Web%20Personalization%20and%20Recommender%20Systems

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Web Personalization andRecommender Systems
Bamshad Mobasher School of Computing, DePaul
University
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What is Web Personalization

• Web Personalization personalizing the browsing
  experience of a user by dynamically tailoring the
  look, feel, and content of a Web site to the
  users needs and interests.
• Related Phrases
• mass customization, one-to-one marketing, site
  customization, target marketing
• Why Personalize?
• broaden and deepen customer relationships
• provide continuous relationship marketing to
  build customer loyalty
• help automate the process of proactively market
  products to customers
• lights-out marketing
• cross-sell/up-sell products
• provide the ability to measure customer behavior
  and track how well customers are responding to
  marketing efforts

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Personalization v. Customization

• Its a question of who controls the users
  browsing experience
• Customization
• user controls and customizes the site or the
  product based on his/her preferences
• usually manual, but sometimes semi-automatic
  based on a given user profile
• Personalization
• done automatically based on the users actions,
  the users profile, and (possibly) the profiles
  of others with similar profiles

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Challenges and Pitfalls

• Technical Challenges
• data collection and data preprocessing
• discovering actionable knowledge from the data
• which personalization algorithms
• Implementation/Deployment Challenges
• what to personalize
• when to personalize
• degree of personalization or customization
• how to target information without being intrusive

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Web Personalization Recommender Systems

• Dynamically serve customized content (pages,
  products, recommendations, etc.) to users based
  on their profiles, preferences, or expected
  interests
• Most common type of personalization Recommender
  systems

User profile
Recommendationalgorithm
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Common Recommendation Techniques

• Collaborative Filtering
• Give recommendations to a user based on
  preferences of similar users
• Preferences on items may be explicit or implicit
• Content-Based Filtering
• Give recommendations to a user based on items
  with similar content in the users profile
• Rule-Based (Knowledge-Based) Filtering
• Provide recommendations to users based on
  predefined (or learned) rules
• age(x, 25-35) and income(x, 70-100K) and
  childred(x, gt3) ? recommend(x, Minivan)

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The Recommendation Task

• Basic formulation as a prediction problem
• Typically, the profile Pu contains preference
  scores by u on some other items, i1, , ik
  different from it
• preference scores on i1, , ik may have been
  obtained explicitly (e.g., movie ratings) or
  implicitly (e.g., time spent on a product page or
  a news article)

Given a profile Pu for a user u, and a target
item it, predict the preference score of user u
on item it
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Notes on User Profiling

• Utilizing user profiles for personalization
  assumes
• 1) past behavior is a useful predictor of the
  future behavior
• 2) wide variety of behaviors amongst users
• Basic task in user profiling Preference
  elicitation
• May be based on explicit judgments from users
  (e.g. ratings)
• May be based on implicit measures of user
  interest
• Automatic user profiling
• Use machine learning or data mining techniques to
  learn models user behavior, preferences
• May build a model for each specific user or build
  group profiles
• Usually based on passive observation of user
  behavior
• Advantages
• less work for user and application writer
• adaptive behavior
• user and system build trust relationship gradually

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Consequences of passiveness

• Weak heuristics
• example click through multiple uninteresting
  pages en route to interestingness
• example user browses to uninteresting page, then
  goes for a coffee
• example hierarchies tend to get more hits near
  root
• Cold start
• No ability to fine tune profile or express
  interest without visiting appropriate pages
• Some possible alternative/extensions to
  internally maintained profiles
• expose to the user (e.g. fine tune profile) ?
• expose to other users/agents (e.g. collaborative
  filtering)?
• expose to web server (e.g. cnn.com custom news)?

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Content-Based Filtering Systems

• Track which pages/items the user visits and give
  as recommendations other pages with similar
  content
• Often involves the use of client-side learning
  interface agents
• May require the user to enter a profile or to
  rate pages/objects as interesting or
  uninteresting
• Advantages
• useful for large information-based sites (e.g.,
  portals) or for domains where items have
  content-rich features
• can be easily integrated with content servers
• Disadvantages
• may miss important pragmatic relationships among
  items (based on usage)
• not effective in small-specific sites or sites
  which are not content-oriented

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Content-Based Recommenders

• Predictions for unseen (target) items are
  computed based on their similarity (in terms of
  content) to items in the user profile.
• E.g., user profile Pu contains
• recommend highly and
  recommend mildly

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Content-Based Recommender Systems
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Content-Based Recommenders Personalized Search

• How can the search engine determine the users
  context?

?
Query Madonna and Child
?

• Need to learn the user profile
• User is an art historian?
• User is a pop music fan?

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Content-Based Recommenders

• Music recommendations
• Play list generation

Example Pandora
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Example Recommender Systems

• Collaborative filtering recommenders
• Predictions for unseen (target) items are
  computed based the other users with similar
  interest scores on items in user us profile
• i.e. users with similar tastes (aka nearest
  neighbors)
• requires computing correlations between user u
  and other users according to interest scores or
  ratings

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Collaborative Recommender Systems
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Collaborative Recommender Systems
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Collaborative Recommender Systems
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Basic Collaborative Filtering Process
Current User Record
ltuser, item1, item2, gt
Nearest Neighbors
Neighborhood Formation
Recommendation Engine
Combination Function
Historical User Records
Recommendations
user
item
rating
Recommendation Phase
Neighborhood Formation Phase
Both of the Neighborhood formation and the
recommendation phases are real-time components
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Collaborative Filtering Measuring Similarities

• Pearson Correlation
• weight by degree of correlation between user U
  and user J
• 1 means very similar, 0 means no correlation, -1
  means dissimilar
• Works well in case of user ratings (where there
  is at least a range of 1-5)
• Not always possible (in some situations we may
  only have implicit binary values, e.g., whether a
  user did or did not select a document)
• Alternatively, a variety of distance or
  similarity measures can be used

Average rating of user J on all items.
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Collaborative Filtering Making Predictions

• When generating predictions from the nearest
  neighbors, neighbors can be weighted based on
  their distance to the target user
• To generate predictions for a target user a on an
  item i
• ra mean rating for user a
• u1, , uk are the k-nearest-neighbors to a
• ru,i rating of user u on item I
• sim(a,u) Pearson correlation between a and u
• This is a weighted average of deviations from the
  neighbors mean ratings (and closer neighbors
  count more)

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Example Collaborative System
Item1 Item 2 Item 3 Item 4 Item 5 Item 6 Correlation with Alice
Alice 5 2 3 3 ?
User 1 2 4 4 1 -1.00
User 2 2 1 3 1 2 0.33
User 3 4 2 3 2 1 .90
User 4 3 3 2 3 1 0.19
User 5 3 2 2 2 -1.00
User 6 5 3 1 3 2 0.65
User 7 5 1 5 1 -1.00
Bestmatch
Prediction ?
Using k-nearest neighbor with k 1
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Item-based Collaborative Filtering

• Find similarities among the items based on
  ratings across users
• Often measured based on a variation of Cosine
  measure
• Prediction of item I for user a is based on the
  past ratings of user a on items similar to i.
• Suppose
• Predicted rating for Karen on Indep. Day will be
  7, because she rated Star Wars 7
• That is if we only use the most similar item
• Otherwise, we can use the k-most similar items
  and again use a weighted average

sim(Star Wars, Indep. Day) gt sim(Jur. Park,
Indep. Day) gt sim(Termin., Indep. Day)
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Item-Based Collaborative Filtering
Prediction ?
Item1 Item 2 Item 3 Item 4 Item 5 Item 6
Alice 5 2 3 3 ?
User 1 2 4 4 1
User 2 2 1 3 1 2
User 3 4 2 3 2 1
User 4 3 3 2 3 1
User 5 3 2 2 2
User 6 5 3 1 3 2
User 7 5 1 5 1
Item similarity 0.76 0.79 0.60 0.71 0.75
Bestmatch
Cosine Similarity to the target item
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Collaborative Filtering Evaluation

• split users into train/test sets
• for each user a in the test set
• split as votes into observed (I) and to-predict
  (P)
• measure average absolute deviation between
  predicted and actual votes in P
• MAE mean absolute error
• average over all test users

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Other Forms of Collaborative and Social Filtering

• Social Tagging (Folksonomy)
• people add free-text tags to their content
• where people happen to use the same terms then
  their content is linked
• frequently used terms floating to the top to
  create a kind of positive feedback loop for
  popular tags.
• Examples
• Del.icio.us
• Flickr
• Last.fm

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Social Tagging

• By allowing loose coordination, tagging systems
  allow social exchange of conceptual information.
• Facilitates a similar but richer information
  exchange than collaborative filtering.
• I comment that a movie is "romantic", or "a good
  holiday movie". Everyone who overhears me has
  access to this metadata about the movie.
• The social exchange goes beyond collaborative
  filtering - facilitating transfer of more
  abstract, conceptual information about the movie.
  
• Note the preference information is transferred
  implicitly - we are more likely to tag items we
  like than don't like
• No algorithm mediating our connection between
  individuals when we navigate by tags, we are
  directly connecting with others.

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Social Tagging

• Deviating from standard mental models
• No browsing of topical, categorized navigation or
  searching for an explicit term or phrase
• Instead is use language I use to define my world
  (tagging)
• Sharing my language and contexts will create
  community
• Tagging creates community through the overlap of
  perspectives
• This leads to the creation of social networks
  which may further develop and evolve
• But, does this lead to dynamic evolution of
  complex concepts or knowledge? Collective
  intelligence?

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Folksonomies
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Hybrid Recommender Systems
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Semantically Enhanced Collaborative Filtering

• Basic Idea
• Extend item-based collaborative filtering to
  incorporate both similarity based on ratings (or
  usage) as well as semantic similarity based on
  domain knowledge
• Semantic knowledge about items
• Can be extracted automatically from the Web based
  on domain-specific reference ontologies
• Used in conjunction with user-item mappings to
  create a combined similarity measure for item
  comparisons
• Singular value decomposition used to reduce noise
  in the semantic data
• Semantic combination threshold
• Used to determine the proportion of semantic and
  rating (or usage) similarities in the combined
  measure

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Semantically Enhanced Hybrid Recommendation

• An extension of the item-based algorithm
• Use a combined similarity measure to compute item
  similarities
• where,
• SemSim is the similarity of items ip and iq based
  on semantic features (e.g., keywords, attributes,
  etc.) and
• RateSim is the similarity of items ip and iq
  based on user ratings (as in the standard
  item-based CF)
• ? is the semantic combination parameter
• ? 1 ? only user ratings no semantic similarity
• ? 0 ? only semantic features no collaborative
  similarity

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Semantically Enhanced CF

• Movie data set
• Movie ratings from the movielens data set
• Semantic info. extracted from IMDB based on the
  following ontology

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Semantically Enhanced CF

• Used 10-fold x-validation on randomly selected
  test and training data sets
• Each user in training set has at least 20 ratings
  (scale 1-5)

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Semantically Enhanced CF

• Dealing with new items and sparse data sets
• For new items, select all movies with only one
  rating as the test data
• Degrees of sparsity simulated using different
  ratios for training data

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Collaborative Filtering Problems

• Problems with standard CF
• major problem with CF is scalability
• neighborhood formation is done in real-time
• small number of users relative to items may
  result in poor performance
• data become too sparse to provide accurate
  predictions
• new item problem
• Vulnerability to attacks (will come back to this
  later)
• Problems in context of clickstream / e-commerce
  data
• explicit user ratings are not available
• features are binary (visit or a non-visit for a
  particular item) or a function of the time spent
  on a particular item
• a visit to a page is not necessarily an
  indication of interest in that item
• number of user records (and items) is far larger
  than the standard domains for CF where users are
  limited to purchasers or people who rated items
• need to rely on very short user histories

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Web Mining Approach to Personalization

• Basic Idea
• generate aggregate user models (usage profiles)
  by discovering user access patterns through Web
  usage mining (offline process)
• Clustering user transactions
• Clustering items / pageviews
• Association rule mining
• Sequential pattern discovery
• match a users active session against the
  discovered models to provide dynamic content
  (online process)
• Advantages
• no explicit user ratings or interaction with
  users
• helps preserve user privacy, by making effective
  use of anonymous data
• enhance the effectiveness and scalability of
  collaborative filtering
• more accurate and broader recommendations than
  content-only approaches

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Automatic Web Personalization
Offline Process
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Automatic Web Personalization
Online Process
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Conceptual Representation of User Transactions or
Sessions
Pageview/objects
Session/user data
Raw weights are usually based on time spent on a
page, but in practice, need to normalize and
transform.
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Real-Time Recommendation Engine

• Keep track of users navigational history through
  the site
• a fixed-size sliding window over the active
  session to capture the current users
  short-term history depth
• Match current users activity against the
  discovered profiles
• profiles either can be based on aggregate usage
  profiles, or are obtained directly from
  association rules or sequential patterns
• Dynamically generated recommendations are added
  to the returned page
• each pageview can be assigned a recommendation
  score based on
• matching score to user profiles (e.g., aggregate
  usage profiles)
• information value of the pageview based on
  domain knowledge (e.g., link distance of the
  candidate recommendation to the active session)

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Recommendations Based on Aggregate Profiles

• Matching score computed using cosine similarity
• Users active session (pageviews in the current
  window) is compared to each aggregate profile
  (both are viewed as pageview vectors)
• Weight of items in the profile vector is the
  significance weight of the item for that profile
• Weight of items in the session vector can be all
  1s, or based on some method for determining
  their significance in the current session
• Generating recommendations based on matching
  profiles
• from each matching profile recommend the items
  not already in the user session window, and not
  directly linked from the pages in the current
  session window
• the recommendation score for an item is based on
  a combination of profile matching score
  (similarity to session window) and the weight of
  the item in that profile
• additionally, we can weight items farther away
  from the current location of user higher (i.e.,
  consider them better recommendations)

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Discovery of Aggregate Profiles

• Transaction clusters as Aggregate Profiles
• Each transaction is viewed as a pageview vector
• Each cluster contains a set of transaction
  vectors with a centroid
• Each centroid acts as an aggregate profile with
  representing the weight for pageview pi in
  the profile
• Personalization involves computing similarity
  between a current users profile (or the active
  user session) and the cluster centroids

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Web Usage Mining clustering example

• Transaction Clusters
• Clustering similar user transactions and using
  centroid of each cluster as a usage profile
  (representative for a user segment)

Sample cluster centroid from dept. Web site
(cluster size 330)
Support URL Pageview Description
1.00 /courses/syllabus.asp?course450-96-303q3y2002id290 SE 450 Object-Oriented Development class syllabus
0.97 /people/facultyinfo.asp?id290 Web page of a lecturer who thought the above course
0.88 /programs/ Current Degree Descriptions 2002
0.85 /programs/courses.asp?depcode96deptmnesecourseid450 SE 450 course description in SE program
0.82 /programs/2002/gradds2002.asp M.S. in Distributed Systems program description
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Using Clusters for Personalization
Original Session/user data
Given an active session A ? B, the best matching
profile is Profile 1. This may result in a
recommendation for page F.html, since it appears
with high weight in that profile.
Result of Clustering
PROFILE 0 (Cluster Size 3) ---------------------
----------------- 1.00 C.html 1.00 D.html PROFILE
1 (Cluster Size 4) ----------------------------
---------- 1.00 B.html 1.00 F.html 0.75 A.html 0.2
5 C.html PROFILE 2 (Cluster Size
3) -------------------------------------- 1.00 A.h
tml 1.00 D.html 1.00 E.html 0.33 C.html
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Association Rules Personalization

• Approach of Fu, Budzik, Hammond, 2000
• Proposed solution to the problem of reduced
  coverage due to sparse data
• rank all discovered rules by the degree of
  intersection between the left-hand-side of rule
  and a user's active session and then generate the
  top k recommendations
• Problem requires the generation of all
  association rules, requiring a search in the full
  space of rules during the recommendation process
• Approach of Lin, Alvarez, Ruiz, 2000
• Basic Approach
• find an appropriate number of rules for each
  target user by automatically selecting the
  minimum support
• the recommendation engine generates association
  rules among both users and articles
• Problem requires online generation of relevant
  rules for each user

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Association Rules Personalization

• Approach of Mobasher, et al., 2001
• discovered frequent itemsets of are stored into
  an itemset graph (an extension of lexicographic
  tree structure of Agrawal, et al. 1999)
• each node at depth d in the graph corresponds to
  an itemset, I, of size d and is linked to
  itemsets of size d1 that contain I at level d1.
  The single root node at level 0 corresponds to
  the empty itemset.
• frequent itemsets are matched against a user's
  active session S by performing a search of the
  graph to depth S
• recommendation generation can be done in constant
  time
• does not require apriori generate association
  rules from frequent itemsets
• a recommendation r is an item at level S1
  whose recommendation score is the confidence of
  rule S gt r

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Sequential Patterns Personalization

• Sequential / Navigational Patterns as Aggregate
  Profiles
• similar to association rules, but the ordering of
  accessed items is taken into account
• Two basic approaches
• use contiguous sequential patterns (CSP) (e.g.,
  Web navigational patterns)
• use general sequential patterns (SP)
• Contiguous sequential patterns are often modeled
  as Markov chains and used for prefetching (i.e.,
  predicting the immediate next user access based
  on previously accessed pages)
• In context of recommendations, they can achieve
  high accuracy, but may be difficult to obtain
  reasonable coverage

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Sequential Patterns Personalization

• Sequential / Navigational Patterns (continued)
• representation as Markov chains often leads to
  high space complexity due to model sizes
• some approaches have focused on reducing model
  size
• selective Markov Models (Deshpande, Karypis,
  2000)
• use various pruning strategies to reduce the
  number of states (e.g., support or confidence
  pruning, error pruning)
• longest repeating subsequences (Pitkow, Pirolli,
  1999)
• similar to support pruning, used to focus only on
  significant navigational paths
• increased coverage can be achieved by using
  all-Kth-order models (i.e., using all possible
  sizes for user histories)

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Sequential Patterns Personalization(Mobasher,
et al. 2002)

• A Frequent Sequence Trie (FST), is used to store
  both the sequential and contiguous sequential
  patterns
• organized into levels from 0 to k, where k is the
  maximal size among all sequential (respectively,
  contiguous sequential) patterns
• each non-root node N at depth d contains an item
  sd and representing a frequent sequence
  lts1,s2,...,sdgt
• along with each node the support (or frequency)
  value of the corresponding pattern is stored
• for each active session window w ltw1,w2,...,wngt
  
• perform a depth-first search of the FST to level
  n
• If a match is found, then the children of the
  matching node N are used to generate candidate
  recommendations
• given a sequence S ltw1,w2,...,wn,pgt, the item p
  is added to the recommendation set if the
  confidence of S is greater than or equal to the
  confidence threshold

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Example Frequent Itemsets
Sample Transactions
Frequent itemsets (using min. support frequency
4)
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Example Sequential Patterns
Sample Transactions
CSP (min. support frequency 4)
SP (min. support frequency 4)
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Example An Itemset Graph
Frequent Itemset Graph for the Example
Given an active session window ltB,Egt, the
algorithm finds items A and C with recommendation
scores of 1 and 4/5 (corresponding to confidences
of the rules B,E gt A and B,E gt C ).
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Example Frequent Sequence Trie
Frequent Sequence Trie for the Example
Given an active session window ltA,Bgt, the
algorithm finds item E with recommendation score
of 1 (corresponding to confidences of the rules
A,B gt E .
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Quantitative Evaluation of Recommendation
Effectiveness

• Two important factors in evaluating
  recommendations
• Precision measures the ratio of correct
  recommendations to all recommendations produced
  by the system
• low precision would result in angry or frustrated
  users
• Coverage measures the ratio of correct
  recommendations to all pages/items that will be
  accessed by user
• low coverage would inhibit the ability of the
  system to give relevant recommendations at
  critical points in user navigation
• Transactions Divided into Training Evaluation
  Sets
• training set is used to build models (generation
  of aggregate profiles, neighborhood formation)
• evaluation set is used to measure precision
  coverage
• 10-Fold Cross Validation generally used in the
  experiments

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Evaluation Methodology

• Each transaction t in the evaluation set is
  divided into two parts
• ast portion of the first n items in t, used as
  the user session to generate recommendations (n
  is the maximum allowable window size)
• Evalt the remaining portion of t is used to
  evaluate the recommendations (Evalt t - n)
• R(ast, t) the recommendation set which contains
  all items whose recommendation score is greater
  than or equal to the threshold t.

• Example t A,B,C,D,E,F,G,H
• Use A,B,C,D to generate recommendations, say
  E,G,K
• Match E,G,K with E,F,G,H
• No. of matches 2
• Size of Evalt 4
• Size of recommendation set 3
• Coverage 2/4 50
• Precision 2/3 67

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Impact of Window Size

• Increasing window sizes (using larger portion of
  users history) generally leads to improvement in
  precision

This example is based on the association rule
approach
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Associations vs. Sequences

• Comparison of recommendations based on
  association rules, sequential patterns,
  contiguous sequential patterns, and standard
  k-nearest neighbor

Support threshold for Association, SP, CSP 0.04
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Problems with Web Usage Mining

• New item problem
• Patterns will not capture new items recently
  added
• Bad for dynamic Web sites
• Poor machine interpretability
• Hard to generalize and reason about patterns
• No domain knowledge used to enhance results
• E.g., Knowing a user is interested in a program,
  we could recommend the prerequisites, core or
  popular courses in this program to the user
• Poor insight into the patterns themselves
• The nature of the relationships among items or
  users in a pattern is not directly available

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Solution Integrate Semantic Knowledge with Web
Usage Mining

• Information Retrieval/Extraction Approach
• Represent semantic knowledge in pageviews as
  keyword vectors
• Keywords extracted from text or meta-data
• Text mining can be used to capture higher-level
  concepts or associations among concepts
• Cannot capture deeper relationships among objects
  based on their inherent properties or attributes
• Ontology-based approach
• Represent domain knowledge using relational model
  or ontology representation languages
• Process Web usage data with the structured domain
  knowledge
• Requires the extraction of ontology instances
  from Web pages
• Challenge performing underlying mining
  operations on structured objects (e.g., computing
  similarities or performing aggregations)

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Integration of Content Features

• Pre-Mining
• Initial transaction vector t ltweight(p1,t), ,
  weight(Pn,t)gt
• Transform into content-enhanced transaction
• t ltweight(w1,t), , weight(wk,t)gt
• Now transaction clustering can be performed based
  on content similarity among user transactions
• Post-Mining
• First perform mining operations on usage and
  content data independently
• Integrate usage and content patterns in the
  recommendation phase
• Example Content Profiles
• Perform clustering on the term-pageview matrix
• Each cluster centroid represents pages with some
  similar content
• Use both content and usage profiles to generate
  recommendations

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A.html B.html C.html D.html E.html
user1 1 0 1 0 1
user2 1 1 0 0 1
user3 0 1 1 1 0
user4 1 0 1 1 1
user5 1 1 0 0 1
user6 1 0 1 1 1
User transaction matrix UT
A.html B.html C.html D.html E.html
web 0 0 1 1 1
data 0 1 1 1 0
mining 0 1 1 1 0
business 1 1 0 0 0
intelligence 1 1 0 0 1
marketing 1 1 0 0 1
ecommerce 0 1 1 0 0
search 1 0 1 0 0
information 1 0 1 1 1
retrieval 1 0 1 1 1
Feature-Pageview Matrix FP
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Content Enhanced Transactions
User-Feature Matrix UF
Note that UF UT x FPT
web data mining business intelligence marketing ecommerce search information retrieval
user1 2 1 1 1 2 2 1 2 3 3
user2 1 1 1 2 3 3 1 1 2 2
user3 2 3 3 1 1 1 2 1 2 2
user4 3 2 2 1 2 2 1 2 4 4
user5 1 1 1 2 3 3 1 1 2 2
user6 3 2 2 1 2 2 1 2 4 4
Example users 4 and 6 are more interested in
concepts related to Web information retrieval,
while user 3 is more interested in data mining.
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Integrating Content and UsageFor Personalization
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Example Content Profiles
Examples of feature (word) clusters (Association
for Consumer Research Web site)
CLUSTER 10 ---------- ballot result vote ...
CLUSTER 0 ---------- anthropologi associ behavior
...
CLUSTER 4 ---------- consum journal market Psychol
ogi
CLUSTER 11 ---------- advisori appoint committe co
uncil ...
Cluster Centroids
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Example Usage Profiles

• Example Usage Profiles from the ACR Site

1.00 Call for Papers 0.67 ACR News Special
Topics 0.67 CFP Journal of Psychology and
Marketing I 0.67 CFP Journal of Psychology and
Marketing II 0.67 CFP Journal of Consumer
Psychology II 0.67 CFP Journal of Consumer
Psychology I
1.00 CFP Winter 2000 SCP Conference 1.00 Call
for Papers 0.36 CFP ACR 1999 Asia-Pacific
Conference 0.30 ACR 1999 Annual
Conference 0.25 ACR News Updates 0.24 Conference
Update

• Generated by clustering user transactions
  directly
• Usage profiles represent groups of users commonly
  accessing certain pages together
• Content profiles represent groups of pages with
  similar content

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Comparison of Recommendations
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Ontology-Based Usage Mining

• Approach 1 Ontology-Enhanced Transactions
• Initial transaction vector t ltweight(p1,t), ,
  weight(Pn,t)gt
• Transform into content-enhanced transaction
• t ltweight(o1,t), , weight(or,t)gt
• The structured objects o1, , or are instances on
  ontology entities extracted from pages p1, , pn
  in the transaction
• Now mining tasks can be performed based on
  ontological similarity among user transactions
• Approach 2 Ontology-Enhanced Patterns
• Discover usage patterns in the standard way
• Transform patterns by creating an aggregate
  representation of the patterns based on the
  ontology
• Requires the categorization of similar objects
  into ontology classes
• Also requires the specification of different
  aggregation/combination function for each
  attribute of each class in the ontology

71
Example Ontology for a Movie Site
An example of a Movie object instance
72
Ontology-Based Pattern Aggregation
Year
Name
Genre
Actor
Usage profile
2002
A
S 0.7 T 0.2 U 0.1
Genre-All
Movie 1
0.50 Movie1.html 0.35 Movie2.html0.15 Movie3.html
Romance
Comedy
Romantic Comedy
KidFamily
Object Extraction
B
1999
S 0.5 T0.5
Movie 2
Genre-All
Romance
Comedy
Genre-All
2000
C
S 0.6 W 0.4
Movie 3
Ontology-Based Aggregation
Comedy
Semantic Usage pattern
73
Personalization with Semantic Usage Patterns
Current User Profile
Aggregate Semantic Usage Patterns
Note that the matching between the semantic
representations of users profile and patterns
requires computation of similarities at the
ontological level (may be defined based on
domain-specific characteristics)
Match Profiles
Extended User Profile
Instantiate to Real Web Objects
Recommendations of Items
74
Profile Injection Attacks

• Consist of a number of "attack profiles"
• added to the system by providing ratings for
  various items
• engineered to bias the system's recommendations
• Two basic types
• Push attack (Shilling) designed to promote
  an item
• Nuke attack designed to demote a item
• Prior work has shown that CF recommender systems
  are highly vulnerable to such attacks
• Attack Models
• strategies for assigning ratings to items based
  on knowledge of the system, products, or users
• examples of attack models random, average,
  bandwagon, segment, love-hate

75
A Successful Push Attack
Item1 Item 2 Item 3 Item 4 Item 5 Item 6 Correlation with Alice
Alice 5 2 3 3 ?
User 1 2 4 4 1 -1.00
User 2 2 1 3 1 2 0.33
User 3 4 2 3 2 1 .90
User 4 3 3 2 3 1 0.19
User 5 3 2 2 2 -1.00
User 6 5 3 1 3 2 0.65
User 7 5 1 5 1 -1.00
Attack 1 2 3 2 5 -1.00
Attack 2 3 2 3 2 5 0.76
Attack 3 3 2 2 2 5 0.93
BestMatch
Prediction ?
user-based algorithm using k-nearest neighbor
with k 1
76
A Generic Attack Profile
IS
IF
IÆ
it
null null null
Ratings for l filler items
Ratings for k selected items
Rating for the target item
Unrated items in the attack profile

• Attack models differ based on ratings assigned to
  filler and selected items

77
Average and Random Attack Models
IF
IÆ
it
null null null rmax
Rating for the target item
Random ratings for l filler items
Unrated items in the attack profile

• Random Attack filler items are assigned random
  ratings drawn from the overall distribution of
  ratings on all items across the whole DB
• Average Attack ratings each filler item drawn
  from distribution defined by average rating for
  that item in the DB
• The percentage of filler items determines the
  amount knowledge (and effort) required by the
  attacker

78
Bandwagon Attack Model
IS
IF
IÆ
it
rmax rmax null null null rmax
Ratings for k frequently rated items
Random ratings for l filler items
Unrated items in the attack profile
Rating for the target item

• What if the system's rating distribution is
  unknown?
• Identify products that are frequently rated
  (e.g., blockbuster movies)
• Associate the pushed product with them
• Ratings for the filler items centered on overall
  system average rating (Similar to Random attack)
• frequently rated items can be guessed or obtained
  externally

79
Segment Attack Model
IF
IS
IÆ
it
rmax rmax rmin rmin null null null rmax
Ratings for k favorite items in user segment
Rating for the target item
Ratings for l filler items
Unrated items in the attack profile

• Assume attacker wants to push product to a target
  segment of users
• those with preference for similar products
• fans of Harrison Ford
• fans of horror movies
• like bandwagon but for semantically-similar items
• originally designed for attacking item-based CF
  algorithms
• maximize sim(target item, segment items)
• minimize sim(target item, non-segment items)

80
Nuke Attacks Love/Hate Attack Model
IF
IÆ
it
rmax rmax null null null rmin
Min rating for the target item
Unrated items in the attack profile
Max rating for l filler items

• A limited-knowledge attack in its simplest form
• Target item given the minimum rating value
• All other ratings in the filler item set are
  given the maximum rating value
• Note
• Variations of this (an the other models) can also
  be used as a push or nuke attacks, essentially by
  switching the roles of rmin and rmax.

81
How Effective Can Attacks Be?

• First A Methodological Note
• Using MovieLens 100K data set
• 50 different "pushed" movies
• selected randomly but mirroring overall
  distribution
• 50 users randomly pre-selected
• Results were averages over all runs for each
  movie-user pair
• K 20 in all experiments
• Evaluating results
• prediction shift
• how much the rating of the pushed movie differs
  before and after the attack
• hit ratio
• how often the pushed movie appears in a
  recommendation list before and after the attack

82
Example Results Average Attack

• Average attack is very effective against user
  based algorithm (Random not as effective)
• Item-based CF more robust (but vulnerable to
  other attack types such as segment attack
  Burke Mobasher, 2005

83
Example Results Bandwagon Attack

• Only a small profile needed (3-7)
• Only a few (lt 10) popular movies needed
• As effective as the more data-intensive average
  attack (but still not effective against
  item-based algorithms)

84
Results Impact of Profile Size
Only a small number of filler items need to be
assigned ratings. An attacker, therefore, only
needs to use part of the product space to make
the attack effective.
In the item-based algorithm we dont see the same
drop-off, but prediction shift shows a
logarithmic behavior near maximum at about 7
filler size.
85
Example Results Segmented Attack Against
Item-Based CF

• Very effective against targeted group
• Best against item-based
• Also effective against user-based
• Low knowledge

86
Possible Solutions

• Explicit trust calculation?
• select peers through network of trust
  relationships
• law of large numbers
• hard to achieve numbers needed for CF to work
  well
• Hybrid recommendation
• Some indications that some hybrids may be more
  robust
• Model-based recommenders
• Certain recommenders using clustering are more
  robust, but generally at the cost of less
  accuracy
• But a probabilistic approach has been shown to be
  relatively accurate
• Detection and Response

87
Results Semantically Enhanced Hybrid
Semantic features extracted for movies top
actors, director, genre, synopsis (top
keywords), etc.
Alpha 0.0 100 semantic item-based
similarity Alpha 1.0 100 collaborative
item-based similarity
88
Approaches to Detection Response

• Profile Classification
• Classification model to identify attack profiles
  and exclude these profiles in computing
  predictions
• Uses the characteristic features of most
  successful attack models
• Designed to increase cost of attacks by detecting
  most effective attacks
• Anomaly Detection
• Classify Items (as being possibly under attack)
• Not dependent on known attack models
• Can shed some light on which type of items are
  most vulnerable to which types of attacks

But, what if the attack does not closely
correspond to known attack signature
In Practice need a comprehensive framework
combining both approaches
89
Conclusions

• Why recommender systems?
• Many algorithmic advances ? more accurate and
  reliable systems ? more confidence by users
• Assist users in
• Finding more relevant information, items,
  products
• Give users alternatives ? broaden user knowledge
• Building communities
• Help companies to
• Better engage users and customers ? building
  loyalty
• Increase sales (on average 5-10)
• Problems and challenges
• More complex Web-based applications ? more
  complex user interactions ? need more
  sophisticated models
• Need to further explore the impact of
  recommendations on (a) user behavior and (b) on
  the evolution of Web communities
• Privacy, security, trust