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Filters
Recommender Systems Based on Detection Community in Academic Social Network
2021
Zenodo
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In this context, we propose a hybrid approach to recommending scientific papers that uses collaborative filtering combined with semantic exploration and extraction of latent themes by techniques combining ...
useful information, hence the need for a filtering process to reduce this information overload. ...
a new probabilistic approach, which combines collaborative filtering based on latent topics and content analysis based on thematic modeling. our algorithm provides interpretable profiles of researchers ...
doi:10.5281/zenodo.5801852
fatcat:wufjafk54bhe5o25f5zrvonqyi
Collaborative filtering via gaussian probabilistic latent semantic analysis
2003
Proceedings of the 26th annual international ACM SIGIR conference on Research and development in informaion retrieval - SIGIR '03
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In this paper, we describe a new model-based algorithm designed for this task, which is based on a generalization of probabilistic latent semantic analysis to continuous-valued response variables. ...
Collaborative filtering aims at learning predictive models of user preferences, interests or behavior from community data, i.e. a database of available user preferences. ...
GAUSSIAN PROBABILISTIC LATENT SEMANTIC MODEL
Co-occurrence Model We would like to discuss a simple model for co-occurrence data first, which is known as probabilistic latent semantic analysis (pLSA) ...
doi:10.1145/860435.860483
dblp:conf/sigir/Hofmann03
fatcat:twtqbw7iczcsnhn3rfbvjcgrc4
Collaborative filtering via gaussian probabilistic latent semantic analysis
2003
Proceedings of the 26th annual international ACM SIGIR conference on Research and development in informaion retrieval - SIGIR '03
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In this paper, we describe a new model-based algorithm designed for this task, which is based on a generalization of probabilistic latent semantic analysis to continuous-valued response variables. ...
Collaborative filtering aims at learning predictive models of user preferences, interests or behavior from community data, i.e. a database of available user preferences. ...
GAUSSIAN PROBABILISTIC LATENT SEMANTIC MODEL
Co-occurrence Model We would like to discuss a simple model for co-occurrence data first, which is known as probabilistic latent semantic analysis (pLSA) ...
doi:10.1145/860480.860483
fatcat:pkekcyzntzabdgr56skxr5mida
Probabilistic latent preference analysis for collaborative filtering
2009
Proceeding of the 18th ACM conference on Information and knowledge management - CIKM '09
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A central goal of collaborative filtering (CF) is to rank items by their utilities with respect to individual users in order to make personalized recommendations. ...
From a user's observed ratings, we extract his preferences in the form of pairwise comparisons of items which are modeled by a mixture distribution based on Bradley-Terry model. ...
Probabilistic latent semantic analysis (pLSA) [7] is a widely used latent variable model for co-occurrence data based on mixture distributions. ...
doi:10.1145/1645953.1646050
dblp:conf/cikm/LiuZY09
fatcat:5bj4u25tobf27ph7cokzomcvzy
Web mining for web personalization
2003
ACM Transactions on Internet Technology
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Vazirgiannis
Web Mining for Web Personalization
28
PKDD 2005
Web log data
Analysis Techniques
Statistical
Content/Collaborative filtering
Data mining
Probabilistic
Link analysis
M.Eirinaki ...
Vazirgiannis
Web Mining for Web Personalization
32
PKDD 2005
Web log data
Analysis Techniques
Statistical
Content/Collaborative filtering
Data mining
Probabilistic
Link analysis
M.Eirinaki ...
doi:10.1145/643477.643478
fatcat:xeex2a6y5bfn5ltkwlcugauqxa
Probabilistic Semantic Data Association for Collaborative Human-Robot Sensing
[article]
2021
arXiv
pre-print
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The new PSDA method is incorporated into a hybrid Bayesian data fusion scheme which uses Gaussian mixture priors for object states and softmax functions for semantic human sensor observation likelihoods ...
In collaborative human-robot semantic sensing problems, e.g. for scientific exploration, robots could potentially overtrust information given by a human partner, resulting in suboptimal state estimation ...
As a result, the PHD filter and other finite set statistics (FISTT) methods [23] cannot be combined in a straightforward way with probabilistic algorithms to support optimal planning under uncertainty ...
arXiv:2110.09621v1
fatcat:othezjr2r5e33kn75v2jf5a6ky
Semantic Pattern Mining Based Web Service Recommendation
[chapter]
2016
Lecture Notes in Computer Science
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We propose a new content-based recommendation system. ...
Its originality comes from the combination of probabilistic topic models and pattern mining to capture the maximal common semantic of sets of services. ...
They are a family of generative probabilistic models based on the assumption that documents are generated by a mixture of topics where topics are probability distributions on words. ...
doi:10.1007/978-3-319-46295-0_26
fatcat:pkuqakkbongabdzp5cho4pbbuq
Bayesian Metanetworks for Modelling User Preferences in Mobile Environment
[chapter]
2003
Lecture Notes in Computer Science
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The works [2], [11] on probabilistic model-based collaborative filtering introduce a graphical model for probabilistic relationships -an alternative to the Bayesian network -called the dependency network ...
In modern adaptive systems content-based and collaborative filtering are combined [6], [8] . ...
As location of mobile user is being considered as a very important determinative attribute in modelling of user's decision making, the applications of Bayesian Metanetworks for mobile location-based systems ...
doi:10.1007/978-3-540-39451-8_27
fatcat:rwarcmdehjgd5hlljn4shbgjz4
PROBABILISTIC TOPIC MODELING AND ITS VARIANTS – A SURVEY
2018
International Journal of Advanced Research in Computer Science
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Topic modeling is one of the fast-growing research areas as there is a huge increase in internet users. ...
In this study, we are presenting a survey on the advanced algorithms that are used in topic modeling. ...
LDA is a generative probabilistic topic modeling based on statistical Bayesian topic models. It is a very widely used algorithm in text mining. ...
doi:10.26483/ijarcs.v9i3.6107
fatcat:fayeojqtjbdctkuvamkns7gmfa
Improved Collaborative Filtering Algorithm Using Topic Model
2016
2016 17th International Conference on Parallel and Distributed Computing, Applications and Technologies (PDCAT)
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In this paper, we proposed collaborative filtering algorithm using topic model. ...
We describe user-item matrix as document-word matrix and user are represented as random mixtures over item, each item is characterized by a distribution over users. ...
Given the parameters and , the joint distribution of a topic mixture , a set of topics z, and a set of N words w is given by:
Collaborative Filtering Recommenders using Topic Model In collaborative ...
doi:10.1109/pdcat.2016.079
dblp:conf/pdcat/LiuLTWXL16
fatcat:lgpylxjv6nbthl7nq3q2mqn25i
Improved Collaborative Filtering Algorithm using Topic Model
2016
ITM Web of Conferences
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In this paper, we proposed collaborative filtering algorithm using topic model. ...
We describe user-item matrix as document-word matrix and user are represented as random mixtures over item, each item is characterized by a distribution over users. ...
Given the parameters and , the joint distribution of a topic mixture , a set of topics z, and a set of N words w is given by:
Collaborative Filtering Recommenders using Topic Model In collaborative ...
doi:10.1051/itmconf/20160705008
fatcat:afoylljhcnbuze5muszdj36yge
Feature extraction using Latent Dirichlet Allocation and Neural Networks: A case study on movie synopses
[article]
2016
arXiv
pre-print
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The recommended movies of this approach are compared with the recommended movies from IMDB, which use a collaborative filtering recommendation approach, to show that our two models could constitute either ...
an alternative or a supplementary recommendation approach. ...
Precisely, in content-based and in collaborative filtering recommendation methods, the algorithms used are divided in memory-based and in model-based approaches. ...
arXiv:1604.01272v1
fatcat:tcmbvywmazc5bnjj3h73msszha
Multiple Human Tracking Using Particle Filter with Gaussian Process Dynamical Model
2008
EURASIP Journal on Image and Video Processing
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With the particle filter Gaussian process dynamical model (PFGPDM), a highdimensional target trajectory dataset of the observation space is projected to a low-dimensional latent space in a nonlinear probabilistic ...
We present a particle filter-based multitarget tracking method incorporating Gaussian process dynamical model (GPDM) to improve robustness in multitarget tracking. ...
by distributed and collaborative intelligence (e.g., a social/P2P network) and let them benefit from the processes taking place in such a network (e.g., tagging, collaborative filtering). ...
doi:10.1155/2008/969456
fatcat:3bzflhkm4fczreoqcywmlmuwrm
LDA based integrated document recommendation model for e-learning systems
2011
2011 International Conference on Emerging Trends in Networks and Computer Communications (ETNCC)
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a similarity measurement to content-based recommendation approach. ...
Personalized recommendation model is a specific type of information filtering system used to identify a set of objects that are relevant to a learner. ...
A well known methods of this type is TF-IDF, Latent Semantic Indexing (LSI), probabilistic latent semantic indexing(p-LSI) [11] . ...
doi:10.1109/etncc.2011.6255892
fatcat:kkqepkxofrb4xhubgkzjg7gf2m
Social Network Friend Recommendation System Using Semantic Web
2016
International Journal of Science and Research (IJSR)
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In this paper, a personalised friendbook recommendation mobile application is presented, which is a novel semantic based friend recommendation system for social networking services. ...
It recommends friend to system users based on their life styles, habits, locations or user profiles. ...
For examples Bian and Holtzman [6] discovered a collaborative filtering friend recommendation system such as MatchMaker based on personality matching. ...
doi:10.21275/v5i1.nov152976
fatcat:fdjpn7tkpvfkjl7ifuadyp57ce
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