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Task-Based User Modelling for Personalization via Probabilistic Matrix Factorization

Rishabh Mehrotra, Emine Yilmaz, Manisha Verma
2014 ACM Conference on Recommender Systems  
We introduce a novel approach to user modelling for behavioral targeting: task-based user representation and present an approach based on search task extraction from search logs wherein users are represented  ...  More specifically, we construct a user-task association matrix and borrow insights from Collaborative Filtering to learn low-dimensional factor model wherein the interests/preferences of a user are determined  ...  Probabilistic Matrix Factorization for User Representations: We wish to extract task-based user vector representations by jointly mapping users and tasks to a joint latent factor space.  ... 
dblp:conf/recsys/MehrotraYV14 fatcat:2wflufurobei7ghrbturbevtg4

Terms, Topics & Tasks

Rishabh Mehrotra, Emine Yilmaz
2015 Proceedings of the 2015 International Conference on Theory of Information Retrieval - ICTIR '15  
Through extensive evaluation via query recommendations and user cohort analysis, we demonstrate the value of considering topic specific task information while developing user models.  ...  for personalization.  ...  Probabilistic Matrix Factorization for User Representations: We wish to extract task-based user vector representations by jointly mapping users and tasks to a joint latent factor space.  ... 
doi:10.1145/2808194.2809467 dblp:conf/ictir/MehrotraY15 fatcat:jmrwia42xnfhpl6msons3dnk6q

A Geographical Factor of Interest Recommended Strategies in Location Based Social Networks

Bulusu Rama, K Sai Prasad, Ayesha Sultana, K Shekar
2018 International Journal of Engineering & Technology  
First, we quickly introduce the shape and records traits of LBSNs, then we current a formalization of user modeling for POI suggestions in LBSNs.  ...  data-based consumer modeling, geographical information-based consumer modeling, spatial-temporal information-based consumer modeling, and geo-social information-based consumer modeling.  ...  The important task is how to comprise users' social family members into the popular models (such as matrix factorization).  ... 
doi:10.14419/ijet.v7i3.27.17649 fatcat:xbhdedvfkbe6tgacryx7fbwyke

A Survey on Social Circle Influenced Personalized Recommendation System

2015 International Journal of Science and Research (IJSR)  
This survey paper is to study various traditional recommendation techniques and main three social aspects, and how these factors are to be fused into a personalized recommendation model to give efficient  ...  Traditional recommendation techniques are limited because they do not consider factors of social relation in the social network for giving recommendation.  ...  This is based on the probabilistic matrix factorization [4] , which uses the low rank matrix.  ... 
doi:10.21275/v4i11.nov151815 fatcat:6rkdgsqdufgmhemcswjr5rn6aa

Neural Personalized Ranking via Poisson Factor Model for Item Recommendation

Yonghong Yu, Li Zhang, Can Wang, Rong Gao, Weibin Zhao, Jing Jiang
2019 Complexity  
Specifically, we firstly develop a ranking-based poisson factor model, which combines the poisson factor model and the Bayesian personalized ranking.  ...  After that, we propose a neural personalized ranking model on top of the ranking-based poisson factor model, named NRPFM, to capture the complex structure of user-item interactions.  ...  This observation indicates that the Poisson factor model is more suitable for modeling uses' implicit frequency feedback than probabilistic matrix factorization.  ... 
doi:10.1155/2019/3563674 fatcat:rc4kaow6fzg5dpppucsjdcewsy

Recommender System Incorporating User Personality Profile through Analysis of Written Reviews

Peter Potash, Anna Rumshisky
2016 ACM Conference on Recommender Systems  
In this work we directly incorporate user personality profiles into the task of matrix factorization for predicting user ratings.  ...  We use Kernelized Probabilistic Matrix Factorization to integrate the personality profile of the users as side-information.  ...  with probabilistic matrix factorization to predict user ratings for movies.  ... 
dblp:conf/recsys/PotashR16 fatcat:otgggc7h2bbzdiu2jkjz66utvu

Enhanced tensor factorization framework using non-negative and probabilistic tensor factorization approaches for microblogging content propagation modelling

N. Baggyalakshmi, A. Kavitha, A. Marimuthu
2017 International Journal of Engineering & Technology  
The propagation occurrences are signified as a tensor factorization model so-called V2S is presented with the aim of deriving the behavioral aspects via which the content propagation is designed.  ...  In existing studies, four user behavior aspects were used by the content propagation model that is to say topic virality, user's position, user susceptibility and user virality.  ...  The evaluations are done amid previous V2S-based probabilistic factorization model (V2S-PF), V2S-based numerical factorization model (V2S-NF) and the M-V2S-based HOSVD (MV2S-HOSVD) and M-V2S based PLTF  ... 
doi:10.14419/ijet.v7i1.1.9204 fatcat:7iauugmeaja7jbtk62ri4l4adq

Probabilistic latent class models for predicting student performance

Suleyman Cetintas, Luo Si, Yan Ping Xin, Ron Tzur
2013 Proceedings of the 22nd ACM international conference on Conference on information & knowledge management - CIKM '13  
Predicting student performance is an important task for many core problems in intelligent tutoring systems. This paper proposes a set of novel probabilistic latent class models for the task.  ...  The most effective probabilistic model utilizes all available information about the educational content and users/students to jointly identify hidden classes of students and educational content that share  ...  are compared to a large number of baselines including traditional regression-based models as well as recommendation system techniques such as collaborative filtering and matrix factorization.  ... 
doi:10.1145/2505515.2507832 dblp:conf/cikm/CetintasSXT13 fatcat:zyqdn6xmwne6dffeaow74z35ba

A Web Recommendation Technique Based on Probabilistic Latent Semantic Analysis [chapter]

Guandong Xu, Yanchun Zhang, Xiaofang Zhou
2005 Lecture Notes in Computer Science  
In this work, we propose a Web recommendation framework incorporating Web usage mining technique based on Probabilistic Latent Semantic Analysis (PLSA) model.  ...  With the discovered user access pattern, we then present user more interested content via collaborative recommendation.  ...  For the given aspect model, suppose that there is a latent factor space i j s p can convey the user navigational interests over the kdimensional latent factor space.  ... 
doi:10.1007/11581062_2 fatcat:iyefq5wyprbljjgtjk2mb6sfwy

Multi-modal multi-correlation person-centric news retrieval

Zechao Li, Jing Liu, Xiaobin Zhu, Hanqing Lu
2010 Proceedings of the 19th ACM international conference on Information and knowledge management - CIKM '10  
Second, a Multi-correlation Probabilistic Matrix Factorization (MPMF) algorithm is proposed to complete and refine the three correlations.  ...  Different from traditional Probabilistic Matrix Factorization (PMF), the proposed MPFM additionally considers the event correlations and the entity correlations as well as the event-entity correlations  ...  To fully employ the multi-correlation information, we proposed a Multi-correlation Probabilistic Matrix Factorization model (MPMF) to analyze news entity correlation, news event correlation and entity-event  ... 
doi:10.1145/1871437.1871464 dblp:conf/cikm/LiLZL10 fatcat:tghxviynnve63gwurutveq3a3u

Web usage mining based on probabilistic latent semantic analysis

Xin Jin, Yanzan Zhou, Bamshad Mobasher
2004 Proceedings of the 2004 ACM SIGKDD international conference on Knowledge discovery and data mining - KDD '04  
In this paper, we develop a unified framework for the discovery and analysis of Web navigational patterns based on PLSA.  ...  Probabilistic Latent Semantic Analysis (PLSA) is particularly useful in this context, since it can uncover latent semantic associations among users and pages based on the co-occurrence patterns of these  ...  The probabilistic latent factor model can be described as the following generative model: 1. select a user session ui from U with probability P r(ui), 2. pick a latent factor z k with probability P r(z  ... 
doi:10.1145/1014052.1014076 dblp:conf/kdd/JinZM04 fatcat:ooilgclhcbfjzgcsks6rhfpcya

Explainable Recommendation via Multi-Task Learning in Opinionated Text Data

Nan Wang, Hongning Wang, Yiling Jia, Yue Yin
2018 The 41st International ACM SIGIR Conference on Research & Development in Information Retrieval - SIGIR '18  
Two companion learning tasks of user preference modeling for recommendation and opinionated content modeling for explanation are integrated via a joint tensor factorization.  ...  In this work, we develop a multi-task learning solution for explainable recommendation.  ...  ACKNOWLEDGEMENT We thank the anonymous reviewers for their insightful comments. This paper is based upon work supported by the National Science Foundation under grant IIS-1553568 and CNS-1646501.  ... 
doi:10.1145/3209978.3210010 dblp:conf/sigir/WangWJY18 fatcat:mt27zjgkxzhsvodcgq36f4ncr4

Fused Matrix Factorization with Geographical and Social Influence in Location-Based Social Networks

Chen Cheng, Haiqin Yang, Irwin King, Michael Lyu
2021 PROCEEDINGS OF THE THIRTIETH AAAI CONFERENCE ON ARTIFICIAL INTELLIGENCE AND THE TWENTY-EIGHTH INNOVATIVE APPLICATIONS OF ARTIFICIAL INTELLIGENCE CONFERENCE  
To solve this task, matrix factorization is a promising tool due to its success in recommender systems.  ...  Personalized Point-of-interest (POI) recommendation is a significant task in LBSNs since it can help targeted users explore their surroundings as well as help third-party developers to provide personalized  ...  (one for Focused Grant Project "Mobile 2014" and one for Google Research Awards).  ... 
doi:10.1609/aaai.v26i1.8100 fatcat:7ows63nvovgpngqbpkvzfci6ym

Rating Prediction based on Social Sentiment from Textual Reviews

R. G., S. R.
2019 International Journal of Computer Applications  
At last, we have a tendency to fuse 3 factors-user sentiment similarity, social sentimental influence, and associated item's name similarity into our recommender system to form a correct rating prediction  ...  During this work, we have a tendency to propose a sentiment-based rating prediction methodology (RPS) to enhance prediction accuracy in recommender systems.  ...  In this paper, three social factors, personal interest, interpersonal interest similarity, and interpersonal influence, fuse into a unified personalized recommendation model based on probabilistic matrix  ... 
doi:10.5120/ijca2019919085 fatcat:mbcizsubsbhgdejjllcloojlsm

Why I like it

Yichao Lu, Ruihai Dong, Barry Smyth
2018 Proceedings of the 12th ACM Conference on Recommender Systems - RecSys '18  
ABSTRACT We describe a novel, multi-task recommendation model, which jointly learns to perform rating prediction and recommendation explanation by combining matrix factorization, for rating prediction,  ...  and adversarial sequence to sequence learning for explanation generation.  ...  We examine the following baseline methods together with our multi-task learning model: (i) PMF: Probabilistic Matrix Factorization (PMF) [33] is a factorbased model from a probabilistic point of view  ... 
doi:10.1145/3240323.3240365 dblp:conf/recsys/LuDS18 fatcat:4ipls74wfndzznwho5dw7flcce
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