Abstracts of Recent PhDs
The Knowledge Engineering Review · 2007
Nowadays, people have access to a huge amount of information due to the Internet's resources.However, they spend too much time searching for interesting, adequate or useful information.The difficulty to find worthwhile information increases when interesting things dispute the user's attention.Information retrieval and information-filtering systems are applicable in order to minimize search difficulties, aiming to aid the user in the search for worthwhile information.Information retrieval systems are widely spread in the Internet through search engines (e.g.google, av, citeseer).However, there is a problem in this kind of application, which consists in compelling the user to know the terms (keywords) that are relevant for the search.Recommender Systems are an information-filtering solution.They present a different approach that frees the user from creating queries with keywords.It means that the system tries to match the user's profile (historical interests) with the content of items to be recommended, and then offers these items to the user (recommendee).In parallel, an alternative approach to item recommendation was proposed, this one based on the offering of items based on other users' opinion, that is, the user receives an item recommendation based on the evaluation of other users (collaborative filtering or social filtering).However, a different question is raised here-how much the opinion of a user who evaluated an item is relevant to be employed in the recommendation process applying a collaborative method?This thesis presents a new approach to model and include in the collaborative recommendation process an additional information named Recommender's Rank, which represents the relevance of the user's opinion and complements the typical information used in the large majority of Recommender Systems.This approach is an alternative to aid the user to identify the importance of a recommended item based on other users' opinions, as people with higher relevance of opinion are more likely to better evaluate and recommend items.