Hybrid web recommender systems
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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


Concepts and techniques

Concepts and Techniques


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


    Experiments

    Experiments


    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 related issues

    System Example & Related Issues


    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


    Questions comments

    Questions & Comments


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