|کد مقاله||کد نشریه||سال انتشار||مقاله انگلیسی||ترجمه فارسی||نسخه تمام متن|
|4950288||1364283||2018||11 صفحه PDF||سفارش دهید||دانلود کنید|
- We proposed a time-aware recommendation method called TCARS.
- A reliability measure is used to evaluate the quality of the initial predicted rates.
- A novel overlapping community detection method is proposed to group similar users.
- Experiments were performed on two real-world datasets.
- TCARS obtained accurate results compared to several state-of-the-art methods.
With the abundance of information produced by users on items (e.g., purchase or rating histories), recommender systems are a major ingredient of online systems such as e-stores and service providers. Recommendation algorithms use information available from users-items interactions and their contextual data to provide a list of potential items for each user. These algorithms are constructed based on similarity between users and/or items (e.g., a user is likely to purchase the same items as his/her most similar users). In this work, we introduce a novel time-aware recommendation algorithm that is based on identifying overlapping community structure among users. Users' interests might change over time, and accurate modeling of dynamic users' preferences is a challenging issue in designing efficient personalized recommendation systems. The users-items interaction network is often highly sparse in real systems, for which many recommenders fail to provide accurate predictions. The proposed overlapping community structure amongst the users helps in minimizing the sparsity effects. We apply the proposed algorithm on two real-world benchmark datasets and show that it overcomes these challenges. The proposed algorithm shows better precision than a number of state-of-the-art recommendation methods.
Journal: Future Generation Computer Systems - Volume 78, Part 1, January 2018, Pages 419-429