{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2024,8,7]],"date-time":"2024-08-07T07:34:50Z","timestamp":1723016090805},"publisher-location":"California","reference-count":0,"publisher":"International Joint Conferences on Artificial Intelligence Organization","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2018,7]]},"abstract":"<jats:p>A continuous-time tensor factorization method is developed for event sequences containing multiple \"modalities.\" Each data element is a point in a tensor, whose dimensions are associated with  the discrete alphabet of the modalities. Each tensor data element has an associated time of occurence and a feature vector. We model such data based on pairwise interactive point processes, and the proposed framework connects pairwise tensor factorization with a feature-embedded point process. The model accounts for interactions within each modality, interactions across different modalities, and continuous-time dynamics of the interactions. Model learning is formulated as a convex optimization problem, based on online alternating direction method of multipliers. Compared to existing state-of-the-art methods, our approach captures the latent structure of the tensor and its evolution over time, obtaining superior results on real-world datasets.<\/jats:p>","DOI":"10.24963\/ijcai.2018\/403","type":"proceedings-article","created":{"date-parts":[[2018,7,5]],"date-time":"2018-07-05T01:49:10Z","timestamp":1530755350000},"page":"2905-2911","source":"Crossref","is-referenced-by-count":2,"title":["Online Continuous-Time Tensor Factorization Based on Pairwise Interactive Point Processes"],"prefix":"10.24963","author":[{"given":"Hongteng","family":"Xu","sequence":"first","affiliation":[{"name":"Infinia ML, Inc"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Dixin","family":"Luo","sequence":"additional","affiliation":[{"name":"Department of Electrical and Computer Engineering, Duke University"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Lawrence","family":"Carin","sequence":"additional","affiliation":[{"name":"Department of Electrical and Computer Engineering, Duke University"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"10584","event":{"number":"27","sponsor":["International Joint Conferences on Artificial Intelligence Organization (IJCAI)"],"acronym":"IJCAI-2018","name":"Twenty-Seventh International Joint Conference on Artificial Intelligence {IJCAI-18}","start":{"date-parts":[[2018,7,13]]},"theme":"Artificial Intelligence","location":"Stockholm, Sweden","end":{"date-parts":[[2018,7,19]]}},"container-title":["Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence"],"original-title":[],"deposited":{"date-parts":[[2018,7,5]],"date-time":"2018-07-05T01:52:25Z","timestamp":1530755545000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.ijcai.org\/proceedings\/2018\/403"}},"subtitle":[],"proceedings-subject":"Artificial Intelligence Research Articles","short-title":[],"issued":{"date-parts":[[2018,7]]},"references-count":0,"URL":"https:\/\/doi.org\/10.24963\/ijcai.2018\/403","relation":{},"subject":[],"published":{"date-parts":[[2018,7]]}}}