Taxonomy-Driven Filtering

One of the primary issues that recommender systems are facing is rating sparsity, resulting in a decrease of the recommendations’ accuracy.Hence, high-quality product suggestions are only feasible when information density is high, i.e., large numbers of users voting for small numbers of items and issuing large numbers of explicit ratings each. Smaller-sized, decentralized and open communities are typical for the Web 2.0. Here, ratings are mainly derived implicitly from user behavior and interaction patterns. However, these communities poorly qualify for blessings provided by recommender systems.