserted-by":"crossref","unstructured":"WangH. WangN. YeungD.Y. Collaborative deep learning for recommender systems Knowledge Discovery and Data Mining 2014.","DOI":"10.1145\/2783258.2783273"},{"key":"e_1_3_1_24_2","doi-asserted-by":"crossref","unstructured":"SerbanI.V. SordoniA. LoweR. CharlinL. PineauJ. CourvilleA. BengioY. A hierarchical latent variable encoderdecoder model for generating dialogues Salon des Re fuses 2016.","DOI":"10.1609\/aaai.v31i1.10983"},{"key":"e_1_3_1_25_2","unstructured":"DwivediV.P. JoshiC.K. LaurentT. BengioY. BressonX. Benchmarking graph neural networks arXiv: Learning 2020."},{"key":"e_1_3_1_26_2","doi-asserted-by":"crossref","unstructured":"ZhengG. ZhangF. ZhengZ. XiangY. YuanN.J. XieX. LiZ. Drn: A deep reinforcement learning framework for news recommendation The Web Conference 2018.","DOI":"10.1145\/3178876.3185994"},{"key":"e_1_3_1_27_2","doi-asserted-by":"crossref","unstructured":"WangT. ShiX. ShangM. Diversityaware topn recom mendation: A deep reinforcement learning way CCF Conference on Big Data 2020.","DOI":"10.1007\/978-981-16-0705-9_16"},{"key":"e_1_3_1_28_2","unstructured":"RadfordA. NarasimhanK. SalimansT. SutskeverI. Improving language understanding by generative pretraining 2022."},{"key":"e_1_3_1_29_2","doi-asserted-by":"crossref","unstructured":"KazemiS. Dynamic graph neural networks 2022","DOI":"10.1007\/978-981-16-6054-2_15"},{"key":"e_1_3_1_30_2","doi-asserted-by":"crossref","unstructured":"TangJ. LiaoR. Graph neural networks for node classi fication Graph Neural Networks: Foundations Frontiers and Applications 2022.","DOI":"10.1007\/978-981-16-6054-2_4"},{"key":"e_1_3_1_31_2","doi-asserted-by":"crossref","unstructured":"GeneraleA. BlumeT. CochezM. Scaling rgcn training with graph summarization The Web Conference 2022.","DOI":"10.1145\/3487553.3524719"},{"key":"e_1_3_1_32_2","doi-asserted-by":"crossref","unstructured":"ChenZ. HuangK. WuL. ZhongZ. JiaoZ. Relational graph convolutional network for textminingbased accident causal classification Applied Sciences 2022.","DOI":"10.3390\/app12052482"},{"key":"e_1_3_1_33_2","doi-asserted-by":"crossref","unstructured":"ZhengW. YinL. ChenX. MaZ. LiuS. YangB. Knowledge base graph embedding module design for visual question answering model Pattern Recognition 2021.","DOI":"10.1016\/j.patcog.2021.108153"},{"key":"e_1_3_1_34_2","doi-asserted-by":"crossref","unstructured":"FirsanovaV. Transformer models for question answering on autism spectrum disorder qa dataset Communications in Computer and Information Science 2022.","DOI":"10.1007\/978-3-030-93715-7_9"}],"container-title":["Journal of Intelligent &amp; Fuzzy Systems: Applications in Engineering and Technology"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/journals.sagepub.com\/doi\/pdf\/10.3233\/JIFS-230905","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/journals.sagepub.com\/doi\/full-xml\/10.3233\/JIFS-230905","content-type":"application\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/journals.sagepub.com\/doi\/pdf\/10.3233\/JIFS-230905","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,9,29]],"date-time":"2026-09-29T03:57:58Z","timestamp":1790654278000},"score":1,"resource":{"primary":{"URL":"https:\/\/journals.sagepub.com\/doi\/10.3233\/JIFS-230905"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,1,16]]},"references-count":33,"journal-issue":{"issue":"3","published-print":{"date-parts":[[2024,3,5]]}},"alternative-id":["10.3233\/JIFS-230905"],"URL":"https:\/\/doi.org\/10.3233\/jifs-230905","relation":{},"ISSN":["1064-1246","1875-8967"],"issn-type":[{"value":"1064-1246","type":"print"},{"value":"1875-8967","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,1,16]]}}}