Application-Aware Cross Domains Selective Transfer Learning
Other Title
具應用可適性之跨平台相互輔助推薦系統
Date Issued
2014
Date
2014
Author(s)
WANG, KANG LIN
Abstract
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.
Subjects
推薦系統
跨平台
最佳化
Type
thesis
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