This paper proposes a swarm intelligence based recommender system (FlockRecom) based on the collaborative behavior of bird flocks for generating Top-N recommendations. The flock-based recommender algorithm (FlockRecom) iteratively adjusts the position and speed of dynamic flocks of agents on a visua

In this paper, we use a flock of agent-based swarm intelligence approach for simultaneously clustering and visualizing high-dimensional Web usage data. Our approach is based on improvements that overcome several limitations of the FClust algorithm. Our proposed approach is a hybrid, combining the strengths of the spherical k-means algorithm for fast clustering of high-dimensional data sets in the original feature domain and the flock-based (FClust) algorithm which iteratively adjusts the position and speed of dynamic flocks of agents on a visualization plane. Our hybridization decreases the complexity of FClust from quadratic to linear, with further improvements in the cluster quality. Experiments on real data illustrate the workings of the hybrid algorithm and its advantages over its FClust baseline.

In this paper, we present a complete framework and findings in mining Web usage patterns from Web log files of a real Web site that has all the challenging aspects of real-life Web usage mining, including evolving user profiles and external data describing an ontology of the Web content. Even though the Web site under study is part of a nonprofit organization that does not "sell" any products, it was crucial to understand "who" the users were, "what" they looked at, and "how their interests changed with time," all of which are important questions in Customer Relationship Management (CRM). Hence, we present an approach for discovering and tracking evolving user profiles. We also describe how the discovered user profiles can be enriched with explicit information need that is inferred from search queries extracted from Web log data. Profiles are also enriched with other domain-specific information facets that give a panoramic view of the discovered mass usage modes. An objective validation strategy is also used to assess the quality of the mined profiles, in particular their adaptability in the face of evolving user behavior.