Sales Prediction and Product Recommendation Model Through ...
Community-Based Link Prediction/Recommendation in the Bipartite Network of BoardGameGeek.com
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Transcript of Community-Based Link Prediction/Recommendation in the Bipartite Network of BoardGameGeek.com
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Community-Based Link Prediction/Recommendation
in the Bipartite Network of BoardGameGeek.com
Brett BogeCS 765University of Nevada, Reno
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Introduction
The Problem
Related Works
Conclusion
Questions / Comments
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Amazon.com Netflix IMDB
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• Very important for online businesses
• Drive demand for product
• Companies have had contests with million dollar prizes to increase performance
Recommender Systems
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Introduction
The Problem
Related Works
Conclusion
Questions / Comments
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BoardGameGeek.com
• 55,000 Board Games
• 400,000 Users
• Profile data:• Ownership• Ratings• # of players• Price• Genre• Length
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• 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
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50 32
85 ?
User 1
User 2
Item1
Item2
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Introduction
The Problem
Related Works
Conclusion
Questions / Comments
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• 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
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• 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
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• 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.
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• 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.
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Link-analysis approach
Z. Huang, et al., "A Link analysis approach to recommendation
under sparse data," 2004.
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• Consumer Representativeness• Product Representativeness
Link-analysis approach
Z. Huang, et al., "A Link analysis approach to recommendation
under sparse data," 2004.
Cr Pr
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• 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
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• 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,"
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• 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,"
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• Weighted• Switched• Mixed• Feature combination• Cascade
Methods of Hybrid Filtering
R. Burke, "Hybrid recommender systems: Survey and experiments,"
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Clustering Approach
Q. Li and B. M. Kim, "Clustering approach for hybrid recommender system," 2003
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Introduction
The Problem
Related Works
Conclusion
Questions / Comments
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• Content vs Collborative• Graph-based, use modeified
popular algorithms (e,g. PageRank)• Similarity metrics important• Hybrid models use extra
information
Recommender Systems
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Introduction
The Problem
Related Works
Conclusion
Questions / Comments