PRemiSE : Personalized News Recommendation via Implicit Social Experts

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PRemiSE : Personalized News Recommendation via Implicit Social Experts. Overview. Introduction Expert model PRemiSE Experimental Future work. Google News. Existing news recommender systems. Content-based Recommenders bag-of-word model : document word - PowerPoint PPT Presentation

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PRemiSE:Personalized News Recommendation via Implicit Social ExpertsPersonalized News RecommendationImplicit 10Social Experts 1IntroductionExpert modelPRemiSEExperimentalFuture work

Overview2Google News

Existing news recommender systemsContent-based Recommenders bag-of-word model : document word topic models : document topic word Collaborative Filtering KNN MF PMFHybrid Recommenders combining social networkbag-of-word model topic models news CF KNN MF.PMF CF,MF 4data sparsitycold-start problem

PRemiSEincorporating content information, collaborative filtering and information diffusion in virtual social network into probabilistic matrix factorization.Two problems in previous studiesthe aggregation of user preference, semanticitem profiles and preferences of the most influential experts.

5Capable of handling the cold-start problemSemantically interpretableProducing better predictionsOur contribution6Expert Model

Follow-consuming relationship: If user Ui accesses to or rates an item Vj after Ue within a predefined time-window, then Ui has a follow-consuming relationship with Ue. 7An illustrative example of implicit social network

8Building Implicit Social NetworkStep0Compute time span & number of visits for each itemStep1Plot the time span number of visits find the abnormal items remove itStep2Build the graphbased on user-item accessing historyif U1 access the same item V after U2and access_time(U1) access_time(U2) < time_window , we say in the graph , there is an directed edges from U2 to U1.Step3 : Normalized weights

Time_window:find enough neighbors for each userprecisely find the right experts

Empirical study on a real data set

2a

10How probably the given user will follow the experts adoption on the same item?How probably any individual will follow the experts adoption on the same item?

Local Expert and Global Expert

find global expertnodenode1/MTTop K 11Matrix factorizationProbabilistic matrix factorizationPRemiSELearning in PRemiSEInference in PRemiSE

PRemiSEPMF(query)12Matrix factorization

user uitem i

Gradient descentuser-item user factors item factors useritemKfactorsuserKitemKR13Probabilistic matrix factorization

Linear probability model

PMFPMFMFmeanN

user factor vector item factor vectoruser factor vector item factor vector14PRemiSE

PMF information diffusion content

w1 w2 w2 w1

contentcontentrich

15Learning in PRemiSE

U V ()MuserNitemWterm R W U V ()

16See detailed in the paperOptimization Algorithmline search trust regionPaper 17Existing Item by Existing User

Existing Item by New User

New Item by Existing User

New Item by New UserInference in PRemiSE

old user new usernew usernews new itemnewsnewsitem 18Real-World Dataset 1. crawled from several popular news service websites 2. two types of elements : news stories and named entities.Rating 1. Rating in Storybinary 2. Rating in EntitynumericalExperimental Evaluation

news stories: named entitiesnews storiesstoriesentitiesstoriesentities

two types of elements for constructing implicit social graph: news stories and named entities.

Rating in Story 01Rating in Entity stories19Step1eliminate outlier items employing by ELKIStep2: The size of time-window set to be 8 days. we delete edges that are caused by a delayed co-consumption (9 days or even longer)step 3, we normalize the edges weight, and empirically set the edge weight threshold as 0.001

Construction of Networks

Parameters of Global Expert Model

DataSet1 news user old item 30items

p r p r T = 3 , R = 2.021Comparative Study

MF CF LDARMSE1. CF muisc film 2. MF CF LDALDA new items 3. K factorsitemRMSE MAE A/B RMSE22Cold-start problem

PRemiSE oop onp nop nnp allMF LDA CF onpMF: pseduo item factor user factor LDA itemldaLDA nopMF:pseduo user factor item factor LDA pesduo user topics item topics nnpMFgive a random guess CF nopitemonpusernnp 1. oopPRemiSEMF 2. CF 3. PRemiSEcold-start

23Semantics of factors

PEemiSElda factorsfactorsitemitemtopicword24We integrate this expert model with the content information and collaborative filtering, and propose a hybrid recommendation framework, called PRemiSE.effectively handle the cold-start problembetter Semantics Explanationbetter performance in recommendation accuracy

FUTURE WORK : social media & information diffusion model & export modelConclusion AND Future work25Thank you for your time!