A Graph-based Recommender System

1
A Graph-based Recommender System

• Zan Huang, Wingyan Chung, Thian-Huat Ong,
  Hsinchun Chen
• Artificial Intelligence Lab
• The University of Arizona
• 07/15/2002

Acknowledgement NSF DLI-2 (IIS-9817473)
2
Agenda

• Introduction
• Literature Review
• A Graph-based Recommender System
• Research Questions
• Research Testbed and Experiments
• Conclusion and Discussion
• Questions and Comments

3

• Introduction
• Recommender System From Business Application to
  Digital Libraries

4
Information Overload

• Product information
• User information
• Interaction information between users and
  products
• Challenges for both buyers and sellers

5
Recommender Systems

• Automatic recommendation generation
• Substantial research interests
• PHOAKS (1997), Syskills Webert (1997), Fab
  (1997), GroupLens (1998)
• Commercial applications
• Amazon.com, CDNOW, Drugstore, MovieFinder
• Business success (Schafer et al. 2001)
• Browser to buyer
• Cross-selling
• Customer loyalty

6
Digital Libraries

• Information overload
• Library content information
• User information
• Library usage information
• Recommender system for Digital Libraries
• Efficient knowledge dissemination
• User satisfaction

7

• Literature Review
• Recommender System System Inputs and
  Recommendation Approaches

8
Recommender System

• Recommending items to users by predicting users
  interest in an item based on various sorts of
  information including item, user information and
  interactions between users and items.
• Items - documents, web pages, books, movies,
  restaurants, etc.

9
System Inputs

• User factual data
• Demographic information
• Item factual data
• Structural attribute information
• Textual description/content information
• Transactional data
• Explicit feedback rating, comments
• Implicit feedback purchase, browsing

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Recommendation Approaches

• Content-based approach
• Based on item factual data
• Item neighborhood formation
• Machine learning methods
• Collaborative filtering approach
• Based on user factual data and transactional data
• User neighborhood formation
• Similarity functions, correlation, clustering
• Collaborative filtering association rules (Fu et
  al. 2000)

11
Recommendation Approaches (cont.)

• Hybrid approach
• Combining content-based approach and
  collaborative filtering approach
• Combining recommendation results
• (Claypool et al. 1999)
• Collaborative filtering augmented by content
  analysis
• (GroupLens, Sarwar et al. 1998, Fab, Balabanovic
  and Shoham 1997)
• Comprehensive models
• (Basu et al. 1998)

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• A Graph-based Recommender System
• Model and Recommendation Methods

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A Two-layer Graph Model

• Goal
• Comprehensive representation
• Support flexible recommendation approaches
• A two-layered graph model
• User layer users as nodes, user similarity as
  links
• Item layer items as nodes, item similarity as
  links
• Inter-layer links interaction between user and
  items

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A Two-layer Graph Model (cont.)
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Model Characteristics

• Comprehensiveness
• All three types of system inputs
• Transformation of feature data into similarity
  data
• Flexibility
• Flexible similarity calculation
• Multiple types of transactional data
• Recommendation as a graph search task
• Finding item nodes highly associated with the
  user nodes
• Support different recommendation approaches
• Different association calculation methods

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Recommendation Approaches

• Content-based approach
• Starting from item nodes associated with the
  target user, exploring the item-layer links
• Collaborative filtering approach
• Starting from the target user node, exploring the
  user-layer links and inter-layer links
• Hybrid approach
• Starting with the target user node, exploring all
  three types of links

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Recommendation Methods

• Low-degree association
• Exploring direct associations
• High-degree association
• Exploring transitive associations
• A simple example
• 1-degree association
• ltC1, B1gt 0
• 2-degree association
• ltC1,B1gt 0.50.60.3 (C1-B2-B1)
• 3-degree association
• ltC1, B1gt 0.30.210.120.280.91
• (C1-B2-B1, C1-C2-B2-B1, C1-B2-B3-B1,
• C1-C2-B3-B1)

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Recommendation Methods (cont.)

• High-degree association recommendation algorithm
• High-degree association retrieval in associative
  retrieval literature
• Hopfield Net Spreading Activation (Chen and Ng
  1995, Houston et al. 2000)

• Item and user nodes as neurons and links as
  synapses in the Hopfield Net
• Parallel relaxation search
• Stop until activation values in the network
  converge
• Item nodes with highest activation values as
  recommendations

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• Research Questions

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Recommender System Problems

• Content-based recommendation
• Over-specification
• Collaborative filtering recommendation
• Early rater problem
• Sparsity problem
• Possible solutions
• Hybrid recommendation approach
• High-degree association recommendation

21
Research Questions

• Whether hybrid recommendation approach achieves
  higher recommendation quality over content-based
  or collaborative filtering approaches?
• Whether high-degree association recommendation
  improves the recommendation quality?

22

• Research Testbed and Experiments

23
Research Testbed

• A online bookstore
• Books.com.tw
• One of the biggest online bookstores in Taiwan
• Data Set
• 2000 Customers
• 9695 Books
• 18771 Transaction Records
• Similarity with a typical digital library
  environment
• Books with description and attributes
  Electronic documents in DL
• Customer demographic information DL user
  demographic information
• Customers with purchase history DL users with
  browsing or borrowing histories

24
Implementation Details

• Book representation
• Book attributes (price, publisher,layout, etc.)
• Book content (title, keyword, introduction, etc.)
• Chinese key phrase extraction
• Mutual Information algorithm (Ong and Chen 1999)
• Similarity calculation
• Attribute based similarity
• Book content similarity
• An asymmetric algorithm based on key phrase
  vector model (Houston et al. 2000)

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Book Sales Transactions
2000.1
2000.2
2000.3
2000.4
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Experiment Procedure

• Holdout testing
• Use half of the purchases (past purchases) to
  make recommendations. See if they match the other
  half (future purchases). (Sarwar et al. 1998)
• Used 100 randomly selected customers as sample
  data.
• Measurement of recommendation quality

27
Hypotheses

• Hybrid recommendation approach achieves better
  performance than content-based recommendation
  approach
• Hybrid recommendation approach achieves better
  performance than collaborative filtering
  recommendation approach
• Exploring high-degree associations achieves
  better performance than only exploring low-degree
  associations.

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Experiment Results

• Statistical results
• Hybrid approach achieved significantly higher
  precision and recall than content-based (t-test
  p-value precision 0.0058, recall 0.0000) and
  collaborative approaches (t-test p-value
  precision 0.0016, recall 0.0002)
• No significant difference between high-degree
  association and low-degree association methods

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• Conclusion and Discussion

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Conclusion and Discussion

• A generic graph model for recommender systems
• Comprehensive data representation
• Flexible recommendation approaches
• Applicable in Digital Libraries
• A hybrid approach improved recommendation quality
• No significant improvement was observed for
  high-degree association methods

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Conclusion and Discussion (cont.)

• About low precision and recall
• The gap between interest and purchase behavior
• Online bookstore data might not fully represent
  users interests
• High-degree association method
• Poor performance might be related to the density
  of the graph

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Recent Development

• The relationship between high-degree association
  recommendation performance and graph density
• Implementation of association rule mining under
  the graph model for different recommendation
  approaches
• Implementation of other associative retrieval
  algorithms for high-degree association
  recommendation
• Associative Linear Retrieval Model
• Leaky Capacity Model Spreading Activation
• Branch-and-Bound Spreading Activation

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• For Project Information
• http//ai.bpa.arizona.edu
• zhuang_at_eller.arizona.edu
• Acknowledgement
• NSF DLI-2 9817473