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Inductive personalization aims to infer the relations between existing entities and unseen new ones, e.g., searching relevant authors for new papers or recommending new items to users. This problem, however, is challenging since most of recent studies focus on transductive problem for existing entities. In addition, despite some inductive learning approaches have been introduced recently, their performance is sub-optimal due to relatively simple and inflexible architectures for aggregating entity\u2019s content. To this end, we propose the inductive contextual personalization (ICP) framework through contextual relation learning. Specifically, we first formulate the pairwise relations between entities with a ranking optimization scheme that employs neural aggregator to fuse entity\u2019s heterogeneous contents. Next, we introduce a node embedding term to capture entity\u2019s contextual relations, as a smoothness constraint over the prior ranking objective. Finally, the gradient descent procedure with adaptive negative sampling is employed to learn the model parameters. The learned model is capable of inferring the relations between existing entities and inductive ones. Thorough experiments demonstrate that ICP outperforms numerous baseline methods for two different applications, i.e., relevant author search and new item recommendation.<\/jats:p>","DOI":"10.1145\/3450353","type":"journal-article","created":{"date-parts":[[2021,5,25]],"date-time":"2021-05-25T22:18:01Z","timestamp":1621981081000},"page":"1-22","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":2,"title":["Inductive Contextual Relation Learning for Personalization"],"prefix":"10.1145","volume":"39","author":[{"given":"Chuxu","family":"Zhang","sequence":"first","affiliation":[{"name":"Brandeis University, Waltham, MA, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Huaxiu","family":"Yao","sequence":"additional","affiliation":[{"name":"Pennsylvania State University, PA, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Lu","family":"Yu","sequence":"additional","affiliation":[{"name":"Ant Financial Services Group, Hangzhou, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2062-1512","authenticated-orcid":false,"given":"Chao","family":"Huang","sequence":"additional","affiliation":[{"name":"JD Finance America Corporation, CA, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Dongjin","family":"Song","sequence":"additional","affiliation":[{"name":"University of Connecticut, Storrs, CT, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Haifeng","family":"Chen","sequence":"additional","affiliation":[{"name":"NEC Laboratories America Inc, Princeton, NJ, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Meng","family":"Jiang","sequence":"additional","affiliation":[{"name":"University of Notre Dame, Notre Dame, IN, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Nitesh V.","family":"Chawla","sequence":"additional","affiliation":[{"name":"University of Notre Dame, Notre Dame, IN, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2021,5,25]]},"reference":[{"key":"e_1_2_1_1_1","unstructured":"Dzmitry Bahdanau Kyunghyun Cho and Yoshua Bengio. 2015. 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