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Inf. Syst."],"published-print":{"date-parts":[[2019,7,31]]},"abstract":"<jats:p>\n            To address the sparsity and cold-start problem of collaborative filtering, researchers usually make use of side information, such as social networks or item attributes, to improve the performance of recommendation. In this article, we consider the knowledge graph (KG) as the source of side information. To address the limitations of existing embedding-based and path-based methods for KG-aware recommendation, we propose\n            <jats:italic>RippleNet<\/jats:italic>\n            , an end-to-end framework that naturally incorporates the KG into recommender systems. RippleNet has two versions: (1) The\n            <jats:italic>outward propagation<\/jats:italic>\n            version, which is analogous to the actual ripples on water, stimulates the propagation of user preferences over the set of knowledge entities by automatically and iteratively extending a user\u2019s potential interests along links in the KG. The multiple \u201cripples\u201d activated by a user\u2019s historically clicked items are thus superposed to form the preference distribution of the user with respect to a candidate item. (2) The\n            <jats:italic>inward aggregation<\/jats:italic>\n            version aggregates and incorporates the neighborhood information biasedly when computing the representation of a given entity. The neighborhood can be extended to multiple hops away to model high-order proximity and capture users\u2019 long-distance interests. In addition, we intuitively demonstrate how a KG assists with recommender systems in RippleNet, and we also find that RippleNet provides a new perspective of explainability for the recommended results in terms of the KG. Through extensive experiments on real-world datasets, we demonstrate that both versions of RippleNet achieve substantial gains in a variety of scenarios, including movie, book, and news recommendations, over several state-of-the-art baselines.\n          <\/jats:p>","DOI":"10.1145\/3312738","type":"journal-article","created":{"date-parts":[[2019,3,18]],"date-time":"2019-03-18T12:09:30Z","timestamp":1552910970000},"page":"1-26","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":105,"title":["Exploring High-Order User Preference on the Knowledge Graph for Recommender Systems"],"prefix":"10.1145","volume":"37","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-7474-8271","authenticated-orcid":false,"given":"Hongwei","family":"Wang","sequence":"first","affiliation":[{"name":"Shanghai Jiao Tong University, Shanghai, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Fuzheng","family":"Zhang","sequence":"additional","affiliation":[{"name":"Meituan-Dianping Group, Beijing, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jialin","family":"Wang","sequence":"additional","affiliation":[{"name":"The Hong Kong Polytechnic University, Hong Kong, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Miao","family":"Zhao","sequence":"additional","affiliation":[{"name":"The Hong Kong Polytechnic University, Hong Kong, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Wenjie","family":"Li","sequence":"additional","affiliation":[{"name":"The Hong Kong Polytechnic University, Hong Kong, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xing","family":"Xie","sequence":"additional","affiliation":[{"name":"Microsoft Research Asia, Beijing, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Minyi","family":"Guo","sequence":"additional","affiliation":[{"name":"Shanghai Jiao Tong University, Shanghai, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2019,3,16]]},"reference":[{"key":"e_1_2_1_1_1","volume-title":"Neural machine translation by jointly learning to align and translate. arXiv preprint arXiv:1409.0473","author":"Bahdanau Dzmitry","year":"2014","unstructured":"Dzmitry Bahdanau , Kyunghyun Cho , and Yoshua Bengio . 2014. 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