The existing music recommendation systems rely on user’s contexts or content analysis to satisfy the users’ music playing needs. They achieved a certain degree of success and inspired future researches to get more progress. However, a cold start problem and the limitation to the similar music have been pointed out. Therefore, this paper proposes a unique recommendation method using a ‘renso’ al...

Disaster-tolerant systems are complex and expensive constructions that have hitherto been the provision of only the very rich or the very scared. The current state of the art for surviving site-wide failures is to mirror persistent storage to a remote location and cold-start applications there in the event of failure. This requires delicate, tailored modifications to production software to rest...

Thermistors which are thermally-sensitive resistors separately. The separate application and removal of having large negative temperature coefficients of resistance can be power to the individual sectioijs facilitates trouble aptly used for the gradual application of heater voltage to thermionic tubes, thereby diminishing thermal transients and reducing mechanistin andmnizes thener of time thee...

This thesis consists of four parts: - An analysis of the core functions and the prerequisites for recommender systems in an industrial context: we identify four core functions for recommendation systems: Help do Decide, Help to Compare, Help to Explore, Help to Discover. The implementation of these functions has implications for the choices at the heart of algorithmic recommender systems. - A s...

In recent years, recommendation algorithms have become one of the most active research areas driven by the enormous industrial demands. Most of the existing recommender systems focus on topics such as movie, music, e-commerce etc., which essentially differ from the TV show recommendations due to the cold-start and temporal dynamics. Both effectiveness (effectively handling the cold-start TV sho...

ion—Solving free riding and selecting a reliable service provider in P2P networks has been separately investigated in last few years. Using trust has shown to be one of the best ways of solving these problems. But using this approach to simultaneously deal with both problems makes it impossible for newcomers to join the network and the expansion of network is prevented. In this paper we used th...

Preference mining plays an important role in e-commerce and video websites for enhancing user satisfaction and loyalty. Some classical methods are not available for the cold-start problem when the user or the item is new. In this paper, we propose a new model, called parametric neighborhood rough set on two universes (NRSTU), to describe the user and item data structures. Furthermore, the neigh...

I present a hybrid matrix factorisation model representing users and items as linear combinations of their content features’ latent factors. The model outperforms both collaborative and content-based models in cold-start or sparse interaction data scenarios (using both user and item metadata), and performs at least as well as a pure collaborative matrix factorisation model where interaction dat...

We have developed a novel hybrid representation for Music Information Retrieval. Our representation is built by incorporating audio content into the tag space in a tag-track matrix, and then learning hybrid concepts using latent semantic analysis. We apply this representation to the task of music recommendation, using similarity-based retrieval from a query music track. We also develop a new ap...

Most of current recommendation systems use numerical ratings to suggest a content (e.g. movie, restaurant) to a user. Instead, we apply probabilistic topic models to text reviews. We profile contents in a latent space where we compute distances that can be used for cold start recommendation.