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Recommender Systems Based on Detection Community in Academic Social Network

Boussaadi*, Smail, Aliane**, Hasina, Abdeldjalil Ouahabi***
2021 Zenodo  
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

Thomas Hofmann
2003 Proceedings of the 26th annual international ACM SIGIR conference on Research and development in informaion retrieval - SIGIR '03  
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

Thomas Hofmann
2003 Proceedings of the 26th annual international ACM SIGIR conference on Research and development in informaion retrieval - SIGIR '03  
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

Nathan N. Liu, Min Zhao, Qiang Yang
2009 Proceeding of the 18th ACM conference on Information and knowledge management - CIKM '09  
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

Magdalini Eirinaki, Michalis Vazirgiannis
2003 ACM Transactions on Internet Technology  
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]

Shohei Wakayama, Nisar Ahmed
2021 arXiv   pre-print
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]

Hafida Naïm, Mustapha Aznag, Nicolas Durand, Mohamed Quafafou
2016 Lecture Notes in Computer Science  
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]

Vagan Terziyan, Oleksandra Vitko
2003 Lecture Notes in Computer Science  
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

Padmaja CH V R
2018 International Journal of Advanced Research in Computer Science  
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

Liu Na, Lu Ying, Tang Xiao-Jun, Wang Hai-Wen, Xiao Peng, Li Ming-Xia
2016 2016 17th International Conference on Parallel and Distributed Computing, Applications and Technologies (PDCAT)  
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

Na Liu, Ying Lu, Xiao-Jun Tang, Hai-Wen Wang, Peng Xiao, Ming-Xia Li, T. Gong, T. Yang, J. Xu
2016 ITM Web of Conferences  
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]

Despoina Christou
2016 arXiv   pre-print
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

Jing Wang, Yafeng Yin, Hong Man
2008 EURASIP Journal on Image and Video Processing  
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

Rohit Nagori, G. Aghila
2011 2011 International Conference on Emerging Trends in Networks and Computer Communications (ETNCC)  
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)  
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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