An extended Q-gram algorithm for calculating the relevance factor of products in electronic marketplaces

Intelligent agents offer a number of advantages when used in electronic markets. In such environments, intelligent agents can represent users acting as buyers or sellers. On the buyer’s side, an intelligent agent can undertake the responsibility of finding and purchasing products that meet the owner’s needs. In this process, the agent should decide if a product, offered by a seller, is relevant to the owner’s preferences. We propose an algorithm for calculating the relevance factor of a product based on the product description, constraints defined by the buyer and the product’s quality of service characteristics, such as the delivery time or the seller trust level. The proposed algorithm is based on widely known similarity assessment techniques. However, we also propose a new similarity assessment scheme based on the Q-grams technique. We describe the proposed solution and evaluate our methodology. The results show that the algorithm is an efficient way for the relevance factor calculation and quality of service characteristics play an important role in the calculation process. Quality of service factor calculation provides an additional level of intelligence in the proposed methodology.