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2,555
Tag recommendations based on tensor dimensionality reduction
- In RecSys ’08: Proc. of the ACM Conference on Recommender systems, 43–50
, 2008
"... Social tagging is the process by which many users add metadata in the form of keywords, to annotate and categorize information items (songs, pictures, web links, products etc.). Collaborative tagging systems recommend tags to users based on what tags other users have used for the same items, aiming ..."
Abstract
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Cited by 54 (1 self)
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Value Decomposition (HOSVD) technique. We perform experimental comparison of the proposed method against two state-of-the-art tag recommendations algorithms with two real data sets (Last.fm and BibSonomy). Our results show significant improvements in terms of effectiveness measured through recall/precision.
Semi-Supervised Tag Recommendation- Using Untagged Resources to Mitigate Cold-Start Problems
"... Abstract. Tag recommender systems are often used in social tagging systems, a popular family of Web 2.0 applications, to assist users in the tagging process. But in cold-start situations i.e., when new users or resources enter the system, state-of-the-art tag recommender systems perform poorly and a ..."
Abstract
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Cited by 3 (1 self)
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Abstract. Tag recommender systems are often used in social tagging systems, a popular family of Web 2.0 applications, to assist users in the tagging process. But in cold-start situations i.e., when new users or resources enter the system, state-of-the-art tag recommender systems perform poorly
Toward the next generation of recommender systems: A survey of the state-of-the-art and possible extensions
- IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING
, 2005
"... This paper presents an overview of the field of recommender systems and describes the current generation of recommendation methods that are usually classified into the following three main categories: content-based, collaborative, and hybrid recommendation approaches. This paper also describes vario ..."
Abstract
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Cited by 1490 (23 self)
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This paper presents an overview of the field of recommender systems and describes the current generation of recommendation methods that are usually classified into the following three main categories: content-based, collaborative, and hybrid recommendation approaches. This paper also describes
A Maximum Entropy Model for Part-Of-Speech Tagging
, 1996
"... This paper presents a statistical model which trains from a corpus annotated with Part-OfSpeech tags and assigns them to previously unseen text with state-of-the-art accuracy(96.6%). The model can be classified as a Maximum Entropy model and simultaneously uses many contextual "features" t ..."
Abstract
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Cited by 580 (1 self)
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This paper presents a statistical model which trains from a corpus annotated with Part-OfSpeech tags and assigns them to previously unseen text with state-of-the-art accuracy(96.6%). The model can be classified as a Maximum Entropy model and simultaneously uses many contextual "
Automatic Tagging of Audio: The State-of-the-Art
"... Recently there has been a great deal of attention paid to the automatic prediction of tags for music and audio in general. Social tags are usergenerated keywords associated with some resource on the Web. In the case of music, social tags have become an important component of ``Web 2.0' ' r ..."
Abstract
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Cited by 22 (5 self)
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; recommender systems. There have been many attempts at automatically applying tags to audio for different purposes: database management, music recommendation, improved humancomputer interfaces, estimating similarity among songs, and so on. Many published results show that this problem can be tackled using
Folksonomy-based Recommender Systems -- A State-of-the-Art Review
"... Collaborative tagging systems, also known as folksonomies, have grown in popularity over the Web on account of their simplicity to organize several types of content (e.g. Web pages, pictures and video) using open-ended tags. The rapid adoption of these systems has lead to an increasing amount of use ..."
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of users providing information about themselves and, at the same time, a growing corpus of rich social knowledge that can be exploited by recommendation technologies. In this context, tripartite relationships between users, resources and tags contained in folksonomies set new challenges for knowledge
Open information extraction from the web
- IN IJCAI
, 2007
"... Traditionally, Information Extraction (IE) has focused on satisfying precise, narrow, pre-specified requests from small homogeneous corpora (e.g., extract the location and time of seminars from a set of announcements). Shifting to a new domain requires the user to name the target relations and to ma ..."
Abstract
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Cited by 373 (39 self)
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page corpus that compare TEXTRUNNER with KNOWITALL, a state-of-the-art Web IE system. TEXTRUNNER achieves an error reduction of 33% on a comparable set of extractions. Furthermore, in the amount of time it takes KNOWITALL to perform extraction for a handful of pre-specified relations, TEXTRUNNER
A unified architecture for natural language processing: Deep neural networks with multitask learning
, 2008
"... We describe a single convolutional neural network architecture that, given a sentence, outputs a host of language processing predictions: part-of-speech tags, chunks, named entity tags, semantic roles, semantically similar words and the likelihood that the sentence makes sense (grammatically and sem ..."
Abstract
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Cited by 340 (13 self)
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for the shared tasks. We show how both multitask learning and semi-supervised learning improve the generalization of the shared tasks, resulting in stateof-the-art performance. 1.
Flickr tag recommendation based on collective knowledge
- IN WWW ’08: PROC. OF THE 17TH INTERNATIONAL CONFERENCE ON WORLD WIDE WEB
, 2008
"... Online photo services such as Flickr and Zooomr allow users to share their photos with family, friends, and the online community at large. An important facet of these services is that users manually annotate their photos using so called tags, which describe the contents of the photo or provide addit ..."
Abstract
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Cited by 224 (1 self)
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photos and what information is contained in the tagging. Based on this analysis, we present and evaluate tag recommendation strategies to support the user in the photo annotation task by recommending a set of tags that can be added to the photo. The results of the empirical evaluation show that we can
A Critique of Software Defect Prediction Models
- IEEE TRANSACTIONS ON SOFTWARE ENGINEERING
, 1999
"... Many organizations want to predict the number of defects (faults) in software systems, before they are deployed, to gauge the likely delivered quality and maintenance effort. To help in this numerous software metrics and statistical models have been developed, with a correspondingly large literatur ..."
Abstract
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Cited by 292 (21 self)
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literature. We provide a critical review of this literature and the state-of-the-art. Most of the wide range of prediction models use size and complexity metrics to predict defects. Others are based on testing data, the “quality ” of the development process, or take a multivariate approach. The authors
Results 1 - 10
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2,555
