In this paper, we propose an approach to learn distributed representations of users and items from text comments for recommendation systems. Traditional recommendation algorithms, e.g. collaborative filtering and matrix completion, are not designed to exploit the key information hidden in the text comments, while existing opinion mining methods do not provide direct support to recommendation sy...

Social network based-recommendation has some benefits that it approach used for improve of recommendation systems. Recommendation systems are appropriate tools for provide useful and suitable recommendations in social networks. Nowadays web users are not only consumers of information, but they actively participate in social networks. We checked dimensions of recommendation systems on social net...

Traditional recommendation systems offer relevant items (e.g., books, movies, music, etc.) to users, but they are not designed for mobile environments. In those environments, the context (e.g., the location, the time, the weather, the presence of other people, etc.) and the movements of the users may be important factors to obtain relevant and helpful recommendations. The emergence of context-a...

The information overflow of today’s information society can be overcome by the usage of recommender systems. Due to the fact that most recommender systems act as black boxes, trust in a system decrease, especially when a recommendation failed. Recommender systems usually don’t offer any insight into the systems logic and cannot be questioned as it is normal for a recommendation process between ...

Consumers currently enjoy a surplus of goods (books, videos, music, or other items) available to purchase. While this surplus often allows a consumer to find a product tailored to their preferences or needs, the volume of items available may require considerable time or effort on the part of the user to find the most relevant item. Recommendation systems have become a common part of many online...

Towards the Next Generation of Recommender Systems: A Survey of the State-of-the-Art and Possible Extensions Gediminas Adomavicius and Alexander Tuzhilin Abstract–The paper presents an overview of the field of recommender systems and describes the current generation of recommendation methods that are usually classified into the following three main categories: content-based, collaborative, and ...

With the exponential increase in the amount of digital information over the internet, online shops, online music, video and image libraries, search engines and recommendation system have become the most convenient ways to find relevant information within a short time. In the recent times, deep learning’s advances have gained significant attention in the field of speech recognition, image proces...

Recommender systems are designed to assist users in the search for product and service information and have been successfully deployed in a range of domains, from restaurants to route planning, movies to news. In particular, conversational recommender systems engage the user in an extended recommendation dialog, making suggestions and eliciting user feedback in order to guide the next round of ...

Cold start recommendations are important because they help build user loyalty, which is the key to the success of e-services and e-commerce systems. Recommending useful information for new users generally creates a sense of belonging and loyalty, and encourages them to visit e-commerce systems frequently. However, as new users take time to become familiar with recommendation systems, the system...

Recently ideas from mixed-initiative systems have been explored in the context of conversational recommender systems as a way to improve the interaction between the user and system. In this paper we examine some of the shortcomings of existing conversational recommender systems. In particular, we highlight how a more flexible recommendation strategy, one that responds to intermediate recommenda...