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PRemiSE: Personalized News Recommendation via Implicit Social Experts

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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Page 1: PRemiSE : Personalized  News Recommendation  via  Implicit  Social Experts

PRemiSE:Personalized News Recommendation via Implicit Social Experts

Page 2: PRemiSE : Personalized  News Recommendation  via  Implicit  Social Experts

1. Introduction2. Expert model3. PRemiSE4. Experimental5. Future work

Overview

Page 3: PRemiSE : Personalized  News Recommendation  via  Implicit  Social Experts

Google News

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Existing news recommender systems

Content-based Recommenders bag-of-word model : document word topic models : document topic word Collaborative Filtering KNN MF PMF Hybrid Recommenders combining social network

Page 5: PRemiSE : Personalized  News Recommendation  via  Implicit  Social Experts

1. data sparsity2. cold-start problem

PRemiSE: incorporating content information, collaborative filtering and information diffusion in virtual social network into probabilistic matrix factorization.

Two problems in previous studies

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1. Capable of handling the cold-start problem2. Semantically interpretable3. Producing better predictions

Our contribution

Page 7: PRemiSE : Personalized  News Recommendation  via  Implicit  Social Experts

Expert Model

Page 8: PRemiSE : Personalized  News Recommendation  via  Implicit  Social Experts

An illustrative example of implicit social network

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Building Implicit Social Network

Step0: Compute time span & number of visits for each itemStep1: Plot the time span , number of visits , find the abnormal items , remove itStep2: Build the graph, based on user-item accessing historyif U1 access the same item V after U2, and 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:1. find enough neighbors for each user2. precisely find the right experts

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Empirical study on a real data set

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1. How probably the given user will follow the expert’s adoption on the same item?

2. How probably any individual will follow the expert’s adoption on the same item?

Local Expert and Global Expert

find global expert?

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1. Matrix factorization2. Probabilistic matrix factorization3. PRemiSE4. Learning in PRemiSE5. Inference in PRemiSE

PRemiSE

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Matrix factorization

user u

𝑝𝑢

=

𝑞𝑖

item i

Now how to get

Gradient descent

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Probabilistic matrix factorization

Linear probability model

Page 15: PRemiSE : Personalized  News Recommendation  via  Implicit  Social Experts

PRemiSE

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Learning in PRemiSE

Page 17: PRemiSE : Personalized  News Recommendation  via  Implicit  Social Experts

See detailed in the paper

Optimization Algorithm

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Existing Item by Existing User

Existing Item by New User

New Item by Existing User

New Item by New User

Inference in PRemiSE

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Real-World Dataset 1. crawled from several popular news service websites 2. two types of elements : news stories and named entities.

Rating 1. Rating in Story: binary 2. Rating in Entity: numerical

Experimental Evaluation

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Step1: eliminate 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

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Parameters of Global Expert Model

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Comparative Study

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Cold-start problem

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Semantics of factors

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We integrate this “expert” model with the content information and collaborative filtering, and propose a hybrid recommendation framework, called PRemiSE.

1. effectively handle the cold-start problem2. better Semantics Explanation3. better performance in recommendation accuracy

FUTURE WORK : social media & information diffusion model & export model

Conclusion AND Future work

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Thank you for your time!