Expert-opinions Based Linear Regression Model for Top-N Recommendation

Suggest * Top-n Recommendation Engine

Recommendation Based on Contextual Opinions

Context has been recognized as an important factor in constructing personalized recommender systems. However, most contextaware recommendation techniques mainly aim at exploiting item-level contextual information for modeling users’ preferences, while few works attempt to detect more fine-grained aspect-level contextual preferences. Therefore, in this article, we propose a contextual recommenda...

Feature-based factorized Bilinear Similarity Model for Cold-Start Top-n Item Recommendation

Recommending new items to existing users has remained a challenging problem due to absence of user’s past preferences for these items. The user personalized non-collaborative methods based on item features can be used to address this item cold-start problem. These methods rely on similarities between the target item and user’s previous preferred items. While computing similarities based on item...

Content-Based Top-N Recommendation Using Heterogeneous Relations

Top-N recommender systems have been extensively studied. However, the sparsity of user-item activities has not been well resolved. While many hybrid systems were proposed to address the cold-start problem, the profile information has not been sufficiently leveraged. Furthermore, the heterogeneity of profiles between users and items intensifies the challenge. In this paper, we propose a content-...

Integrated Expert Recommendation Model for Online Communities

Online communities have become vital places for Web 2.0 users to share knowledge and experiences. Recently, finding expertise user in community has become an important research issue. This paper proposes a novel cascaded model for expert recommendation using aggregated knowledge extracted from enormous contents and social network features. Vector space model is used to compute the relevance of ...