Marco Túlio Ribeiro

Traditional content-based e-mail spam filtering takes into account content of e-mail messages and apply machine learning
techniques to infer patterns that discriminate spams from
hams. In particular, the use of content-based spam filtering
unleashed an unending arms race between spammers and filter developers, given the spammers' ability to continuously
change spam message content in ways that might circumvent
the current filters. In this paper, we propose to expand… Show more

Traditional content-based e-mail spam filtering takes into account content of e-mail messages and apply machine learning
techniques to infer patterns that discriminate spams from
hams. In particular, the use of content-based spam filtering
unleashed an unending arms race between spammers and filter developers, given the spammers' ability to continuously
change spam message content in ways that might circumvent
the current filters. In this paper, we propose to expand the
horizons of content-based filters by taking into consideration
the content of the Web pages linked by e-mail messages.
We describe a methodology for extracting pages linked
by URLs in spam messages and we characterize the rela-
tionship between those pages and the messages. We then
use a machine learning technique (a lazy associative classifier) to extract classification rules from the web pages that
are relevant to spam detection. We demonstrate that the
use of information from linked pages can nicely complement
current spam classification techniques, as portrayed by SpamAssassin. Our study shows that the pages linked by spams
are a very promising battleground Show less

This demo presents Spam Miner, an online system designed
for real-time monitoring and characterization of spam traffic over the Internet. Our system is based on high-level
abstractions such as spam message attributes, spam campaigns and spamming strategies...

In this paper we propose new approaches for multi-objective recommender systems based on the concept of Pareto-efficiency − a state achieved when the system is devised in the
most efficient manner in the sense that there is no way to improve one of the objectives without making
any other objective worse off. Given that existing multi-objective recommendation algorithms differ in their
level of accuracy, diversity and novelty, we exploit the Pareto-efficiency concept in two distinct… Show more

In this paper we propose new approaches for multi-objective recommender systems based on the concept of Pareto-efficiency − a state achieved when the system is devised in the
most efficient manner in the sense that there is no way to improve one of the objectives without making
any other objective worse off. Given that existing multi-objective recommendation algorithms differ in their
level of accuracy, diversity and novelty, we exploit the Pareto-efficiency concept in two distinct manners:
(i) the aggregation of ranked lists produced by existing algorithms into a single one, which we call Pareto-efficient ranking, and (ii) the weighted combination of existing algorithms resulting in a hybrid one, which
we call Pareto-efficient hybridization. Our evaluation involves two real application scenarios: music recommendation with implicit feedback (i.e., Last.fm) and movie recommendation with explicit feedback (i.e., MovieLens). We show that the proposed Pareto-efficient approaches are effective in suggesting items that
are likely to be simultaneously accurate, diverse and novel. We discuss scenarios where the system achieves
high levels of diversity and novelty without compromising its accuracy. Further, comparison against multi-
objective baselines reveals improvements in terms of accuracy (from 10.4% to 10.9%), novelty (from 5.7% to 7.5%), and diversity(from 1.6% to 4.2%).
Show less

Performing accurate suggestions is an objective of paramount importance for effective recommender systems.
Other important and increasingly evident objectives are novelty and diversity, which are achieved by recommender systems that are able to suggest diversified items
not easily discovered by the users. Different recommendation algorithms have
particular strengths and weaknesses when it comes to each of these objectives, motivating the construction
of hybrid approaches. However… Show more

Performing accurate suggestions is an objective of paramount importance for effective recommender systems.
Other important and increasingly evident objectives are novelty and diversity, which are achieved by recommender systems that are able to suggest diversified items
not easily discovered by the users. Different recommendation algorithms have
particular strengths and weaknesses when it comes to each of these objectives, motivating the construction
of hybrid approaches. However, most of these approaches only focus on
optimizing accuracy, with no regard for novelty and diversity. The problem of combining
recommendation algorithms grows significantly harder
when multiple objectives are considered simultaneously. For instance, devising
multi-objective recommender systems that suggest items
that are simultaneously accurate, novel and diversified
may lead to a conflicting-objective problem, where the attempt to
improve an objective further may result in worsening other competing objectives.
In this paper we propose a hybrid recommendation approach
that combines existing
algorithms which differ in their level of accuracy, novelty and diversity.
We employ an evolutionary search for hybrids following the Strength Pareto approach,
which isolates hybrids that are not dominated by others (i.e., the so
called Pareto frontier).
Experimental results on two recommendation scenarios show that: (i) we can combine
recommendation algorithms in order to improve an
objective without significantly hurting other objectives,
and (ii) we allow for adjusting the compromise between accuracy, diversity and novelty,
so that the recommendation emphasis can be adjusted dynamically according to the needs of
different users. Show less