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...

Collaborative filtering is one of the most widely used techniques for recommendation system which has been successfully applied in many applications. However, it suffers from the cold start users who rate only a small fraction of the available items. In addition, these methods can not indicate confidence they are for recommendation. Trust-based recommendation methods assume the additional knowl...

The main aim of a credit scoring model is the classification of the loan customers into two classes, reliable and unreliable customers, on the basis of their potential capability to keep up with their repayments. Nowadays, credit scoring models are increasingly in demand, due to the consumer credit growth. Such models are usually designed on the basis of the past loan applications and used to e...

Case Amplification can improve the accuracy of a collaborative filtering (CF) algorithm with no extra space overhead by amplifying the effect of close candidates in the prediction. However, in a cold start scenario, the traditional Case Amplification on an item-based prediction can reduce accuracy. Given a small known set, Case Amplification can give a mediocre candidate an unsuitable amplifica...

Sistemas de recomendação de filtragem colaborativa encontram diversos desafios para alcançar acuradas recomendações. Um deles é o denominado cold-start de item que é o fato do sistema não ser capaz de recomendar itens que nunca foram avaliados por outros usuários. Neste artigo apresentamos uma proposta para minimizar esse problema, o XPrefRec. Trata-se de um sistema de recomendação híbrido, seg...

Previous research showed that choice-based preference elicitation can be successfully used to reduce effort during user cold start, resulting in an improved user satisfaction with the recommender system. However, it has also been shown to result in highly popular recommendations. In the present study we investigate if trailers reduce this bias to popular recommendations by informing the user an...