{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2024,8,12]],"date-time":"2024-08-12T15:50:33Z","timestamp":1723477833188},"reference-count":0,"publisher":"Association for the Advancement of Artificial Intelligence (AAAI)","issue":"1","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["AAAI"],"abstract":"\n \n Top-N item recommendation techniques, e.g., pairwise models, learn the rank of users' preferred items through separating items into positive samples if user-item interactions exist, and negative samples otherwise. This separation results in an important issue: the extreme imbalance between positive and negative samples, because the number of items with user actions is much less than those without actions. The problem is even worse for \"cold-start\" users. In addition, existing learning models only consider the observed user-item proximity, while neglecting other useful relations, such as the unobserved but potentially helpful user-item relations, and high-order proximity in user-user, item-item relations. In this paper, we aim at incorporating multiple types of user-item relations into a unified pairwise ranking model towards approximately optimizing ranking metrics mean average precision (MAP), and mean reciprocal rank (MRR). Instead of taking statical separation of positive and negative sets, we employ a random walk approach to dynamically draw positive samples from short random walk sequences, and a rank-aware negative sampling method to draw negative samples for efficiently learning the proposed pairwise ranking model. The proposed method is compared with several state-of-the-art baselines on two large and sparse datasets. Experimental results show that our proposed model outperforms the other baselines with average 4% at different top-N metrics, in particular for cold-start users with 6% on average.\n \n <\/jats:p>","DOI":"10.1609\/aaai.v32i1.11866","type":"journal-article","created":{"date-parts":[[2022,6,24]],"date-time":"2022-06-24T21:33:45Z","timestamp":1656106425000},"source":"Crossref","is-referenced-by-count":29,"title":["WalkRanker: A Unified Pairwise Ranking Model With Multiple Relations for Item Recommendation"],"prefix":"10.1609","volume":"32","author":[{"given":"Lu","family":"Yu","sequence":"first","affiliation":[]},{"given":"Chuxu","family":"Zhang","sequence":"additional","affiliation":[]},{"given":"Shichao","family":"Pei","sequence":"additional","affiliation":[]},{"given":"Guolei","family":"Sun","sequence":"additional","affiliation":[]},{"given":"Xiangliang","family":"Zhang","sequence":"additional","affiliation":[]}],"member":"9382","published-online":{"date-parts":[[2018,4,26]]},"container-title":["Proceedings of the AAAI Conference on Artificial Intelligence"],"original-title":[],"link":[{"URL":"https:\/\/ojs.aaai.org\/index.php\/AAAI\/article\/download\/11866\/11725","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/ojs.aaai.org\/index.php\/AAAI\/article\/download\/11866\/11725","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,11,7]],"date-time":"2022-11-07T18:01:22Z","timestamp":1667844082000},"score":1,"resource":{"primary":{"URL":"https:\/\/ojs.aaai.org\/index.php\/AAAI\/article\/view\/11866"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2018,4,26]]},"references-count":0,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2018,2,8]]}},"URL":"https:\/\/doi.org\/10.1609\/aaai.v32i1.11866","relation":{},"ISSN":["2374-3468","2159-5399"],"issn-type":[{"value":"2374-3468","type":"electronic"},{"value":"2159-5399","type":"print"}],"subject":[],"published":{"date-parts":[[2018,4,26]]}}}