Tag And Tag Based Recommender

Tag & Tag-based Recommenders

IBM Research – China

Presenter: Xiatian Zhang (张夏天)

Team:

赵石顽 张夏天 袁 泉

About Me

2000-2004, B.S. Math, Central South University

2004-2007, M.S. Computer Science, BUPT

2007-Present, Researcher, Working on Recommender Systems and Data Mining

Agenda

Social Tagging System and Its Features

Tag Recommender

Tag-based Recommender

Social Tagging

A folksonomy is a system of classification derived from the practice and method of collaboratively creating and managing tags to annotate and categorize content; this practice is also known as collaborative tagging, social classification, social indexing, and social tagging. Folksonomy is a portmaneau of folk and taxonomy.

Social Tagging boomed from 2004, with the wave of Web 2.0.– Delicious

– Citeulike

– Bibsonomy

– Youtube

– Flickr

– Dogear – A internal social book marking system in IBM

– …

Some Insights of Tagging System

Shilad Sen et.al., tagging, communities, vocabulary, evolution, CSCW’06

– Modeling vocabulary evolution

– Tagging system features

– Based on Movielens recommender system

– Personal tendency and community influence

– Tag displaying strategies and their effects

– Tag utility

Modeling vocabulary evolution

Tagging System Features

Design Features

– Tag Sharing

– Tag Selection

– Item Ownership

– Tag Scope– Broad– Narrow

Tag Class

– Factual Tag

– Subjective Tag

– Personal Tag

Tagging System in Movielens

Personal Tendency

How strongly do investment and habit affect personal tagging behavior?

– 1. Habit and investment influence user’s tag applications.

– 2. Habit and investment influence grows stronger as users apply more tags.

– 3. Habit and investment cannot be the only factors thatcontribute to vocabulary evolution.

Community Influence

How does the tagging community influence personal vocabulary?

– 1. Community influence affects a user’s personal vocabulary.

– 2. Community influence on a user’s first tag is stronger for users who have seen more tags.

Tag Displaying Strategies Effects

Tag Utility

Tag Recommender

Purpose

– Encourage users to tag more frequently, apply more tags to an individual resource, reuse common tags

– Make user use tags not previously considered.

– Eliminate Redundant tags– Promote a core tag vocabulary steering the user toward adopting

certain tags while not imposing any strict rules. – Avoid ambiguous tags in favor of tags that offer greater information

value.

Tag Recommender – Technologies

Naive Methods

– Most Popular Tags on Resources– Most Popular Tags on Users– Most Popular Tags on Resources and Users

Classical Collaborative Filtering

– User-KNN– Item-KNN

Adapted KNN Methods

– Extend User-Item Matrix– Degrade User-Item-Tag Relationship

Content-based Method

Tensor Method

– Tensor Factorization

Graph Based

– FolkRank

Our Work

Adapted KNN – Extend UI Matrix

Adapted KNN – Degrade User-Item-Tag relationship

Process

– TF/IDF on UI, UT, IT

– P-Core Processing– Remove noise data

– Extract User Model by Hebbian Deflation

Tensor Factorization

FolkRank

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)(/)1()(

ij pMp j

ji pL

pPRdNdpPR

PageRank

Personalized PageRank

FolkRank

1. Compute global PageRank by (1)2. Then for each <user, item> pair, compute personalized PageRank by (2)

– p[i] = 1, but p [u] = 1 + |U| and p [r] = 1 + |R|.3. FolkRank = Personalized PageRank - PageRank

)( )(

)()1()(

ij pMp j

jii pL

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(1)

(2)

Our Work

Explored and Exploring Methods

– Non-classical Tensor Fusion Factorization

– Multi-label Classification by Random Decision Trees, High Speed

– The performance of both two methods are close to FolkRank

Current Progress

– Shiwan develop a simple graph model

– Best precision and recall on several datasets compared to other methods

– We are writing paper targeting ACM RecSys 2010

Tag-based Recommender

Our Work

– IUI 2008 Paper, Improved Recommendation based on Collaborative Tagging Behaviors

– Explored Methods– Tensor Factorization– Non-classical Tensor and Matrix Fusion Factorization

Other Works

– Shilad Sen, Jesse Vig, and John Riedl, Tagommenders: Connecting Users to Items through Tags, WWW 2009

IUI 2008 Paper Overview

We invent a new collaborative filtering approach TBCF (Tag-based Collaborative Filtering) based on the semantic distance among tags assigned by different users to improve the effectiveness of neighbor selection.

That is, two users could be considered similar not only if they rated the items similarly, but also if they have similar cognitions over these items.

Example– Both Bob and Tom may rate the movie Avatar with 5 stars, which indicates they

all like this movie very much. – Nevertheless, as a 3D fan, Bob appreciates this movie for its high quality 3D

animations, while Tom may think that it is a wonderful action movie.

Tag-based Collaborative Filtering

1. Calculate the semantic similarity of tags based on WordNet (for the tags not included in WordNet, calculate the edit-distance instead)

2. Calculate the similarity between tag sets3. Calculate the similarity between user u and v by summing up the similarity of tag

sets on common pages (tagged by both u & v)4. Find the top-N nearest neighbors of the active user to make the prediction5. Return the top-M predicted items to the active user

Tag-based User-Item Matrix

Item1 Item2 Item3 Item4

Alice Art, photo Home, Products Writing, Design Learning, Education

Daniel Photo, Album, Image

Ø Typewriter Tutorial, Training

Sherry Ø Cleaning Ø Language, Study

Maggie Photography Ø Ovens Ø

Steps

Tag Similarity Calculation

Tag similarity– WordNet

– LSA/PLSA

Tag set similarity– Hungarian method

WordNet Concept Tree

If x and y are contained in WordNet, dis(x,y) is the shortest path length between x and y.

Word similarity in WordNet

Experimental Evaluation

Random generated subset Average Precision TBCF

Average Precisioncosine

500 0.208 0.121

2000 0.182 0.118

4000 0.202 0.173

6000 0.209 0.180

Algorithm Average Precision Average Ranking

TBCF 0.27 2.8

cosine 0.13 1.5

Data SetExtract total 8000 users, 5315 pages and 7670 tags from web logs.

Tagommenders: Connecting Users to Items through Tags

Q & A