In e-commerce websites and related micro-blogs, users supply online reviews expressing their preferences regarding various items. Such reviews are typically in the textual comments form, and account for a valuable information source about user interests. Recently, several works have used review texts and their related rich information like review words, review topics and review sentiments, for improving the rating-based collaborative filtering recommender systems. These works vary from one another on how they exploit the review texts for deriving user interests. This paper provides a detailed survey of recent works that integrate review texts and also discusses how these review texts are exploited for addressing some main issues of standard collaborative filtering algorithms.

A large amount of data are generated every moment by connected objects creating Internet of Things (IoT). These data are difficult to handle using traditional databases leading to use NoSQL databases. These latter have achieved a large popularity thanks to their high performance, flexibility in scaling, and high availability. But, which NoSQL database is the most suitable for IoT applications? In this paper, we discuss the main requirements of IoT data management, and we compare five of the most popular NoSQL databases namely, Redis, Cassandra, MongoDb, Couchbase and Neo4j, in accordance with IoT data management requirements, in order to find the most suitable NoSQL database for IoT applications.