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...

A new approach is outlined in which group modeling techniques are used to model an individual user. This helps to reduce cold-start problems, and allows aggregating multiple criteria.

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...

This chapter shows how a system can recommend to a group of users by aggregating information from individual user models and modelling the users affective state. It summarizes results from previous research in this area. It also shows how group recommendation techniques can be applied when recommending to individuals, in particular for solving the cold-start problem and dealing with multiple cr...

We perform a study on cold-start news popularity prediction using a collection of 13,319 news articles obtained from Yahoo News. We characterise the online popularity of news articles by two different metrics and try to predict them using machine learning techniques. Contrary to a prior work on the same topic, our findings indicate that predicting the news popularity at cold start is a difficul...

In this paper we offer a potential solution to the cold-start problem in group recommender systems. To do so, we use information about previous group recommendation events and copy ratings from a user who played a similar role in some previous group event. We show that copying in this way, i.e. conditioned on groups, is superior to copying nothing and also superior to copying ratings from the m...

New York University submitted two runs this year: a KBP Slot Filling run and a Cold Start run. The Slot Filling run used the same system as last year (Min et al. 2012), with only the minimal changes needed to address the revised specifications. These included changes in the provenance information and collapsing employee_of and member_of slots. We included the confidence estimation step (Li and ...

Multimedia annotation is central to its organization and retrieval – a task which tag recommendation systems attempt to simplify. We propose a photo tag recommendation system which automatically extracts semantics from visual and meta-data features to complement existing tags. Compared to standard content/tag-based models, these automatic tags provide a richer description of the image and espec...