{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,4,17]],"date-time":"2025-04-17T14:31:21Z","timestamp":1744900281943},"reference-count":20,"publisher":"World Scientific Pub Co Pte Ltd","issue":"06n07","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Int. J. Found. Comput. Sci."],"published-print":{"date-parts":[[2022,9]]},"abstract":"<jats:p> Different recommendation algorithms, which often use only a single type of user-item engagement, are plagued by imbalanced datasets and cold start problems. Multi-behavior recommendations, which takes advantage of a variety of customer interaction including click and favorites, can be a good option. Early attempts at multi-behavior suggestion tried to consider the varying levels of effect each behavior has on the target behavior. They also disregard the meanings of behaviors, which are implicit in multi-behavior information. Because of these two flaws, the information isn\u2019t being completely utilized to improve suggestion performance on the specific behavior. In this paper, we take a novel response to the situation by creating a unified network to capture multi-behavior information and displaying the MBGCNNN model (Multi-Behavior Graph Convolutional Neural Network). MBGCNN may effectively overcome the constraints of prior studies by learning behavior intensity via the user-item dissemination level and collecting behavior interpretation via the items dissemination level. Practical derives from various data sets back up our model\u2019s order to leverage multi-behavior data. On real methods, our approach beats the average background by 25.02 percent and 6.51 percent, respectively. Additional research on cold-start consumers supports the viability of our suggested approach. <\/jats:p>","DOI":"10.1142\/s0129054122420059","type":"journal-article","created":{"date-parts":[[2022,5,29]],"date-time":"2022-05-29T15:19:41Z","timestamp":1653837581000},"page":"583-601","source":"Crossref","is-referenced-by-count":1,"title":["Management and Monitoring of Multi-Behavior Recommendation Systems Using Graph Convolutional Neural Networks"],"prefix":"10.1142","volume":"33","author":[{"given":"Changwei","family":"Liu","sequence":"first","affiliation":[{"name":"Department of Information Engineering, Guangzhou Nanyang Polytechnic College, Guangzhou, P. R. China"}]},{"given":"Kexin","family":"Wang","sequence":"additional","affiliation":[{"name":"School of Computer Science and Technology, Heilongjiang University, Harbin, P. R. 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