The　microscopic　cascade　prediction　task　has　wide　applications　in　downstream　areas　like　“rumor　detection”.　Its　goal　is　to　forecast　the　diffusion　routines　of　information　cascade　within　networks.　Existing　works　typically　formulate　it　as　a　classification　task,　which　fails　to　well　align　with　the　Social　Homophily　assumption,　as　it　just　use　the　features　of　“infected”　users　while　neglecting　those　of　“uninfected”　users　in　representation　learning.　Moreover,　these　methods　focus　primarily　on　social　relationships,　thereby　dismissing　other　vital　dimensions　like　users＇　historical　behavior　and　the　underlying　preferences　behind　it.　To　address　these　challenges,　we　introduce　the　MSR　(Multifaceted　Self-Retrieval)　framework.　During　encoding,　in　addition　to　the　existing　social　graph,　we　construct　a　preference　graph　to　represent　“behavioral　preferences”　and　further　propose　a　modified　multi-channel　GRAU　for　multi-view　analysis　of　cascade　phenomenon.　For　decoding,　our　approach　diverges　from　classification-based　methods　by　reformulating　the　task　as　an　information　retrieval　problem　that　predicts　the　target　user　with　similarity　measures.　Empirical　evaluations　on　public　datasets　demonstrate　that　this　framework　significantly　outperforms　baselines　on　Hits@κ　and　MAP@κ,　affirming　its　enhanced　ability.　Copyright　©　2025,　Association　for　the　Advancement　of　Artificial　Intelligence　(www.aaai.org).　All　rights　reserved.