will you “reconsume” the near past? fast prediction on ...lastfm, brightkite, gowalla data sets....

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The Twenty-Ninth AAAI Conference on Artificial Intelligence (AAAI-15, Austin, USA). Contact: [email protected], [email protected]. Reconsume items which have recently been consumed by a same user. Alternated now and then with novel consumption behaviors. Short-Term REConsumption (STREC) Behaviors Jun Chen, Chaokun Wang, Jianmin Wang Will You “Reconsume” the Near Past? Fast Prediction on Short-term Reconsumption Behaviors School of Software, Tsinghua University, Beijing 100084, P.R. China A switch problem to narrow down the problem domains. Broad applications, e.g. intelligent marketing, recommender systems, web revisitation, information re-finding. Challenges: 1) high dynamics of short-term behaviors; 2) multiple influential factors; 3) No representative features. Motivations Feature Extraction A. Item Popularity Average normalized frequencies of items. B. Item Reconsumption Ratio Average normalized reconsumption probability of items. C. User Reconsumption Ratio Reconsumption probability of users. D. Window Reconsumption Ratio Fraction of reconsumptions in the current window. = log(1 + ()) max log(1 + ) = 1 | | = log(1 + =∧∈ , = ) = max = 1 | | = , | | =1− |( )| A B C D argmin = ( , 2 , ) 2 s.t. = argmin = ( , 2 , ) 2 + argmin = ( , , ) 2 s.t. = argmin = ( , , ) 2 + Fast Prediction Methods 1. Linear Method Pr , = , , = {ℎ ,ℎ ,ℎ ,ℎ } 2. Quadratic Method Pr , = , 2 Experiments Collected a new App using data set, ManicTime. LastFM, BrightKite, Gowalla data sets. On average, 80% prediction accuracy on 4 real-world datasets. SVM method is prone to be overfitted. The linear and the quadratic methods do not have much difference in prediction accuracy.

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Page 1: Will You “Reconsume” the Near Past? Fast Prediction on ...LastFM, BrightKite, Gowalla data sets. On average, 80% prediction accuracy on 4 real-world datasets. SVM method is prone

The Twenty-Ninth AAAI Conference on Artificial Intelligence (AAAI-15, Austin, USA).

Contact: [email protected], [email protected].

Reconsume items which have recently been consumed by a same user.

Alternated now and then with novel consumption behaviors.

Short-Term REConsumption (STREC) Behaviors

Jun Chen, Chaokun Wang, Jianmin Wang

Will You “Reconsume” the Near Past? Fast Prediction on Short-term Reconsumption Behaviors

School of Software, Tsinghua University, Beijing 100084, P.R. China

A switch problem to narrow down the problem domains.

Broad applications, e.g. intelligent marketing, recommendersystems, web revisitation, information re-finding.

Challenges: 1) high dynamics of short-term behaviors; 2)multiple influential factors; 3) No representative features.

Motivations

Feature Extraction

A. Item Popularity

Average normalized frequencies of items.

B. Item Reconsumption Ratio

Average normalized reconsumption probability of items.

C. User Reconsumption Ratio

Reconsumption probability of users.

D. Window Reconsumption Ratio

Fraction of reconsumptions in the current window.

ℎ𝐼𝑃 𝑥 =log(1 + 𝑓𝑟𝑒𝑞(𝑥))

max𝑦∈𝐗log(1 + 𝑓𝑟𝑒𝑞 𝑦 )ℎ𝐼𝑃 𝑊𝑘 =

1

|𝑊𝑘| 𝑥∈𝑊𝑘

ℎ𝐼𝑃 𝑥

ℎ𝐴𝐼𝑅𝑅 𝑥 = log(1 + 𝑢∈𝐔 𝑡∈𝑇𝑢 𝕝𝑡=𝑥∧𝑡∈𝐶𝑘

𝑢,𝑡

𝑢∈𝐔 𝑡∈𝑇𝑢 𝕝𝑡=𝑥)

ℎ𝐼𝑅𝑅 𝑥 =ℎ𝐴𝐼𝑅𝑅 𝑥

max𝑦∈𝐗ℎ𝐴𝐼𝑅𝑅 𝑦ℎ𝐼𝑅𝑅 𝑊𝑘 =

1

|𝑊𝑘| 𝑥∈𝑊𝑘

ℎ𝐼𝑅𝑅 𝑥

ℎ𝑈𝑅𝑅 𝑢 = 𝑡∈𝑇𝑢 𝕝𝑡∈𝑊𝑘

𝑢,𝑡

|𝑇𝑢|ℎ𝑊𝑅𝑅 𝑊𝑘 = 1 −

|𝐷𝑆(𝑊𝑘)|

𝑘

A

B

C D

argmin𝐰𝒬∗𝒬 𝐰 =

𝑢∈𝐔

𝑡∈𝑇𝑢

( 𝐰𝑇𝑑𝑖𝑎𝑔 𝐱𝑢,𝑡2𝐰− 𝕝

𝑡∈𝑊𝑘𝑢,𝑡)2

s.t.𝐰𝑇𝐰 = 𝟏

argmin𝐰𝒬∗𝒬 𝐰 =

𝑢∈𝐔

𝑡∈𝑇𝑢

( 𝐰𝑇𝑑𝑖𝑎𝑔 𝐱𝑢,𝑡2𝐰− 𝕝

𝑡∈𝑊𝑘𝑢,𝑡)2 + 𝜆𝐰𝑇𝐰

argmin𝐰ℒ∗ℒ 𝐰 =

𝑢∈𝐔

𝑡∈𝑇𝑢

(𝐰𝑇𝐱𝑢,𝑡 − 𝕝𝑡∈𝑊𝑘𝑢,𝑡)2

s.t. 𝒊𝐰𝒊 = 𝟏

argmin𝐰ℒ∗ℒ 𝐰 =

𝑢∈𝐔

𝑡∈𝑇𝑢

(𝐰𝑇𝐱𝑢,𝑡 − 𝕝𝑡∈𝑊𝑘𝑢,𝑡)2 + 𝜆

𝒊

𝐰𝒊

Fast Prediction Methods

1. Linear Method

Prℒ 𝑢, 𝑡 = 𝐰𝑇𝐱𝑢,𝑡

𝐱𝑢,𝑡 = {ℎ𝐼𝑃 𝑊𝑘 , ℎ𝐼𝑅𝑅 𝑊𝑘 , ℎ𝑈𝑅𝑅 𝑢 , ℎ𝑊𝑅𝑅 𝑊𝑘 }𝑇

2. Quadratic Method

Pr𝒬 𝑢, 𝑡 = 𝐰𝑇𝑑𝑖𝑎𝑔 𝐱𝑢,𝑡

2𝐰

Experiments

Collected a new App using data set, ManicTime.

LastFM, BrightKite, Gowalla data sets.

On average, 80% prediction accuracy on 4 real-world datasets.

SVM method is prone to be overfitted.

The linear and the quadratic methods do not have much difference inprediction accuracy.