Hybrid web recommender systems
Sponsored Links
This presentation is the property of its rightful owner.
1 / 44

Hybrid Web Recommender Systems PowerPoint PPT Presentation


  • 90 Views
  • Uploaded on
  • Presentation posted in: General

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 .

Download Presentation

Hybrid Web Recommender Systems

An Image/Link below is provided (as is) to download presentation

Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author.While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server.


- - - - - - - - - - - - - - - - - - - - - - - - - - E N D - - - - - - - - - - - - - - - - - - - - - - - - - -

Presentation Transcript


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.

  • 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


Concepts and Techniques


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

  • 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

  • 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

  • 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

  • Taxonomy by Burke (2002)

    • Weighted

    • Switching

    • Mixed

    • Feature combination

    • Feature augmentation

    • Cascade

    • Meta-level

Hybrid Web Recommender Systems


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

  • 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

  • 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

  • Candidate generation

  • Multiple ranked lists

  • Combined display

Hybrid Web Recommender Systems


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 decision

  • Candidate generation

  • Scoring

  • No role for unchosen recommender

  • Hybrid Web Recommender Systems


    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

       In training stage

    • Candidate generation

    • Scoring

    Hybrid Web Recommender Systems


    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

    Hybrid Web Recommender Systems


    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

    • Procedure

      • Primary recommender

      • Ranks

      • Break ties by secondary recommender

    Hybrid Web Recommender Systems


    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

    • Procedure

      • Contributing recommender

         Learned model

      • Knowledge Source Replacement

      • Actual Recommender

    Hybrid Web Recommender Systems


    Experiments


    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 – 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

    • 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

    • 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

    • 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 Evaluations

    Hybrid Web Recommender Systems


    Hybrid Comparative Study

    • Missing components

      • Mixed hybrid

        • Not possible with retrospective data

      • Demographic recommender

        • No demographic data

    Hybrid Web Recommender Systems


    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

    • KB hybrids – best switching hybrids

    Hybrid Web Recommender Systems


    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

    • Best performance so far

      • Particularly CN*/CF*

    • Good for multi-session profiles

    Hybrid Web Recommender Systems


    Results – Cascade

    • CFP/KB, CFP/CN

      • Great improvement

      • Also good for multi-profile sessions

    Hybrid Web Recommender Systems


    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

    • 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)

    • 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

    • 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 & Related Issues


    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

    • 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)

    • Dynamic query

      • Merges preference & recommendation interfaces

      • Immediate feedback

      • Discover why a given set of ranking recommendations were made

    Hybrid Web Recommender Systems


    Questions & Comments


  • Login