Personalized recommender systems are increasingly important as more content and services become available users struggle to identify what might interest them. Thanks the ability for providing rich information, knowledge graphs (KGs) being incorporated enhance recommendation performance interpretability. To effectively make use of graph, we propose a model in hyperbolic space, which facilitates ...

Strands develops products that help people find information online that they want and need. Strands offers production recommendation services for eCommerce, interactive tools for personal finance management, and personal interest and lifestyle-oriented social discovery solutions. Strands also operates moneystrands.com, a personal finance management platform, and strands.com, a training log and ...

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-...

in recent years, collaborative filtering (cf) methods are important and widely accepted techniques are available for recommender systems. one of these techniques is user based that produces useful recommendations based on the similarity by the ratings of likeminded users. however, these systems suffer from several inherent shortcomings such as data sparsity and cold start problems. with the dev...

With the dawning ubiquitous computing age, increasing online-based multimedia data presents new challenges for storing and querying large amounts of data to online recommendation systems. Recent studies on recommendation systems show that graph data model is more efficient than relational data model for processing complex data. This paper proposes a new graph data storage model for the collabor...

One of the current challenges concerning improving recommender systems consists of finding ways of increasing serendipity and diversity, without compromising the precision and recall of the system. One possible way to approach this problem is to complement a standard recommender by another recommender “orthogonal” to the standard one, i.e. one that recommends different items than the standard. ...

Cold start is one of the main challenges in recommender systems. Solving sparsechallenge of cold start users is hard. More cold start users and items are new. Sine many general methods for recommender systems has over fittingon cold start users and items, so recommendation to new users and items is important and hard duty. In this work to overcome sparse problem, we present a new method for rec...

Context-aware recommender systems extend traditional recommender systems by adapting their output to users’ specific contextual situations. Most of the existing approaches to context-aware recommendation involve directly incorporating context into standard recommendation algorithms (e.g., collaborative filtering, matrix factorization). In this paper, we highlight the importance of context simil...