Trust Model for High Quality of Recommendations G. Lenzini, N. Sahli, and H. Eertink (Telematica Instituut, NL) G. Lenzini, N. Sahli, and H. Eertink (Telematica Instituut, NL) SECRYPT, special session, Porto, July 2008

Opening

SECRYPT, special session, Porto, July 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)

SECRYPT, special session, Porto, July 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

SECRYPT, special session, Porto, July 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 Systems RECSYS 2007 N. Lathia, S. Hailes, L. Capra, Trust-based Collaborative Filtering IFIPTM 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

SECRYPT, special session, Porto, July Epinions.com

SECRYPT, special session, Porto, July Epinions.com

Our motivation

SECRYPT, special session, Porto, July 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.

SECRYPT, special session, Porto, July Flixter.com

SECRYPT, special session, Porto, July 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

SECRYPT, special session, Porto, July 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?

SECRYPT, special session, Porto, July  In practice: Peer Review of Justification

Our Proposal

SECRYPT, special session, Porto, July 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

SECRYPT, special session, Porto, July Virtual Agora, TRat, TRec ItemsRecommenders Virtual Agora Embedded Delegate registrer of (un)trusted items network of (un)trusted recommender TRat TRec

Trust Model

SECRYPT, special session, Porto, July 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.,

SECRYPT, special session, Porto, July Virtual Agora, TRat, TRec ItemsRecommenders Virtual Agora Embedded Delegate register of (un)trusted items network of (un)trusted recommender TRat TRec

SECRYPT, special session, Porto, July 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)

SECRYPT, special session, Porto, July On High quality of recommendation quality = trust in the source  analysis of justification

SECRYPT, special session, Porto, July 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

SECRYPT, special session, Porto, July 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)

SECRYPT, special session, Porto, July 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)

SECRYPT, special session, Porto, July 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

SECRYPT, special session, Porto, July Argumentation Trust N au = # argument accepted or undefined N r = # argument refused N = N r + N au

SECRYPT, special session, Porto, July 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

SECRYPT, special session, Porto, July Virtual Agora, TRat, TRec ItemsRecommenders Virtual Agora Embedded Delegate register of (un)trusted items network of (un)trusted recommender TRat TRec

SECRYPT, special session, Porto, July 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 and Future Directions

SECRYPT, special session, Porto, July Features of our solution Context-awareness Unobtrusiveness Usefulness Quality Privacy and Subjectiveness Mobility Low Traffic

SECRYPT, special session, Porto, July 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

SECRYPT, special session, Porto, July 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

Not(Questions)  Thanks