多元資訊之食譜推薦系統

Abstract

推薦系統在最近幾年成為重要的研究領域。但是目前的研究大多數著重於推薦商業化的產品，如：書或音樂。在這篇論文中，我們將推薦的對象轉移到一個不同的領域：食譜。食譜和書，音樂最大的不同點在於，食譜提供仔細的資訊，如食材，料理過程，這樣使用者才可以做出幾乎一樣味道的食物。我們相信食譜一定存在了某些特性，符合了使用者的個人需求外且足夠吸引他去料理，品嘗後，評分。 在本篇論文中，我們從另外一個角度來解決推薦這個問題。我們把食譜當成許多屬性的集合，這些屬性來自於食材，分類，作法，簡介和營養。我們擴展使用Matrix Factorization的技巧，去模擬使用者有多喜愛某個屬性。另外我們增添多個偏差值，模擬與時間相關的屬性。最後，我們使用Ensemble 的技術加強我們的方法。我們使用RMSE作為評估結果的標準。RMSE是推薦系統中評估準確度最熱門的標準。而我們最後的RMSE結果是0.5813，比MF 進步了 0.0202 (3.36%)。

Recommendation system has been an important and well-studied topic in recent years. However, most of the existing studies focus on the recommendation commercial produces such as movies and music. In this thesis, we aim to bring recommendation to another dimension: recipes. The most special characteristic of recipe compared to movie and music is that recipe provides detail information, ingredients and directions to help people reproduce almost the same taste food. We believe a recipe must have quite charming features, which meet people’s preferences perfectly. So people would like to reproduce it by their self, tasted it then rated it. In this thesis, we process the problem of recipe recommendation in a different aspect. We treat recipes as an aggregation of lots features, which extract from ingredients, categories, directions, profile and nutrition. We use an extension of matrix factorization to module the how people like a feature. Then we add several extra biases to module time-dependence features, and finally we use the ensemble technology to improve our methodology. We used Root Mean Squared Error (RMSE) to evaluate result. RMSE is the most popular metric used in recommendation system to evaluating accuracy of predicted ratings. And our result RMSE is 0.5813, which is improved 0.0202 (3.36%) than MF.