{"type":"ScholarlyArticle","title":"Probabilistic Masked Attention Networks for Explainable Sequential Recommendation","authors":[{"name":"Huiyuan Chen","slug":"huiyuan-chen","given":"Huiyuan","family":"Chen","affiliation":"Visa Research"},{"name":"Kaixiong Zhou","slug":"kaixiong-zhou","given":"Kaixiong","family":"Zhou","affiliation":"Rice University"},{"name":"Zhimeng Jiang","slug":"zhimeng-jiang","given":"Zhimeng","family":"Jiang","affiliation":"Texas A&M University {hchen ,"},{"name":"Chin-Chia Michael Yeh","slug":"chin-chia-michael-yeh","given":"Chin-Chia Michael","family":"Yeh","affiliation":"Visa Research"},{"name":"Xiaoting Li","slug":"xiaoting-li","given":"Xiaoting","family":"Li","affiliation":"Visa Research"},{"name":"Menghai Pan","slug":"menghai-pan","given":"Menghai","family":"Pan","affiliation":"Visa Research"},{"name":"Yan Zheng","slug":"yan-zheng","given":"Yan","family":"Zheng","affiliation":"Visa Research"},{"name":"Xia Hu","slug":"xia-hu","given":"Xia","family":"Hu","affiliation":"Rice University"},{"name":"Hao Yang","slug":"hao-yang","given":"Hao","family":"Yang","affiliation":"Visa Research"}],"abstract":"Transformer-based models are powerful for modeling temporal dynamics of user preference in sequential recommendation. Most of the variants adopt the Softmax transformation in the self-attention layers to generate dense attention probabilities. However, real-world item sequences are often noisy, containing a mixture of true-positive and false-positive interactions. Such dense attentions inevitably assign probability mass to noisy or irrelevant items, leading to sub-optimal performance and poor explainability. Here we propose a Probabilistic Masked Attention Network (PMAN) to identify the sparse pattern of attentions, which is more desirable for pruning noisy items in sequential recommendation. Specifically, we employ a probabilistic mask to achieve sparse attentions under a constrained optimization framework. As such, PMAN allows to select which information is critical to be retained or dropped in a data-driven fashion. Experimental studies on real-world benchmark datasets show that PMAN is able to improve the performance of Transformers significantly.","doi":"10.24963/ijcai.2023/230","url":"https://ijcai.org/proceedings/2023/230","pdf":"https://ijcai.org/proceedings/2023/0230.pdf","markdown":"https://ijcai.org/proceedings/2023/230.md","bibtex":"https://ijcai.org/proceedings/2023/bibtex/230","volume":{"slug":"2023","title":"Proceedings of the Thirty-Second International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2023","url":"https://ijcai.org/proceedings/2023/","year":2023,"location":"Macao, SAR","dates":"19-25 August 2023","editors":"Edith Elkind","isbn":"978-1-956792-03-4","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2023"},"section":"Data Mining","track":"Main Track","number":230,"pages":{"first":2068,"last":2076},"keywords":["AI Ethics, Trust, Fairness: ETF: Explainability and interpretability","Data Mining: DM: Information retrieval","Data Mining: DM: Collaborative filtering","Data Mining: DM: Recommender systems"],"videos":[],"references":[{"position":1,"raw":"38::It Takes Two::Comedy 49::When Night Is Falling::Drama|Romance 53::Lamerica::Drama 11::American President::Comedy|Drama|Romance 25::Leaving Las Vegas::Drama|Romance 19::Ace Ventura: When Nature Calls::Comedy 2::Jumanji::Adventure|Children's|Fantasy 17::Sense and Sensibility::Drama|Romance 37::Across the Sea of Time::Documentary 12::Dracula: Dead and Loving It::Comedy|Horror 14::Nixon::Drama 30::Shanghai Triad::Drama 4::Waiting to Exhale::Comedy|Drama 15::Cutthroat Island::Action|Adventure|Romance 50::Usual Suspects::Crime|Thriller 34::Babe::Children's|Comedy|Drama 8::Tom and Huck::Adventure|Children's 43::Restoration::Drama 51::Guardian Angel::Action|Drama|Thriller 41::Richard III::Drama|War 48::Pocahontas::Animation|Children's|Musical|Romance 18::Four Rooms::Thriller 16::Casino::Drama|Thriller 5::Father of the Bride Part II::Comedy 1::Toy Story::Animation|Children's|Comedy 40::Cry, the Beloved Country::Drama 21::Get Shorty::Action|Comedy|Drama 31::Dangerous Minds::Drama 22::Copycat::Crime|Drama|Thriller 46::How to Make an American Quilt::Drama|Romance 47::Seven (Se7en)::Crime|Thriller 36::Dead Man Walking::Drama 45::To Die For::Comedy|Drama 27::Now and Then::Drama 6::Heat::Action|Crime|Thriller 33::Wings of Courage::Adventure|Romance 10::GoldenEye::Action|Adventure|Thriller 3::Grumpier Old Men::Comedy|Romance 9::Sudden Death::Action 35::Carrington::Drama|Romance 52::Mighty Aphrodite::Comedy 7::Sabrina::Comedy|Romance 13::Balto::Animation|Children's Predict next 44::Mortal Kombat::Action|Adventure 20::Money Train::Action 29::City of Lost Children::Adventure|Sci-Fi 26::Othello::Drama 42::Dead Presidents::Action|Crime|Drama 39::Clueless::Comedy|Romance 24::Powder::Drama|Sci-Fi References [","title":"Dangerous Minds::Drama 22::Copycat::Crime|Drama|Thriller 46::How to Make an American Quilt::Drama|Romance 47::Seven (Se7en)::Crime|Thriller 36::Dead Man Walking","authors":null,"year":null,"venue":null,"doi":null,"url":null,"internal_url":null},{"position":2,"raw":"[Beltagy et al., 2020] Iz Beltagy, Matthew E Peters, and Ar- man Cohan. 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