IntelliShopper A Proactive, Personal, Private Shopping Assistant

1. IntelliShopperA Proactive, Personal, Private Shopping Assistant by Filippo Menczer, W. Nick Street, Narayan Vishwakarma, Alvaro E. Monge, Markus Jakobsson, In The proceedings of AAMAS 2002, First International Joint Conference on Autonomous Agents and Multi-Agent Systems, Bologna, Italy, July 2002. Dissertation by Sujoe Bose

2. Setting the context • Unprecedented growth of e-commerce • Information overload • Filtering Information of relevance/interest • Prioritization of relevant information • Enter IntelliShopper - Shopping assistant designed to empower consumers

3. IntelliShopper • Personal, Proactive, Privacy: • observes users while shopping • learns preferences • Compare with user explicitly specifying preferences • Exercises preferences for users benefit • Applies feedback using heuristics (Interesting) • Protects users privacy by pseudonymity

4. IntelliShopper Architecture • Privacy Agent • Maintains Shopping Persona • Learning Agent Focus • Maintains, Observes, Refines and Applies Interests and Preferences • Monitor Agent • Proactively seeks for new products/prices of interest

5. Learning Agent • Adapts to user preferences to better rank hits • Learning by Observation • Continuous feedback learning with minimum User intervention • Inductive Machine Learning • Apply recent rankings to results in past and consolidate

6. Inductive Learning • Increase ranking of hits similar to those in past • Knowledge gained by tracking user’s selection • Decrease ranking of hits similar to those ignored or actively disliked • ignore: Items in list prior to the one selected are “user ignored” • dislike: Tracking user click on “remove” option

7. Adaptation Scheme • Based on a set of features extracted from the hits • chosen by relevance to user evaluation • In the current model the following were selected • price of item • number of bids placed • time remaining in the auction • similarity between the query and item description

8. Adaptation Scheme • Temperature of feature • degree of likes/dislikes observed by agent • Each feature is associated with a temperature • Higher the temperature, more the desirability • Continuous variables are discretized. • price feature could have high temperature for the value “low” • Cutpoints for discretization based on mean  and standard deviation 

9. Temperature manipulation • Temperatures for features are updated based on user actions • User clicks item 2, which is high-priced • Temperature for “high price” is increased • user interested in expensive coffee • Temperature for “avg price” is decreased • used glossed over item 1, item ignored • T(t+1) = 1T(t) + 2 T • 1, 2 Ratio preference for past to present change

10. Temperature manip (contd) • Four possible reactions to each hit • Buy: Strong positive feedback T = +0.5 • Browse: Weak positive feedback T = +0.25 • Ignore: Weak negative feedback T = -0.25 • Remove: Strong negative feedback T = -0.5 • Temperature values updated • Subsequent queries will rank the results based on temperature values

11. IntelliShopperQuestions ???