About: Joint Relational Dependency Learning for Sequential Recommendation

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An Entity of Type : schema:ScholarlyArticle, within Data Space : covidontheweb.inria.fr associated with source document(s)

Attributes	Values
type	Academic Article research paper schema:ScholarlyArticle
isDefinedBy	Covid-on-the-Web dataset
has title	Joint Relational Dependency Learning for Sequential Recommendation
Creator	Li, Qian Zhang, Wu Liu, Shaowu Wang, Xiangmeng Xu, Guandong Zhu, Wenhao
Source	PMC
abstract	Sequential recommendation leverages the temporal information of users’ transactions as transition dependencies for better inferring user preference, which has become increasingly popular in academic research and practical applications. Short-term transition dependencies contain the information of partial item orders, while long-term transition dependencies infer long-range user preference, the two dependencies are mutually restrictive and complementary. Although some work investigates unifying both long-term and short-term dependencies for better performance, they still neglect the fact that short-term interactions are multi-folds, which are either individual-level interactions or union-level interactions. Existing sequential recommendations mainly focus on user’s individual (i.e., individual-level) interactions but ignore the important collective influence at union-level. Since union-level interactions can reflect that human decisions are made based on multiple items he/she has already interacted, ignoring such interactions can result in the disability of capturing the collective influence between items. To alleviate this issue, we proposed a Joint Relational Dependency learning (JRD-L) for sequential recommendation that exploits both long-term and short-term preferences at individual-level and union-level. Specifically, JRD-L combines long-term user preferences with short-term interests by measuring short-term pair relations at individual-level and union-level. Moreover, JRD-L can alleviate the sparsity problem of union-level interactions by adding more descriptive details to each item, which is carried by individual-level relations. Extensive numerical experiments demonstrate JRD-L outperforms state-of-the-art baselines for the sequential recommendation.
has issue date	2020-04-17(xsd:dateTime)
bibo:doi	10.1007/978-3-030-47426-3_14
has license	no-cc
sha1sum (hex)	2b98c25235193717866dcb7aad2a401a7a943283
schema:url	https://doi.org/10.1007/978-3-030-47426-3_14
resource representing a document's title	Joint Relational Dependency Learning for Sequential Recommendation
has PubMed Central identifier	PMC7206282
schema:publication	Advances in Knowledge Discovery and Data Mining
resource representing a document's body	covid:2b98c25235193717866dcb7aad2a401a7a943283#body_text
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