Personalized web search engine for mobile devices

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Personalized web search engine for mobile devices

• IIIA-2006

Vasudeva Varma, N. Sriharsha, Prasad
Pingali International Institute of Information
Technology, Hyderabad, India vv,
pvvpr_at_iiit.ac.in Murali Krishna Punaganti
Venkata Nokia Research Center, Helsinki,
Finland murali-krishna.punaganti_at_nokia.com July
06, 2006
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Overview

• Search Engines State of the Art
• Personalization and User Profiles
• Personalizing Search Results
• Experiments

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Search Engines Today

• Problems
• Search Engines are impersonal
• Do not consider user expertise level
• ambiguity ( e.g., jaguar, apple )
• Not tuned to mobile environment
• Solution
• Improving search accuracy by
• retrieving by category (e.g., animal, fruit)
• observing mobile user behaviour
• matching user interests
• taking into account user expertise level

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Personalization

• Adaptation of search engine for specific user
  needs
• Typically maintains user profiles
• Typically makes use of the context
• User profile represents the users interests.

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Aspects of User Profile

• User Modeling
• User Profile Modeling
• User Profile Representation
• User Profile Creation
• Updating User Profile

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User Modeling

• Data About Users
• Demographic Who the user is?
• Transactional What the user does?
• Profile Model
• Factual Stated or derived facts
• Behavioral Conjunctive, associative or
  classification rules

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User Profile Modeling

• Explicit and Implicit
• Static and Dynamic
• Long Term and Short Term
• Expertise level

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Sources of User Information

• User explicit information
• User Context
• User browsing histories
• User desktops/mobile applications
• Device profiles
• User search histories

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Our Mobile Search Personalization Architecture
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Process Distribution

• Client Side (Mobile Device)
• The client contains Observer Module
• Keeps track of the users search history (logger)
  and the applications opened.
• The statistics are sent to the server
• Server Side
• Maintains user profiles and device profiles
• Major modules include crawler, indexer, and
  personalization filter.
• Indirect proxy server is used to track the user
  from the server side.
• Creates query log for future analysis.

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User Profile Representation

• As a probabilistic distribution of weights over
  terms
• As a probabilistic distribution of weights over
  categories.

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User Profile Creation

• Collect information about the users interests
  from logs
• Categorize a relevant document into category
  hierarchy
• The determined Category weights are used to
  capture the user interests

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Updating User Profile

• The above formula is used to update the weights
  in user profile using a relevant document dN1
  from the relevance feedback
• where,
• Wui is the weight of category Ci in a user
  profile u.
• N is the total number of past relevant documents.
• dN1 is the current relevant document
• ? is the learning rate which ranges from 0 to 1.
• widN1 is the weight of document dN1 in Ci
• widk is the weight of past relevant document dk
  in Ci

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Features of the Formula

• It is effective in terms of space and complexity.
• It not only uses the weight of the current
  document but also the weights of past documents.
• It has a factor ? which controls the learning
  rate.

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Personalizing Search Results

• Process Framework
• Re-ranking
• Ranking Algorithm

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Personalization Process
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Personalization Process

• Submit query to search engine(s).
• Categorize each result or the snippet/small
  summary of each result into a hierarchy
• The snippet is used to decide on the relevance of
  the result.

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Re-Ranking

• Personalization Score of a result is based on
  similarity between each result profile and user
  profile.
• Re-rank results based on
• Personalization score, and
• Rank given by the search engine

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Ranking Algorithm

• Rank (R) a (PS(R)) (1- a) (S(R))
• R is the result
• S(R) is original ranking given by search engine
• PS(R) is the personalization score of the
  result R.
• a is the personalization factor which ranges
  from 0 to 1.

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Experimental Setup

• 7 users
• Each search record contains a query and the set
  of relevant documents.
• 10-fold cross validation technique
• Divided each users query set into 10
  approximately equal subsets.

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Tools Used

• An open source categorizer and the ODP structure
• Only the first level of ODP
• A total of 11 categories are used for training.
• Lucene to index all the relevant documents.

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Process

• Training on 9 subsets and the 10th one for
  testing the userprofile.
• Tested in two ways
• Personalization
• Trained the userprofile with 9 subsets and tested
  with the testing subset.
• Adaptiveness
• While training each subset, tested with the
  testing subset.

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Calculating Measures

• For each query, precision at 11 recall values is
  calculated.
• Calculated the measure of average precision of
  all queries per each user.
• over all the possible consecutive sets of
  training, and
• over all the possible sets of testing.

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Adaptive Results for ?0.5 and a0.5

• (Average Precision values with increase in
  training size )
• Size user1 user2 user3 user4 user5 user6 user
  7
• 1 0.6168 0.4318 0.4763 0.7578 0.7634 0.7471
  0.8845
• 2 0.6597 0.5536 0.5308 0.7361 0.7931 0.78
  20 0.7719
• 3 0.6302 0.5770 0.5362 0.7890 0.7250
  0.8248 0.7694
• 4 0.6340 0.5444 0.5143 0.7493 0.7072 0.8184
  0.7502
• 5 0.6507 0.6307 0.6213 0.6701 0.6526 0.83
  21 0.7399
• 6 0.6309 0.6259 0.5592 0.7636 0.6812 0.82
  87 0.7138
• 7 0.6490 0.6244 0.5985 0.7922 0.7168
  0.8203 0.7364
• 8 0.5984 0.6084 0.5536 0.8122 0.7335
  0.8278 0.7482
• 9 0.6754 0.6327 0.5764 0.7798 0.8122
  0.7978 0.8393
• 10 0.7064 0.6962 0.7171 0.8380 0.8397
  0.8410 0.8974
• Lucene Results (Average Precision for all
  queries)
• user1 user2 user3 user4 user5 user6 user7
• 0.5332 0.5292 0.5839 0.8369 0.9211 0.7646 0.8584
  

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Inference

• The increment of precision with the increase in
  training size is not so consistent.
• This may be because there is randomness in
  selecting the training and testing subsets.
• When all the subsets are used for testing, the
  final precision values with personalization is
  greater than those without personalization
  (almost in all cases)

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Results of (Single user) Experiment II
Personalization -gt Relevant Documents V YES NO
YES 0.877489177489 0.80759018759
NO 0.852943722944 0.891474667308
Inference Precision increased when tested with
the relevant set of queries and it is decreased
when tested with the non-relevant set of queries.
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Thank you

• Questions?