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(Partial) user preference similarity as classification-based model similarity

Amancio Bouza, Abraham Bernstein
2014 Semantic Web Journal  
Furthermore, the concept of partial preference similarity based on a machine learning model is presented.  ...  Furthermore, the concept of partial preference similarity based on a machine learning model is presented.  ...  Algorithm 0% 10% 20% 30% 40% 50% 60% 70% 80% 90% Bouza and Bernstein / Partial User Preference Similarity as Classification-Based Model Similarity  ... 
doi:10.3233/sw-130099 fatcat:2qyi243ctfb2tk6mo6d3nbn7qe

Music preference learning with partial information

Yvonne Moh, Peter Orbanz, Joachim M. Buhmann
2008 Proceedings of the IEEE International Conference on Acoustics, Speech and Signal Processing  
Specifically, we address the classification of music types according to a user's preferences for a hearing aid application. The classifier has to operate under limited computational resources.  ...  We consider the problem of online learning in a changing environment under sparse user feedback.  ...  We consider a sub-task of such problems, the classification of music [2] into two classes (such as like-dislike) according to user preference. Algorithms should satisfy a number of requirements: 1.  ... 
doi:10.1109/icassp.2008.4518036 dblp:conf/icassp/MohOB08 fatcat:yhcpiwfc4fax3dzmtkf5prhnwe

Partial Relaxed Optimal Transport for Denoised Recommendation [article]

Yanchao Tan, Carl Yang Member, Xiangyu Wei, Ziyue Wu, Xiaolin Zheng
2022 arXiv   pre-print
Without proper denoising, RS models cannot effectively capture users' intrinsic preferences and the true interactions between users and items.  ...  Finally, to consider individual user behaviors for denoising, we develop a partial OT framework to adaptively relabel user-item interactions through a personalized thresholding mechanism.  ...  . , κ M } as the index of the threshold which can filter out users' noisy preferences. κ i denotes user i's threshold.  ... 
arXiv:2204.08619v1 fatcat:bqpsmfdh5ze4lh6dkyr5wsajwa

Partially Observable Reinforcement Learning for Dialog-based Interactive Recommendation

Yaxiong Wu, Craig Macdonald, Iadh Ounis
2021 Fifteenth ACM Conference on Recommender Systems  
Indeed, such partial observations of the users' preferences from their natural-language feedback make it challenging to correctly track the users' preferences over time, which can result in poor recommendation  ...  A dialog-based interactive recommendation task is where users can express natural-language feedback when interacting with the recommender system.  ...  ' accurate preferences over time with the partial observable preferences from the users' natural-language feedback, so as to make better recommendations with KNNs (i.e. the Generator).  ... 
doi:10.1145/3460231.3474256 fatcat:edicpksrcfdf3pg2cwrpwfol3i

Modeling Customer Engagement from Partial Observations

Jelena Stojanovic, Djordje Gligorijevic, Zoran Obradovic
2016 Proceedings of the 25th ACM International on Conference on Information and Knowledge Management - CIKM '16  
Additionally, modeling relations among different customers as a network can be beneficial for predictions at an individual level, as similar customers tend to have similar purchasing patterns.  ...  However, their demographic data, preferences, and other information that might be useful for building loyalty programs is often missing.  ...  as a similarity function (S).  ... 
doi:10.1145/2983323.2983854 dblp:conf/cikm/StojanovicGO16 fatcat:ycg6nxrejneu3ouvmi3b6lvtwy

Partial Convolution based Padding [article]

Guilin Liu, Kevin J. Shih, Ting-Chun Wang, Fitsum A. Reda, Karan Sapra, Zhiding Yu, Andrew Tao, Bryan Catanzaro
2018 arXiv   pre-print
We call it partial convolution based padding, with the intuition that the padded region can be treated as holes and the original input as non-holes.  ...  Extensive experiments with various deep network models on ImageNet classification and semantic segmentation demonstrate that the proposed padding scheme consistently outperforms standard zero padding with  ...  We show that partial convolution based padding achieves better accuracy as well as faster convergence than the default zero padding on image classification.  ... 
arXiv:1811.11718v1 fatcat:uxhvl4h2lje4dbjnocfa3f4vci

Research on the application of spatial partial differential equation in user oriented information mining

Shaofei Wu
2020 Alexandria Engineering Journal  
In view of the problems of traditional methods, this paper puts forward the technology of association mining based on the mathematical model of partial differential classification, and makes an experimental  ...  This method can be used for any complex object research similar to user intention and the evaluation mechanism of a research object.  ...  Mathematical model of partial differential classification and user intention identification User intention Rankbrain is an artificial intelligence algorithm based on machine learning, which was launched  ... 
doi:10.1016/j.aej.2020.01.047 fatcat:2jlahbe5qzb6bbrydlc77jdvfa

Partial results in database systems

Willis Lang, Rimma V. Nehme, Eric Robinson, Jeffrey F. Naughton
2014 Proceedings of the 2014 ACM SIGMOD international conference on Management of data - SIGMOD '14  
However, in some environments we have encountered, users prefer to continue query execution even in the presence of failures (e.g., the unavailability of certain data sources), and receive a "partial"  ...  a partial result.  ...  (Section 2) • We present a partial result analysis framework with four models that determine the degree of our partial result classification precision.  ... 
doi:10.1145/2588555.2612176 dblp:conf/sigmod/LangNRN14 fatcat:mj5p3ubbqbbwxkoiclpwccyo7a


M.Phil R.Navin Kumar M.C.A., S.Sneha
2022 Zenodo  
This project proposes a multi-strategy sentiment analysis method with semantic similarity to solve the problem with partial phrase matching.  ...  Naïve Bayes classification is also applied to find the probability of data distribution in various category of data set. The project is designed using R Studio 1.0.  ...  By computing similarity of the users based on their rating patterns, the system provides the user with similar users (neighbors) and items they recommend.  ... 
doi:10.5281/zenodo.6410034 fatcat:bwt7hjsbkzcvjn3egegmytc4hi

Partially Bayesian variable selection in classification trees

Xuming He, Douglas A. Noe
2008 Statistics and its Interface  
Tree-structured models for classification may be split into two broad categories: those that are completely datadriven and those that allow some direct user interaction during model construction.  ...  Second, by giving an expert's preferred variables priority, we reduce the chance that a spurious variable will appear in the model.  ...  One potential drawback of these older tree-based classification models, however, is that they are completely datadriven.  ... 
doi:10.4310/sii.2008.v1.n1.a13 fatcat:kffbpk2xobcaradyoiyxchaasy

PNA: Partial Network Alignment with Generic Stable Matching

Jiawei Zhang, Weixiang Shao, Senzhang Wang, Xiangnan Kong, Philip S. Yu
2015 2015 IEEE International Conference on Information Reuse and Integration  
Connections between accounts of anchor users in different networks are defined as anchor links and networks partially aligned by anchor links can be represented as partially aligned networks.  ...  The shared users between different networks are called anchor users, while the remaining unshared users are named as non-anchor users.  ...  [25] propose to calculate the similarity scores among users based on meta path in bibliographical network. Sun et al.  ... 
doi:10.1109/iri.2015.34 dblp:conf/iri/ZhangSWKY15 fatcat:sbg7v2ljx5fzhhii5hvwyxea5m

Incremental learning to rank with partially-labeled data

Kye-Hyeon Kim, Seungjin Choi
2009 Proceedings of the 2009 workshop on Web Search Click Data - WSCD '09  
We introduce a matrix-fee technique where we compute the eigenvectors of a huge similarity matrix without constructing the matrix itself.  ...  Then we present an incremental algorithm to learn a linear ranking function using features determined by projecting data onto the eigenvectors of the similarity matrix, which can be applied to a task of  ...  base preference vectors, meaning that a variety of user interest is restricted to given hub pages.  ... 
doi:10.1145/1507509.1507513 dblp:conf/wsdm/KimC09 fatcat:mxhvmnxskfb7bhejldauw7hozm

E-mail categorization using partially related training examples

Maya Sappelli, Suzan Verberne, Wessel Kraaij
2014 Proceedings of the 5th Information Interaction in Context Symposium on - IIiX '14  
With the network algorithm, it is possible to use documents as training material for e-mail categorization without user intervention.  ...  classification methods.  ...  Additionally, the clustering is based on similarities between messages, and it is by no means certain that these clusters are the clusters the user is looking for.  ... 
doi:10.1145/2637002.2637014 dblp:conf/iiix/SappelliVK14 fatcat:gbojp665czdonpzxmfmphjnarq

Partial Highest Possible Edge Analysis For Interactive Image Accessibility

Sridevi R
2013 Zenodo  
Among RF schemes, the most popular technique is SVM based RF scheme. When SVM is used as a classifier in RF, there are two strategies.  ...  One strategy is to display the most positive images and use them as the training samples.  ...  classification problem.  ... 
doi:10.5281/zenodo.821738 fatcat:gte2pkga5rd6tanjcbxpeftphq

The Partially Observable Hidden Markov Model and its Application to Keystroke Dynamics [article]

John V. Monaco, Charles C. Tappert
2017 arXiv   pre-print
The partially observable hidden Markov model is an extension of the hidden Markov Model in which the hidden state is conditioned on an independent Markov chain.  ...  This structure is motivated by the presence of discrete metadata, such as an event type, that may partially reveal the hidden state but itself emanates from a separate process.  ...  These models are referred to as partly-HMM [42] , partially-HMM [43] , and context-HMM [44] .  ... 
arXiv:1607.03854v7 fatcat:kskylfx4kjfvtgy6ket75w34ee
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