A Model of Information Foraging via Ant Colony Simulation Matthew Kusner

Information Foraging Theory Background – People search for information in roughly the same way that animals search for food in their surroundings. Information Scent – Ex: “the text associated with Web links” (Fu, 2007) – Background knowledge – Recommendations

Ant Colony Simulation Pheromone trails – Laid by ants who've found food. – Followed by other ants with probability p. – Path Evaporation Path Optimization Simulation specifics

AOL Data Set 21 million queries (March 1– May 31, 2006) 650k users19 million click-through events Quantities:querytime of query click URLuser IDclicked link rank

Information Foraging → Ant Colony user → ant clicked link → food information scent → pheromone path website importance → food distance where website importance is defined by: – 1. Rank – 2. Popularity of website – 3. Combination of above methods

Distancing Methods Ranking Popularity Combination [based on data in Joachims et al., 2005]

Results AOL user-visit per website vector – [numWvisits 1, numWvisits 2,..., numWvisits n ] Simulation ant-visit per food vector – [numAvisits 1, numAvisits 2,..., numAvisits n ] Pearson Correlation Score (PCS) Permutation Test → 95% Coverage Interval – (AOL_data i, simulation_data i ) selection with replacement Bootstrapping → p-value – Shuffle AOL vector

Query Type of distancing # of users # of clicked links # of distinct websites visited Average PCS Average 95% CI Start Average 95% CI End Significa nt p-val? ranking Yes vacationpopularity combination ranking rhinopopularity combination ranking zebrapopularity combination ranking lionpopularity combination ranking footballpopularity combination ranking basketballpopularity combination

Results Queries with significant p-values: – vacation” (ranking), “baseball” (ranking), “reebok” (ranking), “adidas” (ranking), “marbles” (ranking), “helicopter” (ranking), “car” (ranking), “potatoes” (ranking), “coffee” (ranking), “farming” (ranking), “rock” (popularity), “shirts” (ranking), “playstation” (ranking), “sega” (popularity), “tom cruise” (ranking), “mel gibson” (ranking), “burger king” (ranking), “chicago” (ranking), “los angeles” (ranking), and “paris” (ranking) Distancing methods without 95% CI overlap: – Ranking: “potatoes” - neither popularity, nor combination “shirts” - not popularity “playstation” - not popularity “burger king” - not combination

Discussion Disadvantages of popularity and combination methods – “vacation” example Possible reasons for 95% CI overlap – Randomness – Disregard of structure Significance of queries with low p-values – Search engine matching Future directions – Different Simulation – Other similarity metrics – Random beginnings

References Fu, W., & Pirolli, P. (2007). SNIF-ACT: a cognitive model of user navigation on the World Wide Web. Human-Computer Interaction, 22(4), T. Joachims, L. Granka, B. Pang, H. Hembrooke, and G. Gay (2005). Accurately Interpreting Clickthrough Data as Implicit Feedback, Proceedings of the ACM Conference on Research and Development on Information Retrieval (SIGIR).