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The phishing scam and its variants are estimated to cost victims billions of dollars per year. Researchers have responded with a number of anti-phishing systems, based either on blacklists or on heuristics. The former cannot cope with the churn of phishing sites, while the latter usually employ decision rules that are not congruent to human perception. We propose a novel heuristic anti-phishing system that explicitly employs gestalt and decision theory concepts to model perceptual similarity. Our system is evaluated on three corpora contrasting legitimate Web sites with real-world phishing scams. The proposed system’s performance was equal or superior to current best-of-breed systems. We further analyze current anti-phishing warnings from the perspective of warning theory, and propose a new warning design employing our Gestalt approach.

Recommendation engines are one of the “discovery” products built into integrated library systems. These are a subclass of enterprise systems designed specifically for public and research libraries that incorporate an electronic card catalogue, circulation and inventory management, personnel and payroll systems, etc. The system vendors offer customizations for different contexts of specific library systems, but cannot create a bespoke solution for every customer. Our partner, an Edmonton‐area company, is filling this gap for a consortium of rural libraries in Alberta by creating a mobile app that interfaces with their electronic card catalog. Rural libraries are generally smaller than major urban public libraries, meaning that their holdings are limited overall, and within any given genre. This poses a severe problem for traditional collaborative‐filtering recommender algorithms, as the item sets for recommendations are limited by supply rather than by readers’ interests. The library's relatively small clientele also limits the item sets available for comparison. To deal with this ongoing “cold‐start” problem, we propose a hybridization of collaborative filtering with a content filter using a fuzzy taste vector. Experiments on two benchmark recommender data sets show that this approach is at least as accurate as existing fuzzy recommenders and is particularly effective on sparse data sets.

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