Mô hình tối ưu hóa cho gợi ý hợp tác sử dụng điều chỉnh dựa trên ma trận hiệp phương sai

Data Mining and Knowledge Discovery - Tập 32 - Trang 651-674 - 2018
Fabian Lecron1, François Fouss2
1Department of Engineering Innovation Management, Faculty of Engineering, University of Mons, Mons, Belgium
2Louvain School of Management (LSM), Louvain Research Institute in Management and Organizations (LouRIM), Université catholique de Louvain (UCL), Mons, Belgium

Tóm tắt

Bài báo này đề xuất một mô hình tối ưu hóa điều chỉnh lồi nhằm tạo ra các gợi ý, vừa có khả năng thích ứng, nhanh chóng và mở rộng—trong khi vẫn cạnh tranh rất tốt với các phương pháp hiện đại về độ chính xác. Chúng tôi giới thiệu một điều chỉnh dựa trên ma trận hiệp phương sai sao cho mô hình tối thiểu hóa hai tiêu chuẩn nhằm đảm bảo rằng các gợi ý được cung cấp cho người dùng được dẫn dắt bởi cả sở thích của những người dùng khác trong hệ thống và sở thích đã biết của người dùng đang được xử lý. Mô hình có khả năng thích ứng vì (1) nó có thể được nhìn nhận từ cả góc độ người dùng và sản phẩm (cho phép chọn, tùy thuộc vào nhiệm vụ, việc thể hiện với ít biến quyết định hơn) và (2) nhiều ràng buộc tùy thuộc vào ngữ cảnh (và không chỉ dựa trên độ chính xác, mà còn dựa trên tính hữu ích của các gợi ý cá nhân hóa) có thể dễ dàng được thêm vào, như được trình bày trong bài báo này thông qua hai ví dụ. Vì điều chỉnh của chúng tôi dựa trên ma trận hiệp phương sai, bài báo này cũng mô tả cách cải thiện độ phức tạp tính toán và không gian bằng cách sử dụng các kỹ thuật phân tích ma trận trong mô hình tối ưu hóa, dẫn đến một mô hình nhanh chóng và có khả năng mở rộng. Để minh họa tất cả những khái niệm này, các thí nghiệm đã được thực hiện trên bốn tập dữ liệu thực tế với kích thước khác nhau (tức là, FilmTrust, Ciao, MovieLens và Netflix) và những so sánh với các phương pháp hiện đại được cung cấp, cho thấy rằng phương pháp nhạy cảm với ngữ cảnh của chúng tôi rất cạnh tranh về độ chính xác.

Từ khóa

#tối ưu hóa điều chỉnh #gợi ý hợp tác #ma trận hiệp phương sai #phương pháp hiện đại #độ chính xác

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