research updates

He Xiangnan (PhD student)

11/2/2012

Research Updates

Research Topic General topic: Leveraging UGC in Web2.0 to improve some

IR related tasks

Current task: Leveraging user comments to enable popularity-aware rank

of items in Web2.0

Popularity-aware rank

Based on the current states of items, ranking items to reflect their popularity in the future.

Motivation of popularity-aware rank: Unequally distribution of popularity Huge temporal dynamics of popularity

A rank of items that can forecast their future popularity will improve the user experience, especially for some temporal-related queries. Examples..

Examples(I)

- Search “nba” to YouTube at 6/22/2012 night (NBA final games at that day morning)

- None of the top results are not about the championship of the Miami Heat

Examples(II)

- Search “nobel prize China” at 10/12/2012 to Google domain search(YouTube)

- None of the top results are about Mo Yan’s Nobel Literature Prize

Challenges

Intuitive way: Utilizing the visiting histories of items, treating them as

time-series and performing prediction Difficulties:

Visiting histories are difficult to get and maintain (expensive)

Traditional time-series prediction approaches are easy to fail in the case that items are experiencing bursts

My proposal: Leveraging the user comments

Observation in YouTube

Observation: the comment history is highly correlated with the view history

Pre-Analysis(I)

YouTube dataset (14,509 videos of ten queries). Pearson correlation of comment history and view

history:

More than 80% videos with correlation more than 0.5

Conclusion: the comment history is highly correlated with the view history!

Pre-Analysis(II)

Have shown the tight correlation of comments and views A natural question: how the past comments reflect the

future comments? Autocorrelation of a series:

Measure the correlation of a time series at different distances apart (lags)

Autocorrelation@lagK is the correlation of series

{x_1, x_n-k} and {x_k+1, x_n}

Results of Autocorrelation of Comment Series

Exhibits a short-term correlation (r_1 is large and r_k decreases very fast)

Conclusion: the recent comments reflect most of the future and the predicting ability decreases with time.

Intuitions

More comments an item has, more popular it is. Each comment has a contribution to the item’s

popularity(or importance)

Different user’s commenting behavior has different influence on the item’s popularity. Social interfaces in Web2.0 systems. More active the user is, more influence it is. More popular the commented item is, more influence the

user is.

Method Overview

User-Item Temporal Bipartite Graph Model

The edge weight (decay function with time):

The weight matrix of the graph:

Random Walk Process(I)

Transition matrix:

The nature iterative process (HITS):

Problem: if the graph is sparse and disconnected, it will be trapped into local optima.

Random Walk Process(II)

Add the smoothing to avoid the local optima case:

The process in the bipartite graph can be converted into a random walk in homogeneous-node graph and it will converge (Proof ignored.)

Experiments

Crawled 3 datasets(20k size) to give a comprehensive evaluation of the performance in general Web2.0 systems.

Have not done the whole experiments yet, show an experimental result on Last.fm

#Item #User #Comment

YouTube 21653 6090686 12864491

Flickr 26817 317639 2499102

Last.fm 16284 77996 530237

Preparation

2 time points: 2012.10.19 (t0) 2012.10.22 (t1) Goundtruth is the #views in (t1-t0)

Comparing methods: Comm_Oracle: #comment in the future days(t1-t0). VC: View Count in the day t0 CCP: Comment Count in the Past 3 days of t0 Sum_Tscore: the sum of all comments’ contribution(all

users have the same weights) TPR: my approach

Overall Performance

Split by different popularity

Preparation: Sort all items by the #view. (Large -> Small) Split the 17000 items into 5 folds, each with the same size Evaluate each fold. Report the average performance of all folds

Average Performance of Splitted Folds

Observation:For the 1st fold, VC is the best; for the 2-5 folds, TPR is the best.Possible Reason: for the Last.fm dataset, the past extreme popular artists still attract many visits without attracting many new comments, such as The Beatles, Muse.

To do...

Finish the experiments in the other datasets.

Refinement of the approach for different types of data. such as: For extreme popular but old items, using personalized

vector to have a bias.

Questions && Suggestion?

Thanks!