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Hybrid Web Recommender Systems. Robin Burke Presentation by Jae-wook Ahn 10/04/05. References. Entrée system & dataset Burke, R. (2002). Semantic ratings and heuristic similarity for collaborative filtering. AAAI Workshop on Knowledge-based Electronic Markets 2000 .

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hybrid web recommender systems

Hybrid Web Recommender Systems

Robin Burke

Presentation by Jae-wook Ahn

10/04/05

references
References
  • Entrée system & dataset
    • Burke, R. (2002). Semantic ratings and heuristic similarity for collaborative filtering. AAAI Workshop on Knowledge-based Electronic Markets 2000.
  • Feature augmentation, mixed hybrid example
    • Torres, R., McNee, S., Abel, M., Konstan J., & Riedl J. (2004). Enhancing Digital Libraries with TechLens+. Proceedings of the 2004 Joint ACM/IEEE Conference on Digital Libraries.
  • Hybrid recommender system UI issue
    • Schafer, J. (2005). DynamicLens: A Dynamic User-Interface for a Meta-Recommendation System. Workshop: Beyond Personalization 2005, IUI’05.
  • Collaborative filtering algorithm
    • Sarwar, B., Karypis, G., Konstan, J., & Riedl, J. (2001). Item-based collaborative filtering recommendation algorithms. In Proceedings of the 10th international conference on World Wide Web.

Hybrid Web Recommender Systems

hybrid recommender systems
Hybrid Recommender Systems
  • Mix of recommender systems
  • Recommender system classification – knowledge source
    • Collaborative (CF)
      • User’s ratings “only”
    • Content-based (CN)
      • Product features, user’s ratings
      • Classifications of user’s likes/dislikes
    • Demographic
      • User’s ratings, user’s demographics
    • Knowledge-based (KB)
      • Domain knowledge, product features, user’s need/query
      • Inferences about a use’s needs and preferences

Hybrid Web Recommender Systems

cf vs cn
CF vs. CN
  • User-based CF
    • Searches for similar users in user-item “rating” matrix
  • Item-based CF
    • Searches for similar items in user-item “rating” matrix
  • CN
    • Searches for similar items in item-feature matrix
    • Example – TF*IDF term weight vector for news recommendation

Items

Ratings

Users

Hybrid Web Recommender Systems

recommender system problems
Recommender System Problems
  • Cold-start problem
    • Learning based techniques
    • Collaborative, content-based, demographic

 Hybrid techniques

  • Stability vs. plasticity problem
    • Difficulty to change established user’s profile
    • Temporal discount – older rating with less influence
  • KB – fewer cold start problem (no need of historical data)
  • CF/Demographic – cross-genre niches, jump outside of the familiar (novelty, serendipity)

Hybrid Web Recommender Systems

strategies for hybrid recommendation
Strategies for Hybrid Recommendation
  • Combination of multiple recommendation techniques together for producing output
  • Different techniques of different types
    • Most common implementations
    • Most promise to resolve cold-start problem
  • Different techniques of the same type
    • Ex) NewsDude – naïve Bayes + kNN

Hybrid Web Recommender Systems

seven types of recommender systems
Seven Types of Recommender Systems
  • Taxonomy by Burke (2002)
    • Weighted
    • Switching
    • Mixed
    • Feature combination
    • Feature augmentation
    • Cascade
    • Meta-level

Hybrid Web Recommender Systems

weighted hybrid
Weighted Hybrid
  • Concept
    • Each component of the hybrid scores a given item and the scores are combined using a linear formula
    • When recommenders have consistent relative accuracy across the product space
    • Uniform performance among recommenders (otherwise  other hybrids)

Hybrid Web Recommender Systems

weighted hybrid procedure
Weighted Hybrid Procedure
  • Training
  • Joint rating
    • Intersection – candidates shared between the candidates
    • Union – case with no possible rating  neutral score (neither liked nor disliked)
  • Linear combination

Hybrid Web Recommender Systems

mixed hybrid
Mixed Hybrid
  • Concepts
    • Presentation of different components side-by-side in a combined list
    • If lists are to be combined, how are rankings to be integrated?
      • Merging based on predicted rating or on recommender confidence
    • Not fit with retrospective data
      • Cannot use actual ratings to test if right items ranked highly
  • Example
    • CF_rank(3) + CN_rank(2)  Mixed_rank(5)

Hybrid Web Recommender Systems

mixed hybrid procedure
Mixed Hybrid Procedure
  • Candidate generation
  • Multiple ranked lists
  • Combined display

Hybrid Web Recommender Systems

switching hybrid
Switching Hybrid
  • Concepts
    • Selects a single recommender among components based on recommendation situation
    • Different profile different recommendation
    • Components with different performance for some types of users
    • Existence of criterion for switching decision
      • Ex) confidence value, external criteria

Hybrid Web Recommender Systems

switching hybrid procedure
Switching Hybrid Procedure
    • Switching decision
    • Candidate generation
    • Scoring
  • No role for unchosen recommender

Hybrid Web Recommender Systems

feature combination hybrid
Feature Combination Hybrid
  • Concepts
    • Inject features of one source into a different source for processing different data
    • Features of “contributing recommender” are used as a part of the “actual recommender”
    • Adding new features into the mix
    • Not combining components, just combining knowledge source

Hybrid Web Recommender Systems

feature combination hybrid procedure
Feature Combination Hybrid Procedure
  • Feature combination

 In training stage

  • Candidate generation
  • Scoring

Hybrid Web Recommender Systems

feature augmentation hybrid
Feature Augmentation Hybrid
  • Concepts
    • Similar to Feature Combination
    • Generates new features for each item by contributing domain
    • Augmentation/combination – done offline
  • Comparison with Feature Combination
    • Not raw features (FC), but the result of computation from contribution (FA)
    • More flexible to apply
    • Adds smaller dimension

Hybrid Web Recommender Systems

feature augmentation hybrid procedure
Feature Augmentation Hybrid Procedure

Hybrid Web Recommender Systems

cascade hybrid
Cascade Hybrid
  • Concepts
    • Tie breaker
    • Secondary recommender
      • Just tie breaker
      • Do refinements
    • Primary recommender
      • Integer-valued scores – higher probability for ties
      • Real-valued scores – low probability for ties
      • Precision reduction
        • Score: 0.8348694  0.83

Hybrid Web Recommender Systems

cascade hybrid procedure
Cascade Hybrid Procedure
  • Procedure
    • Primary recommender
    • Ranks
    • Break ties by secondary recommender

Hybrid Web Recommender Systems

meta level hybrid
Meta-level Hybrid
  • Concepts
    • A model learned by contributing recommender

 input for actual recommender

    • Contributing recommender completely replaces the original knowledge source with a learned model
    • Not all recommenders can produce the intermediary model

Hybrid Web Recommender Systems

meta level hybrid procedure
Meta-level Hybrid Procedure
  • Procedure
    • Contributing recommender

 Learned model

    • Knowledge Source Replacement
    • Actual Recommender

Hybrid Web Recommender Systems

testbed entr e restaurant recommender
Testbed – Entrée Restaurant Recommender
  • Entrée System
    • Case-based reasoning
    • Interactive critiquing dialog
      • Ex) Entry Candidates  “Cheaper”  Candidates  “Nicer”  Candidates  Exit
    • Not “narrowing” the search by adding constrains, but changing the focus in the feature space

Hybrid Web Recommender Systems

testbed entr e restaurant recommender cont d
Testbed – Entrée Restaurant Recommender (cont’d)
  • Entrée Dataset
    • Rating
      • Entry, ending point – “positive” rating
      • Critiques – “negative” rating
      • Mostly negative ratings
      • Validity test for positive ending point assumption – strong correlation between original vs. modified (entry points with positive ratings)
    • Small in size

Hybrid Web Recommender Systems

evaluation methodology
Evaluation Methodology
  • Measures
    • ARC (Average Rank of the Correct recommendations)
    • Accuracy of retrieval
      • At different size retrieval set
      • Fraction of the candidate set (0 ~ 1.0)
  • Training & Test set
    • 5 fold cross validation – random partition of training/test set
    • “Leave one out” methodology – randomly remove one item and check whether the system can recommend it
  • Sessions Sizes
    • Single visit profiles – 5S, 10S, 15S
    • Multiple visit profiles – 10M, 20M, 30M

Hybrid Web Recommender Systems

baseline algorithms
Baseline Algorithms
  • Collaborative Pearson (CFP)
    • Pearson’s correlation coefficient for similarity
  • Collaborative Heuristic (CFH)
    • Heuristics for calculating distances between critiques
      • “nicer” and “cheaper”  dissimilar
      • “nicer” & “quieter”  similar
  • Content-based (CN)
    • Naïve Bayes algorithm – compute probability that a item is “liked” / “disliked”
    • Too few “liked” items  modified candidate generation
      • Retrieve items with common features with the “liked” vector of the naïve Bayes profile
  • Knowledge-based (KB)
    • Knowledge-based comparison metrics of Entrée
    • Nationality, price, atmosphere, etc.

Hybrid Web Recommender Systems

baseline evaluations
Baseline Evaluations
  • Techniques vary in performance on the Entrée data
    • Content-based(CN) – weak
    • Knowledge-based (KB) – better on single-session than multi-session
    • Heuristic collaborative (CFH) – better than correlation-based (CFP) for short profiles
  • Room for improvement
    • Multi-session profiles

Hybrid Web Recommender Systems

baseline evaluations1
Baseline Evaluations

Hybrid Web Recommender Systems

hybrid comparative study
Hybrid Comparative Study
  • Missing components
    • Mixed hybrid
      • Not possible with retrospective data
    • Demographic recommender
      • No demographic data

Hybrid Web Recommender Systems

results weighted
Results – Weighted
  • Hybrid performance better in only 10 of 30
  • CN/CFP – consistent synergy (5 of 6)
  • Lacks uniform performance
    • KB, CFH
    • Linear weighting scheme assumption – fault

Hybrid Web Recommender Systems

results switching
Results – Switching
  • KB hybrids – best switching hybrids

Hybrid Web Recommender Systems

results feature combination
Results – Feature Combination
  • CN/CFH, CN/CFP
    • Contributing CN
    • Identical to CFH, CFP
  • CFH maintains accuracy with reduced dataset
  • CF/CN Winnow – modest improvement

Hybrid Web Recommender Systems

results feature augmentation
Results – Feature Augmentation
  • Best performance so far
    • Particularly CN*/CF*
  • Good for multi-session profiles

Hybrid Web Recommender Systems

results cascade
Results – Cascade
  • CFP/KB, CFP/CN
    • Great improvement
    • Also good for multi-profile sessions

Hybrid Web Recommender Systems

results meta level hybrids
Results – Meta-level Hybrids
  • CN/CF, CN/KB, CF/KB, CF/CN
  • Not effective
    • No synergy
  • Weakness of KB/CN in Entrée dataset
  • Both components should be strong

Hybrid Web Recommender Systems

discussion
Discussion
  • Dominance of the hybrids over basic recommenders
  • Synergy was found under
    • Smaller profile size
    • Sparse recommendation density
    •  hybridization conquers cold start problem

Hybrid Web Recommender Systems

discussion cont d
Discussion (cont’d)
  • Best hybrids
    • Feature augmentation, cascade
    • FA allows a contributing recommender to make a positive impact
      • without interfering with the performance of the better algorithm

Hybrid Web Recommender Systems

conclusions
Conclusions
  • Knowledge-based recommendation is not limited
    • Numerously combined to build hybrids
    • Good for secondary or contributing components
  • Cascade hybrids are effective
    • Though rare in literatures
    • Effective for combining recommender with different strengths
  • Different performance characteristics
    • Six hybridization techniques
    • Relative accuracy & consistency of hybrid components

Hybrid Web Recommender Systems

system example techlens
System Example – TechLens+
  • Hybrid recommender system
    • Recommenders – CF, CN
    • Hybrid algorithms – CF/CN FA, CN/CF FA, Fusion (Mixed)
  • Corpus
    • CiteSeer
    • Title, abstract (CN), citations (CF)
  • Methodology
    • Offline experiment, Online user study with questionnaire (by asking satisfaction on the recommendation)
  • Results
    • Fusion was the best
    • Some FA were not good due the their sequential natures
    • Different algorithms should be used for recommending different papers
    • Users with different levels of experiences perceive recommendations differently

Hybrid Web Recommender Systems

meta recommender dynamiclens
Meta-recommender – DynamicLens
  • Can user provided information improve hybrid recommender system output?
  • Meta-recommender
    • Provide users with personalized control over the generation of a recommendation list from hybrid recommender system
  • MetaLens
    • IF (Information Filtering), CF

Hybrid Web Recommender Systems

meta recommender dynamiclens cont d
Meta-recommender – DynamicLens (cont’d)
  • Dynamic query
    • Merges preference & recommendation interfaces
    • Immediate feedback
    • Discover why a given set of ranking recommendations were made

Hybrid Web Recommender Systems