Trust%20Model%20for%20High%20Quality%20of%20Recommendations

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Trust Model for High Quality of Recommendations

• G. Lenzini, N. Sahli, and H. Eertink
• (Telematica Instituut, NL)

SECRYPT, special session, Porto, July 2008
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Opening
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Ratings and Recommender/Review Systems

• Recommender systems aim to predict the rating
  that a user would give to an unknown item (as if
  he had indeed tasted, used, tried it)

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Recommender Systems

• Recommender systems three main categories
• Content based the prediction estimated from the
  ratings that the user has given to similar
  items
• items are similar on content-based factors (tags,
  keywords, ontologies)
• Collaborative (filtering) based the prediction
  estimated from the ratings that similar users
  have given to the item
• users are similar on taste likelihood
  calculated upon common rated items
• Hybrid

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Trust and Collaborative Filtering

• To overcome the limitation of current recommender
  systems (i.e., sparsity and accuracy) very recent
  proposals suggest to substitute the user
  similarity with trust.
• P. Massa, P. Aversani, Trust-aware Recommender
  SystemsRECSYS 2007
• N. Lathia, S. Hailes, L. Capra, Trust-based
  Collaborative FilteringIFIPTM 2008
• DellAmico, L. Capra, SOFIA Social Filtering for
  Robust Recommendations, IFIPTM 2008
• D. Quercia, today
• The experimental results are positive.
  Rummble.com uses trust-based recommendation with
  commercial scope.

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Epinions.com
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Epinions.com
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Our motivation
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Virtual Communities

• We were working on virtual communities in
  e-commerce applications (i.e., recommender and
  reviews systems).
• Virtual communities size may increases quite
  fast. Trust becomes fuzzy quite fast too.

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Flixter.com
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Virtual Communities Networks of Trust questions

• How to provide specific solutions to maintain
  trust relationships in those community? (e.g.,
  autonomous)
• How to increase the trustworthiness of members
  towards the community and the information they
  find there? (e.g., increase personalization)
• What features can be advantageous in the design
  of a trustworthy virtual community (e.g.,
  agent-based, mobility)?
• How to improve current recommender system that
  are based on virtual communities (e.g., by
  improving the quality of recommendation)?

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Quality vs Usefulness

• How to distinguish between a not useful
  recommendation (but coming from a trusted
  recommender) from a recommendation of doubt
  honesty?
• Recommenders experiences might have maturated in
  different contexts. Recommenders may have tastes
  that are completely different from ours.
• That is sufficient/correct to label them as
  untrustworthy?

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In practice Peer Review of Justification
?
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Our Proposal
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Solution for High Quality of Recommendation

• We designed a framework for an hybrid
  recommender/reviews where trust and other
  mechanisms are used to achieved high quality of
  recommendations
• Key concepts
• Trust Model
• Architecture (skipped in the talk, look into the
  paper)

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Key Concepts
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Virtual Agora, TRat, TRec
Embedded
Delegate
Items
Recommenders
TRec
TRat
network of (un)trusted recommender
registrer of (un)trusted items
Virtual Agora
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Trust Model
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Trust Model

• Aim build/use/update TRat(A) and TRec(A)
• Notation
• In TRat(A), agents-items
• In TRec(A), agents-agents (recommenders)
• temporary and eventual, e.g.,

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Virtual Agora, TRat, TRec
Embedded
Delegate
Items
Recommenders
TRec
TRat
network of (un)trusted recommender
register of (un)trusted items
Virtual Agora
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Detail of TRat(A), items

• A rating that a user gives to an item is
  calculated, at a certain time, in a certain
  context, by using a combination of the following
  strategies
• content-based (past experience on the similar
  items, in the same or similar context)
• collaborative filtering approaches (ratings from
  similar users, same or similar items, same or
  similar context)
• trust-based approaches (ratings from trusted
  users, same of similar items, same or similar
  context)
• Recommended ratings are selected/weighted upon
  their quality
• Outputs are merged and recommenders and their
  recommendations are stored (from temporary to
  eventual)

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On High quality of recommendation

• quality trust in the source ? analysis of
  justification

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TRat(A), items Recommendation

• A accepts Ds recommendation only if Ds
  trustworthiness combined with an evaluation of
  the justification that D has given for his
  recommendation is above a certain threshold.
• Ds justification is a set of arguments
  supporting the rating gave for each aspects
  (e.g., food, ambience, service)
• Ds arguments are evaluated against As way of
  reasoning by running an argumentation protocol

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Argumentation Protocol
An argumentation protocol is a composition of
dialogue games (primitives assert, attack,
defend, challenge, justify, accept, refuse, or
declare undefined) Logic-based, efficient,
implementation of argumentation protocols are
available in the literature (J. Bentahar and J.J.
Meyer, 2007)
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Example (informal version)

• Olga
• why? (asking for ground)
• I may not like the place (stating a
  counter-argument)
• since traditional cooking may be not clean
  (ground for the counter-argument)
• is not for that that I am willing to pay a price
  (alternative counter-arguments)
• (refuse the argument)

• Paul
• I love that place (claim)
• They serve traditional food, cooked in the
  traditional way.(grounds for a claim)
• why? (asking for ground)
• yes, sometimes, it is the price you pay for
  discovering new tastes (undercutting
  counter-argument)
• Ok, I agree

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Running an Argumentation Protocol
A and D run a protocol to argue on the arguments
that D has given for each aspect of its
recommendation. Output of the protocol a value of
As argumentation trust in Ds arguments
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Argumentation Trust
Nau argument accepted or undefined Nr
argument refused N Nr Nau
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Consequences

• Ds arguments can be so strong to have Ds
  recommendation accepted (by As) despite Ds
  trust as a recommender is not so strong
• (after a real experience) if Ds recommendation
  was indeed a good one, As trust in D increases.
• Ds arguments are so weak to have Ds
  recommendation refused (by A) despite Ds trust
  as recommender is high.
• (after a real experience) if Ds recommendation
  was not a good one, Ds trust is not affected
  because that recommendation was not accepted
  anyhow.
• Trust is dynamic

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Virtual Agora, TRat, TRec
Embedded
Delegate
Items
Recommenders
TRec
TRat
network of (un)trusted recommender
register of (un)trusted items
Virtual Agora
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TRec(A), recommenders

• As builds/maintains its trust in D by using a
  combination of the following strategies
• evaluation of Ds reputation (as a recommender)
  according to As past experience
• direct evaluation of D by content-based
  strategies (referral trust bootstrap)
• check between Ds given recommendations and As
  direct experience w.r.t. items recommended by D

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Conclusion andFuture Directions
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Features of our solution

• Context-awareness
• Unobtrusiveness
• Usefulness
• Quality
• Privacy and Subjectiveness
• Mobility
• Low Traffic

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On going work Duine Toolkit

• We have already implemented a prototype JADEX
  (Jadex 2008) as a development environment, which
  handles BDI concept.
• In order to commercialise our solution and make
  it useful for the market, we are currently
  integrating our approach to a set of well-known
  techniques.
• Duine Toolkit (M. Van Setten et al, 2004),
  developed in our Institute, is a framework for
  hybrid recommender which makes available a
  number of prediction techniques and allows them
  to be combined dynamically

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On going, future work

• Have the solution implemented in a review site
• Evaluation by return of business-based metrics
• Mobility and automatic context capture with
  IYOUIT

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Not(Questions) ? Thanks(gabriele.lenzini_at_telin.
nl)