Trust Model for High Quality of Recommendations

Trust Model for High Quality of Recommendations

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

SECRYPT, special session, Porto, July 2008

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

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

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)

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

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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Items Recommenders

Virtual Agora

Embedded

Delegate

register of (un)trusted items


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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Items Recommenders

Virtual Agora

Embedded

Delegate

register of (un)trusted items


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

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

https://www.iyouit.eu/portal/Faq.aspx

Not(Questions) Thanks

([email protected])

Trust Model for High Quality of Recommendations

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