Recommender Systems

The ongoing rapid expansion of the Internet greatly increases the necessity of effective recommender systems for filtering the abundant information. Extensive research for recommender systems is conducted by a broad range of communities including social and computer scientists, physicists, and interdisciplinary researchers. Despite substantial theoretical and practical achievements, unification and comparison of different approaches are lacking, which impedes further advances. In this article, we review recent developments in recommender systems and discuss the major challenges. We compare and evaluate available algorithms and examine their roles in the future developments. In addition to algorithms, physical aspects are described to illustrate macroscopic behavior of recommender systems. Potential impacts and future directions are discussed. We emphasize that recommendation has a great scientific depth and combines diverse research fields which makes it of interests for physicists as well as interdisciplinary researchers.Comment: 97 pages, 20 figures (To appear in Physics Reports

Sistem Rekomendasi Artikel Ilmiah Berbasis Web Menggunakan Content-based Learning dan Collaborative Filtering

Penelitian ini mengimplementasikan metode content-based learning dan collaborative filtering pada sistem rekomendasi artikel ilmiah berbasis web untuk merekomendasikan artikel ilmiah berbahasa Inggris. Sistem memiliki empat komponen, yaitu analisa konten, profile learner, komponen penyaringan, dan pengambilan dokumen. Content-based learning diimplementasikan pada komponen analisa konten yang bertugas mengidentifikasi masukan berupa artikel ilmiah berbahasa Inggris. Profile learner dilakukan untuk menghitung kemiripan antar pengguna setelah sistem mendapatkan feedback berupa rating dari pengguna. Collaborative filtering diimplementasikan pada komponen penyaringan yang bertugas untuk merekomendasikan artikel kepada pengguna setelah sistem mendapatkan hasil kemiripan antar pengguna.  Pengambilan dokumen dilakukan pada proses pengambilan artikel yang dicari oleh pengguna. Uji coba dilakukan pada 100 artikel ilmiah, 6 kelas kategori, serta melibatkan 35 pengguna. Hasil penelitian ini membuktikan bahwa implementasi metode content-based learning dan collaborative filtering pada sistem rekomendasi artikel ilmiah berbasis web mampu memberikan tingkat relevansi dan efektifitas sebesar 0.801 berdasarkan Mean Average Precision dan 0,851 berdasarkan Mean Absolute Error

Deep Learning based Recommender System: A Survey and New Perspectives

With the ever-growing volume of online information, recommender systems have been an effective strategy to overcome such information overload. The utility of recommender systems cannot be overstated, given its widespread adoption in many web applications, along with its potential impact to ameliorate many problems related to over-choice. In recent years, deep learning has garnered considerable interest in many research fields such as computer vision and natural language processing, owing not only to stellar performance but also the attractive property of learning feature representations from scratch. The influence of deep learning is also pervasive, recently demonstrating its effectiveness when applied to information retrieval and recommender systems research. Evidently, the field of deep learning in recommender system is flourishing. This article aims to provide a comprehensive review of recent research efforts on deep learning based recommender systems. More concretely, we provide and devise a taxonomy of deep learning based recommendation models, along with providing a comprehensive summary of the state-of-the-art. Finally, we expand on current trends and provide new perspectives pertaining to this new exciting development of the field.Comment: The paper has been accepted by ACM Computing Surveys. https://doi.acm.org/10.1145/328502

Integrating Social Circles and Network Representation Learning for Item Recommendation

With the increasing popularity of social network services, social network platforms provide rich and additional information for recommendation algorithms. More and more researchers utilize trust relationships of users to improve the performance of recommendation algorithms. However, most of existing social-network-based recommendation algorithms ignore the following problems: (1) In different domains, users tend to trust different friends. (2) the performance of recommendation algorithms is limited by the coarse-grained trust relationships. In this paper, we propose a novel recommendation algorithm that integrates social circles and network representation learning for item recommendation. Specifically, we first infer domain-specific social trust circles based on original users’ rating information and social network information. Next, we adopt network representation technique to embed domain-specific social trust circle into a low-dimensional space, and then utilize the low-dimensional representations of users to infer the fine-grained trust relationships between users. Finally, we integrate the fine-gained trust relationships into domain-specific matrix factorization model to learn latent user and item feature vectors. Experimental results on real-world datasets show that our proposed approach outperforms traditional social-network-based recommendation algorithms