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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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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
• Dell’Amico, 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.
Trust and Collaborative Filtering
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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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• 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)?
Virtual Communities Networks of Trust: questions
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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
Items Recommenders
Virtual Agora
Embedded
Delegate
registrer of (un)trusted items
network of (un)trusted recommender
TRat TRec
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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
Items Recommenders
Virtual Agora
Embedded
Delegate
register of (un)trusted items
network of (un)trusted recommender
TRat TRec
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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 D’s recommendation only if D’s trustworthiness combined with an evaluation of the justification that D has given for his recommendation is above a certain threshold.
– D’s justification is a set of arguments supporting the rating gave for each aspects
(e.g., food, ambience, service)
– D’s arguments are evaluated against A’s 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)
• 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
• 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)
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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 A’s argumentation trust in D’s arguments
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Argumentation Trust
Nau = # argument accepted or undefined
Nr = # argument refused
N = Nr + Nau
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Consequences
• D’s arguments can be so strong to have D’s recommendation accepted (by A’s) despite D’s trust as a recommender is not so strong
– (after a real experience) if D’s recommendation was indeed a good one, A’s trust in D increases.
• D’s arguments are so weak to have D’s recommendation refused (by A) despite D’s trust as recommender is high.
– (after a real experience) if D’s recommendation was not a good one, D’s trust is not affected because that recommendation was not accepted anyhow.
• Trust is dynamic
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Virtual Agora, TRat, TRec
Items Recommenders
Virtual Agora
Embedded
Delegate
register of (un)trusted items
network of (un)trusted recommender
TRat TRec
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TRec(A), recommenders
– A’s builds/maintains its trust in D by using a combination of the following strategies:
• evaluation of D’s reputation (as a recommender) according to A’s past experience
• direct evaluation of D by content-based strategies (referral trust bootstrap)
• check between D’s given recommendations and A’s 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