Community-Based Link Prediction/Recommendation

in the Bipartite Network of BoardGameGeek.com

Brett BogeCS 765University of Nevada, Reno

Introduction

The Problem

Related Works

Conclusion

Questions / Comments

Amazon.com Netflix IMDB

• Very important for online businesses

• Drive demand for product

• Companies have had contests with million dollar prizes to increase performance

Recommender Systems

Introduction

The Problem

Related Works

Conclusion

Questions / Comments

BoardGameGeek.com

• 55,000 Board Games

• 400,000 Users

• Profile data:• Ownership• Ratings• # of players• Price• Genre• Length

• Users & Item Profiles• Based on content (e.g.

genre, demographics, length, etc.)

ContentBased

• Users & Items similar to those in the past

• More abstract, only links matter

CollaborativeBased

Filtering Approaches

50 32

85 ?

User 1

User 2

Item1

Item2

Introduction

The Problem

Related Works

Conclusion

Questions / Comments

• Memory-based• Use entire dataset directly

• Model-based• Create a model based on data• Uses model to make

recommendations

Collaborative Filtering

J. S. Breese, et al., "Empirical analysis of predictive algorithms for collaborative filtering," 1998

• Recommendations are based on the users that have liked items similar to ones the user has liked in the past

User-based Collaborative Filtering

• Recommendations are based on the items rated/bought similarly to other items

Item-based Collaborative Filtering

• kNN based on:• Data Normalization• Neighbor selection• Interpolation weights

• Improvements to:• Data Normalization• Interpolation weights

The BellKor Algorithm

R. M. Bell and Y. Koren, "Improved neighborhood-based

collaborative filtering," 2007.

• Sparsity is an issue• Consumer-product matrix looks like:

• Instead, represent the matrix as a bipartite graph

• Significantly better results under sparse conditions

• Computationally expensive

Link-analysis approach

Z. Huang, et al., "A Link analysis approach to recommendation

under sparse data," 2004.

Link-analysis approach

Z. Huang, et al., "A Link analysis approach to recommendation

under sparse data," 2004.

• Consumer Representativeness• Product Representativeness

Link-analysis approach

Z. Huang, et al., "A Link analysis approach to recommendation

under sparse data," 2004.

Cr Pr

• CF Performs poorly for “cold-start” users• Trust-based recommenders work well if a user

is at least connected to a large component• Sparsity forces a trust-based approach to

consider weakly trusted neighbors

• Added a random walk model to allow for defining and measuring a confidence metric

• Protects agains things like faked profiles or spammed ratings

TrustWalker

M. Jamali and M. Ester, "TrustWalker: a random walk model for combining trust-based and item-

based recommendation," 2009

• Require large amount of knowledge about users and items

• Often use textural information (website recommenders)

• Explicit or implicit profile generation

• Can over specialize (some workarounds)

Content-Based Filtering

G. Adomavicius and A. Tuzhilin, "Toward the next generation of recommender systems: A survey of the

state-of-the-art and possible extensions,"

• Collaborative:• + Cross-genre niches• - New users/items• - Gray-sheep users

• Content-based• + Handles new items easily

Hybrid Filtering

R. Burke, "Hybrid recommender systems: Survey and experiments,"

• Weighted• Switched• Mixed• Feature combination• Cascade

Methods of Hybrid Filtering

R. Burke, "Hybrid recommender systems: Survey and experiments,"

Clustering Approach

Q. Li and B. M. Kim, "Clustering approach for hybrid recommender system," 2003

Introduction

The Problem

Related Works

Conclusion

Questions / Comments

• Content vs Collborative• Graph-based, use modeified

popular algorithms (e,g. PageRank)• Similarity metrics important• Hybrid models use extra

information

Recommender Systems

Introduction

The Problem

Related Works

Conclusion

Questions / Comments