具應用可適性之跨平台相互輔助推薦系統

Abstract

本碩士論文係介紹在多個平台上的推薦系統相互甫助的方式。以往的推薦系統多利用於單一平台上，方法多如利用該單一平台上的資訊，尋找相似的評分模式去進行對於使用者們尚未評分的項目進行預測與推薦。近年來， 諸多文獻開始探討關於是否能夠使用多個分別的平台，藉由交換資訊的方式達到對所有參與的平台皆有益處的跨平台推薦系統。 本論文係在討論該跨平台推薦系統，本論文除了回顧了之前方法的好處之外，並提出一新穎的方式進行跨平台推薦系統的優化。我們提出的方式不僅在效能上優於舊有的方式，且運算速度更快。

Traditionally, recommender systems make recommendations based on a single domain (e.g., movie or book domain) only. Recently, several cross-domain recommendation models have been proposed. Some of them proposed to leverage the common latent factors in the rating patterns of users-to-items co-clustering between domains and proposed to transfer the knowledge of such common latent factors to enhance the overall recommendation performance. However, these models often restrain themselves to transfer all the common knowledge between domains. Furthermore, these models often include all the domains in theirs participating domain set without selecting and evaluating the effect of including such domain into the transfer learning task. In this thesis, we propose a novel selective transfer learning model for the cross-multiple domains recommendation problem. This model not only can discover and apply the cross-multiple domains rating patterns to enhance the performance of recommendation on each of the participating domain, but also can select the most beneficial and efficient common knowledge then transfer the knowledge to each of the participating domain to improve the recommendation performance. In addition, we define a domain property index to evaluate the benefit of including each domain into the transfer learning task. Hence, this framework is able to discover and leverage the most influential common and cross-multiple domains rating patterns, and select an efficient participating domain set to enhance the recommendation performance. Extensive experiments on several real world datasets indicate that the proposed framework outperforms state-of-the-art methods for cross-domain recommendation task.