A Hybrid Recommender System Using Link Analysis and Genetic Tuning in the Bipartite Network of...
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Transcript of A Hybrid Recommender System Using Link Analysis and Genetic Tuning in the Bipartite Network of...
A Hybrid Recommender System Using Link Analysis and Genetic Tuning in the Bipartite Network of
BoardGameGeek.com
Brett BogeCS 765University of Nevada, Reno
Recap
Data
General Approach
Step 1: Link-analysis
Step 2: Content-based Cascade
Step 2: Genetic tuning
Recap
Data
General Approach
Step 1: Link-analysis
Step 2: Content-based Cascade
Step 2: Genetic tuning
Data (Overview)
Users
400,000 +
Games
55,000 +
Ratings
0–3000
/ea
Data (Scope)
• Starting with the top 5,000 games
• List of users == those which have rated at least one of the top 5,000 games
• Users with no ratings cannot be connected to anycomponent of the graph, and can only be evaluatedin the most general sense
Data (Retrieval)
• Data will be obtained through the BGG XML API2
• Game|Small World, id 40692http://boardgamegeek.com/xmlapi2/
thing?id=40692&ratingcomments=1
• User|Licinianhttp://boardgamegeek.com/xmlapi2/
user?name=Licinian
http://boardgamegeek.com/xmlapi2/collection?name=Licinian&own/played/trade/want/wishlist/etc
Data (Sets)
Ratings/Ownership Data
TeachingSet70%
TestingSet30%
(hopefully most recent)
Recap
Data
General Approach
Step 1: Link-analysis
Step 2: Content-based Cascade
Step 2: Genetic tuning
• User & Item profiles• Based on content specific to that object
(properties)ContentBased
• Users & Items similar to those liked/owned in the past
• More abstract, only links matterCollaborative
Based
General Approach
• Weighted• Switched• Mixed• Feature combination• Cascade
Methods of Hybrid Filtering
R. Burke, "Hybrid recommender systems: Survey and experiments,"
ApproachesGeneral Approach
Our Method
ApproachesGeneral Approach
Link-
analysis
•As described by Huang et al. in A Link analysis approach to recommendation under sparse data
•A PageRank style analysis of hubs and authorities
Content-based
•Refines the previous results
•Uses information about the items themselves to adjust ranking
•Will need tuning
Recap
Data
General Approach
Step 1: Link-analysis
Step 2: Content-based Cascade
Step 2: Genetic tuning
Overview
From Z. Huang, et al., "A Link analysis approach torecommendation under sparse data," 2004.
ApproachesLink Analysis Step
LinkAnalysis
Consumer - Product
Matrix
ConsumerRepresentativeness
Matrix
ProductRepresentativeness
Matrix
Matrix Definitions
From Z. Huang, et al., "A Link analysis approach torecommendation under sparse data," 2004.
ApproachesLink Analysis Step
ProductRepresentativeness
Matrix
ConsumerRepresentativeness
Matrix
Initialization
From Z. Huang, et al., "A Link analysis approach torecommendation under sparse data," 2004.
ApproachesLink Analysis Step
ConsumerRepresentativeness
Matrix
ProductRepresentativeness
Matrix
Update Phase
From Z. Huang, et al., "A Link analysis approach torecommendation under sparse data," 2004.
ApproachesLink Analysis Step
Update Phase
ConsumerRepresentativeness
Matrix
ProductRepresentativeness
Matrix
Recap
Data
General Approach
Step 1: Link-analysis
Step 2: Content-based Cascade
Step 2: Genetic tuning
Product Representativeness Result
ApproachesContent-based Cascade
ProductRepresentativeness
Matrix
Game1
Game2
Game3
UserA
x x x
UserB
PR21 PR22 PR23
UserC
x x x
PRi
Additional Data
ApproachesContent-based Cascade
Property Description
Subdomain (S) General type of game (Strategy,Family, Party)
Category (C) Genre/specific type of game(Civilization, Territory Building)
Playing Time (P) Publisher provided, in minutes
Mechanic (M) Game mechanics used (Dice Rolling,Variable Powers)
Suggested best Number of players (N)
User voted best number of players toplay the game
Similarity Measures
ApproachesContent-based Cascade
Property Similarity
Subdomain (S) Cosine
Category (C) Cosine
Playing Time (P) Error
Mechanic (M) Cosine
Suggested best Number of players (N)
Error
These will need to be normalized on the same scale (0.00 - 1.00)
Product Similarity Matrix
ApproachesContent-based Cascade
S C P M N
Game 1 .12 .2 .6 .1 .5
…
Refining the Product Ranking
ApproachesContent-based Cascade
• Create PRfinal by refining PR:
• W is a vector of weights which determine how much a givenproperty should effect the original score
Recap
Data
General Approach
Step 1: Link-analysis
Step 2: Content-based Cascade
Step 2: Genetic tuning
Determining an Optimal W
ApproachesGenetic Tuning
• W needs to be defined optimally for this given domain
• A genetic algorithm will be used to tune W
• Chromosome = sequential binary representation of W
• Fitness based on Rank Score (from Huang et al.)
• 8 bits per weight, ranging from 0 - .25 to start
• Rates of crossover/mutation TBD
Conclusion / Questions