{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,2,21]],"date-time":"2025-02-21T02:07:46Z","timestamp":1740103666394,"version":"3.37.3"},"reference-count":15,"publisher":"Wiley","license":[{"start":{"date-parts":[[2020,1,27]],"date-time":"2020-01-27T00:00:00Z","timestamp":1580083200000},"content-version":"unspecified","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["61602282","2016M602181"],"award-info":[{"award-number":["61602282","2016M602181"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100002858","name":"China Postdoctoral Science Foundation","doi-asserted-by":"publisher","award":["61602282","2016M602181"],"award-info":[{"award-number":["61602282","2016M602181"]}],"id":[{"id":"10.13039\/501100002858","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Complexity"],"published-print":{"date-parts":[[2020,1,27]]},"abstract":"<jats:p>Context-aware recommendation (CR) is the task of recommending relevant items by exploring the context information in online systems to alleviate the data sparsity issue of the user-item data. Prior methods mainly studied CR by document-based modeling approaches, that is, making recommendations by additionally utilizing textual data such as reviews, abstracts, or synopses. However, due to the inherent limitation of the bag-of-words model, they cannot effectively utilize contextual information of the documents, which results in a shallow understanding of the documents. Recent works argued that the understanding of document context can be improved by the convolutional neural network (CNN) and proposed the convolutional matrix factorization (ConvMF) to leverage the contextual information of documents to enhance the rating prediction accuracy. However, ConvMF only models the document content context from an item view and assumes users are independent and identically distributed (i.i.d). But in reality, as we often turn to our friends for recommendations, the social relationship and social reviews are two important factors that can change our mind most. Moreover, users are more inclined to interact (buy or click) with the items that they have bought (or clicked). The relationships among items are also important factors that can impact the user\u2019s final decision. Based on the above observations, in this work, we target CR and propose a joint convolutional matrix factorization (JCMF) method to tackle the encountered challenges, which jointly considers the item\u2019s reviews, item\u2019s relationships, user\u2019s social influence, and user\u2019s reviews in a unified framework. More specifically, to explore items\u2019 relationships, we introduce a predefined item relation network into ConvMF by a shared item latent factor and propose a method called convolutional matrix factorization with item relations (CMF-I). To consider user\u2019s social influence, we further integrate the user\u2019s social network into CMF-I by sharing the user latent factor between user\u2019s social network and user-item rating matrix, which can be treated as a regularization term to constrain the recommendation process. Finally, to model the document contextual information of user\u2019s reviews, we exploit another CNN to learn user\u2019s content representations and achieve our final model JCMF. We conduct extensive experiments on the real-world dataset from Yelp. The experimental results demonstrate the superiority of JCMF compared to several state-of-the-art methods in terms of root mean squared error (RMSE) and mean average error (MAE).<\/jats:p>","DOI":"10.1155\/2020\/1401236","type":"journal-article","created":{"date-parts":[[2020,1,27]],"date-time":"2020-01-27T18:30:38Z","timestamp":1580149838000},"page":"1-15","source":"Crossref","is-referenced-by-count":2,"title":["Learning to Make Document Context-Aware Recommendation with Joint Convolutional Matrix Factorization"],"prefix":"10.1155","volume":"2020","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-9408-7594","authenticated-orcid":true,"given":"Lei","family":"Guo","sequence":"first","affiliation":[{"name":"School of Business, Shandong Normal University, Jinan, China"},{"name":"Postdoctoral Research Station of Management Science and Engineering, Shandong Normal University, Jinan, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1185-8814","authenticated-orcid":true,"given":"Yu","family":"Han","sequence":"additional","affiliation":[{"name":"School of Business, Shandong Normal University, Jinan, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1533-1887","authenticated-orcid":true,"given":"Haoran","family":"Jiang","sequence":"additional","affiliation":[{"name":"Information Technology Bureau, Shandong Post Company, Jinan, China"}]},{"given":"Xinxin","family":"Yang","sequence":"additional","affiliation":[{"name":"School of Information Science and Engineering, Shandong Normal University, Jinan, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9505-0603","authenticated-orcid":true,"given":"Xinhua","family":"Wang","sequence":"additional","affiliation":[{"name":"School of Information Science and Engineering, Shandong Normal University, Jinan, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4976-9227","authenticated-orcid":true,"given":"Xiyu","family":"Liu","sequence":"additional","affiliation":[{"name":"School of Business, Shandong Normal University, Jinan, China"},{"name":"Postdoctoral Research Station of Management Science and Engineering, Shandong Normal University, Jinan, China"}]}],"member":"311","reference":[{"key":"4","doi-asserted-by":"publisher","DOI":"10.1016\/j.ins.2017.06.026"},{"key":"7","doi-asserted-by":"publisher","DOI":"10.1145\/3298988"},{"key":"8","doi-asserted-by":"publisher","DOI":"10.1007\/s00778-018-0524-7"},{"key":"13","doi-asserted-by":"publisher","DOI":"10.1109\/mic.2003.1167344"},{"key":"18","doi-asserted-by":"publisher","DOI":"10.1109\/tsc.2015.2413783"},{"key":"19","doi-asserted-by":"publisher","DOI":"10.1146\/annurev.soc.27.1.415"},{"key":"25","doi-asserted-by":"publisher","DOI":"10.1109\/access.2019.2924443"},{"key":"30","doi-asserted-by":"publisher","DOI":"10.1109\/tpami.2016.2605085"},{"key":"31","first-page":"123","volume":"15","year":"2015","journal-title":"AAAI"},{"key":"32","doi-asserted-by":"publisher","DOI":"10.1109\/access.2017.2762459"},{"key":"34","doi-asserted-by":"publisher","DOI":"10.1016\/j.jss.2014.09.019"},{"key":"37","doi-asserted-by":"publisher","DOI":"10.1016\/j.ijhcs.2018.04.002"},{"key":"38","doi-asserted-by":"publisher","DOI":"10.1016\/j.ijhcs.2018.02.008"},{"key":"39","doi-asserted-by":"publisher","DOI":"10.1109\/tkde.2014.2300487"},{"key":"43","doi-asserted-by":"publisher","DOI":"10.1145\/3134438"}],"container-title":["Complexity"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/downloads.hindawi.com\/journals\/complexity\/2020\/1401236.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/downloads.hindawi.com\/journals\/complexity\/2020\/1401236.xml","content-type":"application\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/downloads.hindawi.com\/journals\/complexity\/2020\/1401236.pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2020,1,27]],"date-time":"2020-01-27T18:30:39Z","timestamp":1580149839000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.hindawi.com\/journals\/complexity\/2020\/1401236\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,1,27]]},"references-count":15,"alternative-id":["1401236","1401236"],"URL":"https:\/\/doi.org\/10.1155\/2020\/1401236","relation":{},"ISSN":["1076-2787","1099-0526"],"issn-type":[{"type":"print","value":"1076-2787"},{"type":"electronic","value":"1099-0526"}],"subject":[],"published":{"date-parts":[[2020,1,27]]}}}