{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,1]],"date-time":"2026-05-01T05:53:27Z","timestamp":1777614807631,"version":"3.51.4"},"reference-count":44,"publisher":"National Library of Serbia","issue":"3","license":[{"start":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T00:00:00Z","timestamp":1672531200000},"content-version":"unspecified","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["ComSIS","COMPUT SCI INF SYST","COMPUT SCI INFORM SY","COMPUTER SCI INFORM","COMSIS J"],"published-print":{"date-parts":[[2023]]},"abstract":"<jats:p>This paper addresses the problem of multimedia recommendation that additionally utilizes multimedia data, such as visual and textual modalities of items along with the user-item interaction information. Existing multimedia recommender systems assume that all the non-interacted items of a user have the same degree of negativity, thus regarding them as candidates for negative samples when training the model. However, this paper claims that a user?s non-interacted items do not have the same degree of negativity. We classify these non-interacted items of a user into two kinds of items with different characteristics: unknown and uninteresting items. Then, we propose a novel negative sampling technique that only considers the uninteresting items (i.e., rather than the unknown items) as candidates for negative samples. In addition, we show that using the multiple Bayesian personalized ranking (BPR) losses with both unknown and uninteresting items (i.e., all the noninteracted items) in existing multimedia recommendation methods is very effective in improving recommendation accuracy. By conducting extensive experiments with three real-world datasets, we show the superiority of our ideas. Our ideas can be easily and orthogonally applied to any multimedia recommender systems.<\/jats:p>","DOI":"10.2298\/csis221031041k","type":"journal-article","created":{"date-parts":[[2023,6,12]],"date-time":"2023-06-12T10:52:12Z","timestamp":1686567132000},"page":"933-948","source":"Crossref","is-referenced-by-count":2,"title":["Read between the interactions: Understanding non-interacted items for accurate multimedia recommendation"],"prefix":"10.2298","volume":"20","author":[{"given":"Jiyeon","family":"Kim","sequence":"first","affiliation":[{"name":"Department of Computer Science, Hanyang University Wangsimni-ro, Seongdong-gu, Seoul, Korea"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Taeri","family":"Kim","sequence":"additional","affiliation":[{"name":"Department of Computer Science, Hanyang University Wangsimni-ro, Seongdong-gu, Seoul, Korea"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Sang-Wook","family":"Kim","sequence":"additional","affiliation":[{"name":"Department of Computer Science, Hanyang University Wangsimni-ro, Seongdong-gu, Seoul, Korea"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1078","reference":[{"key":"ref1","doi-asserted-by":"crossref","unstructured":"Albawi, S., Mohammed, T.A., Al-Zawi, S.: Understanding of a convolutional neural network. In: International Conference on Engineering and Technology (ICET). pp. 1-6 (2017)","DOI":"10.1109\/ICEngTechnol.2017.8308186"},{"key":"ref2","doi-asserted-by":"crossref","unstructured":"Bao, Y., Fang, H., Zhang, J.: Topicmf: Simultaneously exploiting ratings and reviews for recommendation. In: AAAI. pp. 2-8 (2014)","DOI":"10.1609\/aaai.v28i1.8715"},{"key":"ref3","doi-asserted-by":"crossref","unstructured":"Chae, D., Kim, J., Chau, D.H., Kim, S.: AR-CF: Augmenting virtual users and items in collaborative filtering for addressing cold-start problems. In: ACM SIGIR. pp. 1251-1260 (2020)","DOI":"10.1145\/3397271.3401038"},{"key":"ref4","doi-asserted-by":"crossref","unstructured":"Chen, X., Chen, H., Xu, H., Zhang, Y., Cao, Y., Qin, Z., Zha, H.: Personalized fashion recommendation with visual explanations based on multimodal attention network: Towards visually explainable recommendation. In: ACM SIGIR. pp. 765-774 (2019)","DOI":"10.1145\/3331184.3331254"},{"key":"ref5","doi-asserted-by":"crossref","unstructured":"Cohen, R., Shalom, O.S., Jannach, D., Amir, A.: A black-box attack model for visually-aware recommender systems. In: ACM WSDM. pp. 94-102 (2021)","DOI":"10.1145\/3437963.3441757"},{"key":"ref6","doi-asserted-by":"crossref","unstructured":"Goldberg, D., Nichols, D.A., Oki, B.M., Terry, D.B.: Using collaborative filtering to weave an information tapestry. Commun. ACM 35(12), 61-70 (1992)","DOI":"10.1145\/138859.138867"},{"key":"ref7","doi-asserted-by":"crossref","unstructured":"Graves, A., Mohamed, A., Hinton, G.E.: Speech recognition with deep recurrent neural networks. In: IEEE ICASSP. pp. 6645-6649 (2013)","DOI":"10.1109\/ICASSP.2013.6638947"},{"key":"ref8","doi-asserted-by":"crossref","unstructured":"He, R., McAuley, J.: VBPR: visual bayesian personalized ranking from implicit feedback. In: AAAI. pp. 144-150 (2016)","DOI":"10.1609\/aaai.v30i1.9973"},{"key":"ref9","doi-asserted-by":"crossref","unstructured":"He, X., Du, X.,Wang, X., Tian, F., Tang, J., Chua, T.: Outer product-based neural collaborative filtering. In: IJCAI. pp. 2227-2233 (2018)","DOI":"10.24963\/ijcai.2018\/308"},{"key":"ref10","unstructured":"Hidasi, B., Karatzoglou, A., Baltrunas, L., Tikk, D.: Session-based recommendations with recurrent neural networks. CoRR abs\/1511.06939 (2015)"},{"key":"ref11","doi-asserted-by":"crossref","unstructured":"Hwang, W., Parc, J., Kim, S., Lee, J., Lee, D.: \u201cTold you I didn\u2019t like it\u201d: Exploiting uninteresting items for effective collaborative filtering. In: IEEE ICDE. pp. 349-360 (2016)","DOI":"10.1109\/ICDE.2016.7498253"},{"key":"ref12","doi-asserted-by":"crossref","unstructured":"Kim, D.H., Park, C., Oh, J., Lee, S., Yu, H.: Convolutional matrix factorization for document context-aware recommendation. In: ACM RecSys. pp. 233-240 (2016)","DOI":"10.1145\/2959100.2959165"},{"key":"ref13","doi-asserted-by":"crossref","unstructured":"Kim, T., Kim, Y., Lee, Y.C., Shin,W., Kim, S.: Is it enough just looking at the title?: Leveraging body text to encrich title words towards accurate news recommendation. In: ACM CIKM. pp. 4138-4142 (2022)","DOI":"10.1145\/3511808.3557619"},{"key":"ref14","doi-asserted-by":"crossref","unstructured":"Kim, T., Lee, Y.C., Shin, K., Kim, S.: Mario:Modality-Aware attention and modalitypreserving decoders for multimedia recommendation. In: ACM CIKM. pp. 993-1002 (2022)","DOI":"10.1145\/3511808.3557387"},{"key":"ref15","doi-asserted-by":"crossref","unstructured":"Ko, Y., Yu, J.S., Bae, H.K., Park, Y., Lee, D., Kim, S.W.: Mascot: A quantization framework for efficient matrix factorization in recommender systems. In: IEEE ICDM. pp. 290-299 (2021)","DOI":"10.1109\/ICDM51629.2021.00039"},{"key":"ref16","doi-asserted-by":"crossref","unstructured":"Kong, T., Kim, T., Jeon, J., Choi, J., Lee, Y.C., Park, N., Kim, S.W.: Linear, or non-linear, that is the question! In: ACM WSDM. pp. 517-525 (2022)","DOI":"10.1145\/3488560.3498501"},{"key":"ref17","doi-asserted-by":"crossref","unstructured":"Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Commun. ACM 60(6), 84-90 (2017)","DOI":"10.1145\/3065386"},{"key":"ref18","doi-asserted-by":"crossref","unstructured":"Lee, J., Hwang, W., Parc, J., Lee, Y., Kim, S., Lee, D.: l-injection: Toward effective collaborative filtering using uninteresting items. IEEE TKDE 31(1), 3-16 (2019)","DOI":"10.1109\/TKDE.2017.2698461"},{"key":"ref19","doi-asserted-by":"crossref","unstructured":"Lee, Y., Kim, S., Lee, D.: gOCCF: Graph-theoretic one-class collaborative filtering based on uninteresting items. In: AAAI. pp. 3448-3456 (2018)","DOI":"10.1609\/aaai.v32i1.11707"},{"key":"ref20","doi-asserted-by":"crossref","unstructured":"Lee, Y.C., Kim, T., Choi, J., He, X., Kim, S.: M-bpr: A novel approach to improving bpr for recommendation with multi-type pair-wise preferences. Information Sciences 547, 255-270 (2021)","DOI":"10.1016\/j.ins.2020.08.027"},{"key":"ref21","doi-asserted-by":"crossref","unstructured":"Lee, Y., Kim, S., Park, S., Xie, X.: How to impute missing ratings?: Claims, solution, and its application to collaborative filtering. In: WWW. pp. 783-792 (2018)","DOI":"10.1145\/3178876.3186159"},{"key":"ref22","doi-asserted-by":"crossref","unstructured":"Lim, H., Lee, Y.C., Lee, J.S., Han, S., Kim, S., Jeong, Y., Kim, C., Kim, J., Han, S., Choi, S., Ko, H., Lee, D., Choi, J., Kim, Y., Bae, H.K., Kim, T., Ahn, J., You, H.S., Kim, S.W.: Airs: A large-scale recommender system at naver news. In: IEEE ICDE. pp. 3386-3398 (2022)","DOI":"10.1109\/ICDE53745.2022.00319"},{"key":"ref23","doi-asserted-by":"crossref","unstructured":"Liu, F., Cheng, Z., Sun, C., Wang, Y., Nie, L., Kankanhalli, M.: User diverse preference modeling by multimodal attentive metric learning. In: ACM MM. pp. 1526-1534 (2019)","DOI":"10.1145\/3343031.3350953"},{"key":"ref24","doi-asserted-by":"crossref","unstructured":"Liu, Q., Wu, S., Wang, L.: Deepstyle: Learning user preferences for visual recommendation. In: ACM SIGIR. pp. 841-844 (2017)","DOI":"10.1145\/3077136.3080658"},{"key":"ref25","doi-asserted-by":"crossref","unstructured":"Niu,W., Caverlee, J., Lu, H.: Neural personalized ranking for image recommendation. In: ACM WSDM. pp. 423-431 (2018)","DOI":"10.1145\/3159652.3159728"},{"key":"ref26","unstructured":"O\u2019Shea, K., Nash, R.: An introduction to convolutional neural networks. arXiv preprint arXiv:1511.08458 (2015)"},{"key":"ref27","doi-asserted-by":"crossref","unstructured":"Pan, R., Zhou, Y., Cao, B., Liu, N.N., Lukose, R.M., Scholz, M., Yang, Q.: One-class collaborative filtering. In: IEEE ICDM. pp. 502-511 (2008)","DOI":"10.1109\/ICDM.2008.16"},{"key":"ref28","doi-asserted-by":"crossref","unstructured":"Park, S.J., Chae, D.K., Bae, H.K., Park, S., Kim, S.W.: Reinforcement learning over sentimentaugmented knowledge graphs towards accurate and explainable recommendation. In: ACM WSDM. pp. 784-793 (2022)","DOI":"10.1145\/3488560.3498515"},{"key":"ref29","doi-asserted-by":"crossref","unstructured":"Reimers, N., Gurevych, I.: Sentence-BERT: Sentence embeddings using siamese bertnetworks. arXiv preprint arXiv:1908.10084 (2019)","DOI":"10.18653\/v1\/D19-1410"},{"key":"ref30","unstructured":"Rendle, S., Freudenthaler, C., Gantner, Z., Schmidt-Thieme, L.: BPR: Bayesian personalized ranking from implicit feedback. In: UAI. pp. 452-461 (2009)"},{"key":"ref31","doi-asserted-by":"crossref","unstructured":"Ricci, F., Rokach, L., Shapira, B. (eds.): Recommender Systems Handbook. Springer (2015)","DOI":"10.1007\/978-1-4899-7637-6"},{"key":"ref32","doi-asserted-by":"crossref","unstructured":"Sarwar, B.M., Karypis, G., Konstan, J.A., Riedl, J.: Item-based collaborative filtering recommendation algorithms. pp. 285-295 (2001)","DOI":"10.1145\/371920.372071"},{"key":"ref33","doi-asserted-by":"crossref","unstructured":"Sedhain, S., Menon, A.K., Sanner, S., Xie, L.: Autorec: Autoencoders meet collaborative filtering. In: WWW. pp. 111-112 (2015)","DOI":"10.1145\/2740908.2742726"},{"key":"ref34","doi-asserted-by":"crossref","unstructured":"Tang, J.,Wang, K.: Personalized top-N sequential recommendation via convolutional sequence embedding. In: ACM WSDM. pp. 565-573 (2018)","DOI":"10.1145\/3159652.3159656"},{"key":"ref35","doi-asserted-by":"crossref","unstructured":"Wang, S., Wang, Y., Tang, J., Shu, K., Ranganath, S., Liu, H.: What your images reveal: Exploiting visual contents for point-of-interest recommendation. In: WWW. pp. 391-400 (2017)","DOI":"10.1145\/3038912.3052638"},{"key":"ref36","doi-asserted-by":"crossref","unstructured":"Wei, Y., Wang, X., Nie, L., He, X., Chua, T.: Graph-refined convolutional network for multimedia recommendation with implicit feedback. In: ACM MM. pp. 3541-3549 (2020)","DOI":"10.1145\/3394171.3413556"},{"key":"ref37","doi-asserted-by":"crossref","unstructured":"Wei, Y.,Wang, X., Nie, L., He, X., Hong, R., Chua, T.: MMGCN: Multi-modal graph convolution network for personalized recommendation of micro-video. In: ACM MM. pp. 1437-1445 (2019)","DOI":"10.1145\/3343031.3351034"},{"key":"ref38","doi-asserted-by":"crossref","unstructured":"Wu, C., Ahmed, A., Beutel, A., Smola, A.J., Jing, H.: Recurrent recommender networks. In: ACM WSDM. pp. 495-503 (2017)","DOI":"10.1145\/3018661.3018689"},{"key":"ref39","doi-asserted-by":"crossref","unstructured":"Xue, H., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI. pp. 3203-3209 (2017)","DOI":"10.24963\/ijcai.2017\/447"},{"key":"ref40","doi-asserted-by":"crossref","unstructured":"Yao, W., He, J., Wang, H., Zhang, Y., Cao, J.: Collaborative topic ranking: Leveraging item meta-data for sparsity reduction. In: AAAI. pp. 374-380 (2015)","DOI":"10.1609\/aaai.v29i1.9151"},{"key":"ref41","doi-asserted-by":"crossref","unstructured":"Ying, H., Chen, L., Xiong, Y., Wu, J.: Collaborative deep ranking: A hybrid pair-wise recommendation algorithm with implicit feedback. In: PAKDD. pp. 555-567 (2016)","DOI":"10.1007\/978-3-319-31750-2_44"},{"key":"ref42","unstructured":"Zagoruyko, S., Komodakis, N.: Paying more attention to attention: Improving the performance of convolutional neural networks via attention transfer. arXiv preprint arXiv:1612.03928 (2016)"},{"key":"ref43","doi-asserted-by":"crossref","unstructured":"Zhang, J., Zhu, Y., Liu, O.,Wu, S.,Wang, S.,Wang, L.: Mining latent structures for multimedia recommendation. In: ACM MM. pp. 3872-3880 (2021)","DOI":"10.1145\/3474085.3475259"},{"key":"ref44","doi-asserted-by":"crossref","unstructured":"Zhang, Y., Ai, Q., Chen, X., Croft, W.B.: Joint representation learning for top-n recommendation with heterogeneous information sources. In: ACM CIKM. pp. 1449-1458 (2017)","DOI":"10.1145\/3132847.3132892"}],"container-title":["Computer Science and Information Systems"],"original-title":[],"language":"en","deposited":{"date-parts":[[2024,7,26]],"date-time":"2024-07-26T08:05:12Z","timestamp":1721981112000},"score":1,"resource":{"primary":{"URL":"https:\/\/doiserbia.nb.rs\/Article.aspx?ID=1820-02142300041K"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023]]},"references-count":44,"journal-issue":{"issue":"3","published-print":{"date-parts":[[2023]]}},"URL":"https:\/\/doi.org\/10.2298\/csis221031041k","relation":{},"ISSN":["1820-0214","2406-1018"],"issn-type":[{"value":"1820-0214","type":"print"},{"value":"2406-1018","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023]]}}}