Recommender Systems - PowerPoint PPT Presentation

Loading...

PPT – Recommender Systems PowerPoint presentation | free to view - id: f43b-YjdhY



Loading


The Adobe Flash plugin is needed to view this content

Get the plugin now

View by Category
About This Presentation
Title:

Recommender Systems

Description:

Recommender Systems – PowerPoint PPT presentation

Number of Views:133
Avg rating:3.0/5.0
Slides: 23
Provided by: maheshma
Category:

less

Write a Comment
User Comments (0)
Transcript and Presenter's Notes

Title: Recommender Systems


1
Recommender Systems
  • Sumir Chandra
  • The Applied Software Systems Laboratory
  • Rutgers University

2
Introduction
  • Information overload decisions???
  • Too many domains, less experience, too much data
  • - books, movies, music, websites, articles, etc.
  • System providing recommendations to users based
    on opinions/behaviors of others
  • - efficient attention, better matches,
    non-obvious connections, keep users coming back
    for more
  • E.g. E-commerce Reel.com, Levis, eBay, Excite
  • Commerce call centers, direct marketing

3
Introduction (contd.)
  • Data sources purchase data, browsing searching
    data, feedback by users, text comments, expert
    recommendations
  • Taxonomy
  • - text comments (expert/user reviews)
  • - attribute based (this author also wrote )
  • - item-to-item correlation (people who bought
    this item also bought )
  • - people-to-people correlation (users like you
    )
  • Primary transformation recommendation
    aggregation or good matching between recommender
    and seeker

4
Correlations
  • Item-to-item correlation
  • Connect users to items they may be unaware of
  • Based on keywords or features of object
  • Key statistic high/low
  • - people who bought A B / people who
    bought A
  • People-to-people correlation
  • Collaborative filtering
  • Assumes user will-
  • - prefer like-minded prefer
  • - prefer dissimilar dislike
  • Object ranking by users
  • CF majority rules, nearest neighbor, weighted
    averages (prediction, S.D., covariance) ve or -ve

5
Design Issues
  • Technical Design Space
  • Content of evaluation single bit to unstructured
    textual notations ease of use, computation
    overload
  • Explicit/Implicit evaluation nature of
    recommendation
  • User identity real names, pseudonyms, anonymous
  • Evaluation aggregation research area weighted
    voting, content analyses, referral chains, etc.
  • Evaluation usage filtering out negatives,
    sorting of items according to numeric
    evaluations, display

6
Design Issues (contd.)
7
Design Issues (contd.)
  • Domain-Space Characteristics of items evaluated
  • Domain to which items belong
  • Sheer volume variable
  • Lifetime rate of gathering and distributing
    evaluations
  • Cost structure miss a good item, sample a bad
    one, costs of incorrect decisions
  • Domain-Space Characteristics of participants and
    evaluations
  • Set of recommenders
  • Recommendation density do recommenders tend to
    evaluate many items in common
  • Set of consumers
  • Consumer taste variability taste matching
    better for larger set, personalized aggregation
    better when tastes differ

8
Design Issues (contd.)
9
Design Issues (contd.)
  • Social Implications
  • Free Riders take but not give mandatory,
    monetary incentives weighted voting to avoid
    unfair evaluation discourage vote early and
    often phenomenon
  • Privacy information vs. privacy privacy blends
    attributed credit for recommendation efforts
    blind refereeing as in peer review system
  • Advertisers charge recipients through
    subscription or pay-per-use advertiser support
    charge owners of the evaluated media

10
Recommender System Types
  • Collaborative/Social-filtering system
    aggregation of consumers preferences and
    recommendations to other users based on
    similarity in behavioral patterns
  • Content-based system supervised machine
    learning used to induce a classifier to
    discriminate between interesting and
    uninteresting items for the user
  • Knowledge-based system knowledge about users
    and products used to reason what meets the users
    requirements, using discrimination tree, decision
    support tools, case-based reasoning (CBR)

11
Content-based Collaborative Information Filtering
  • Research Assistant Agent Project (RAAP)
  • Nagoya Institute of Technology, Japan
  • Registration, research profile bookmark
    database
  • Interesting page - agent suggestion -
    classification - reconfirm or change
  • In parallel, agent checks for newly classified
    bookmarks - recommend to other users -
    accept/reject on login
  • Text categorization positive/negative examples,
    most similar classifier for candidate class using
    term weighting, with TF-IDF scheme in Information
    Retrieval

12
Content-based Collaborative Information Filtering
(contd.)
  • Relevance feedback positive/negative
    prototypes similarity measure is simt(c,D)
    (Qt.Dt) (Qt-.Dt)
  • Feature selection removal of non-informative
    terms using Information Gain (IG) using prob. of
    term present
  • Learning to recommend agent counts with 2
    matrices user vs. category matrix (for
    successful classification) and users confidence
    factor (0.1 to 1) w.r.t. other users to compute
    correlation
  • Circular reference avoided verify that
    recommended document is not registered in
    targets database

13
Knowledge-based Systems
  • FindMe technique knowledge-based similarity
    retrieval
  • User selects source item - requests similar
    items
  • Tweak application same but candidate set is
    filtered prior to sorting, leaving only
    candidates satisfying tweak
  • Car Navigator conversational interaction/navigat
    ion focused around high-level responses
  • PickAFlick multiple task-specific retrieval
    strategies
  • RentMe query menus set, NLP to generate
    database
  • Recommender Personal Shopper (RPS) a
    domain-independent implementation of FindMe
    algorithm

14
Knowledge-based Systems (contd.)
  • Similarity measures goal-based, priorities for
    goals
  • Sorting algorithm metric-based bucket sorting
  • Retrieval algorithm priority-ordered metric
    constraints, plus tweaks, forming an SQL query
  • Product data creation of product database in
    which unique items are associated with sets of
    features
  • Metrics similarity, directional metrics with
    preference
  • Hybrid system knowledge-based system with
    collaborative filtering

15
Recommender Tradeoffs
16
ARMaDA Recommender
  • No single partitioning scheme performs the best
    for all types of applications and systems
  • Optimal partitioning technique depends on input
    parameters and application runtime state
  • Partitioning behavior characterized by the tuple
    partitioner, application, computer system (PAC)
  • PAC quality characterized by 5-component metric
    communication, load imbalance, data migration,
    partitioning time, partitioning overhead
  • Octant approach characterizes application/system
    state
  • Adaptive meta-partitioner - fully dynamic PAC

17
Dynamic Characterization
18
RM-3D Switching Test
  • Richtmyer-Meshkov fingering instability in 3
    dimension
  • Application trace has 51 time-step iterations
  • RM-3D has more localized adaptation and lower
    activity dynamics
  • Depending on computer system, application RM-3D
    resides in octants I and III for most of its
    execution
  • Partitioning schemes pBD-ISP and G-MISPSP are
    suited for these octants
  • Application trace - Partitioner - Output trace
    - Simulator - metric measurements

19
RM-3D Switching Test (contd.)
20
RM-3D Switching Test (contd.)
  • Test Runs
  • CGD complete run
  • pBD-ISP complete run
  • CGDpBD-ISP_load (for improved load balance)
  • 0 12 - CGD 13 22 - pBD-ISP
  • 23 26 - CGD 27 36 - pBD-ISP
  • 37 48 - CGD 49 51 - pBD-ISP
  • CGDpBD-ISP_data (for reduced data migration)
  • 0 10 - CGD 11 28 - pBD-ISP
  • 29 34 - CGD 35 51 - pBD-ISP

21
RM-3D Switching Test (contd.)
22
Conclusions
  • YES !!! Experimental results conform to
    theoretical observations
  • Recommender systems in ARMaDA can result in
    performance optimization
  • Future work
  • - more robust rule-set and switching policies
  • - partitioner/hierarchy optimization at
    switch-points
  • - integration of recommender engine within
    ARMaDA
  • - partitioner and application characterization
    research to form policy rule base
About PowerShow.com