Trust has been explored by many researchers in the past as a successful solution for assisting recommender systems. Even though the approach of using a web-of-trust scheme for assisting the recommendation production is well adopted, issues like the sparsity problem have not been explored adequately so far with regard to this. In this work we are proposing and testing a scheme that uses the exis...

In (Masthoff, 2004), we have investigated techniques for combining individual user models to make recommendations to a group. In Masthoff (2003), we have shown how these techniques might also be applicable when adapting to an individual: for solving the cold start problem and for combining ratings on multiple criteria. In this paper, we look at combining multiple criteria in more detail. We pre...

A study on ignition and early combustion development has been performed in an optical engine at incylinder thermodynamic conditions representative of those occurring during the cold start of a modern DI Diesel engine for passenger cars at an ambient temperature of -20oC. A general description of the process has been derived from in-cylinder pressure analysis and high speed imaging, and the effe...

Matrix factorization has now become a dominant solution for personalized recommendation on the Social Web. To alleviate the cold start problem, previous approaches have incorporated various additional sources of information into traditional matrix factorization models. These upgraded models, however, achieve only “marginal” enhancements on the performance of personalized recommendation. Therefo...

The potential benefit of integrating contextual information for recommendation has received much research attention recently, especially with the ever-increasing interest in mobile-based recommendation services. However, context based recommendation research is limited due to the lack of standard evaluation data with contextual information and reliable technology for extracting such information...

The University of Washington participated in Cold Start Slot Filling for TAC-KBP 2015 with a system that combines three methods: 1) its 2013 OPENIE-KBP system (Soderland et al., 2013); 2) a novel Implicit Relation Information Extractor (IMPLIE); and 3) MULTIR extractor (Hoffmann et al., 2011), trained on a combination of distant supervision and crowdsourced training instances. These three metho...

Recommender system has become an indispensable component in many e-commerce sites. One major challenge that largely remains open is the coldstart problem, which can be viewed as an ice barrier that keeps the cold-start users/items from the warm ones. In this paper, we propose a novel rating comparison strategy (RAPARE) to break this ice barrier. The center-piece of our RAPARE is to provide a fi...

This paper focuses on the new users cold-start issue in the context of recommender systems. New users who do not receive pertinent recommendations may abandon the system. In order to cope with this issue, we use active learning techniques. These methods engage the new users to interact with the system by presenting them with a questionnaire that aim to understand their preferences to the relate...

A happy New Year!?even though it has had a very cold start. Our thoughts have been with those of you, who besides ourselves in London, have had to battle with the bitter weather. In particular we have thought of our 35 residents at Parnham (none younger than 70 and one in her 90s) and the staff, who have had a particularly difficult time in Dorset. Dr. Noel Harris draws attention in a special a...

In order to make personalized recommendations, many collaborative music recommender systems (CMRS) focused on capturing precise similarities among users or items based on user historical ratings. Despite the valuable information from audio features of music itself, however, few studies have investigated how to directly extract and utilize information from music for personalized recommendation i...