93,567 research outputs found
A review of associative classification mining
Associative classification mining is a promising approach in data mining that utilizes the
association rule discovery techniques to construct classification systems, also known as
associative classifiers. In the last few years, a number of associative classification algorithms
have been proposed, i.e. CPAR, CMAR, MCAR, MMAC and others. These algorithms
employ several different rule discovery, rule ranking, rule pruning, rule prediction and rule
evaluation methods. This paper focuses on surveying and comparing the state-of-the-art associative
classification techniques with regards to the above criteria. Finally, future directions in associative
classification, such as incremental learning and mining low-quality data sets, are also
highlighted in this paper
Off-line vs. On-line Evaluation of Recommender Systems in Small E-commerce
In this paper, we present our work towards comparing on-line and off-line
evaluation metrics in the context of small e-commerce recommender systems.
Recommending on small e-commerce enterprises is rather challenging due to the
lower volume of interactions and low user loyalty, rarely extending beyond a
single session. On the other hand, we usually have to deal with lower volumes
of objects, which are easier to discover by users through various
browsing/searching GUIs.
The main goal of this paper is to determine applicability of off-line
evaluation metrics in learning true usability of recommender systems (evaluated
on-line in A/B testing). In total 800 variants of recommending algorithms were
evaluated off-line w.r.t. 18 metrics covering rating-based, ranking-based,
novelty and diversity evaluation. The off-line results were afterwards compared
with on-line evaluation of 12 selected recommender variants and based on the
results, we tried to learn and utilize an off-line to on-line results
prediction model.
Off-line results shown a great variance in performance w.r.t. different
metrics with the Pareto front covering 68\% of the approaches. Furthermore, we
observed that on-line results are considerably affected by the novelty of
users. On-line metrics correlates positively with ranking-based metrics (AUC,
MRR, nDCG) for novice users, while too high values of diversity and novelty had
a negative impact on the on-line results for them. For users with more visited
items, however, the diversity became more important, while ranking-based
metrics relevance gradually decrease.Comment: Submitted to ACM Hypertext 2020 Conferenc
Surrogate Functions for Maximizing Precision at the Top
The problem of maximizing precision at the top of a ranked list, often dubbed
Precision@k (prec@k), finds relevance in myriad learning applications such as
ranking, multi-label classification, and learning with severe label imbalance.
However, despite its popularity, there exist significant gaps in our
understanding of this problem and its associated performance measure.
The most notable of these is the lack of a convex upper bounding surrogate
for prec@k. We also lack scalable perceptron and stochastic gradient descent
algorithms for optimizing this performance measure. In this paper we make key
contributions in these directions. At the heart of our results is a family of
truly upper bounding surrogates for prec@k. These surrogates are motivated in a
principled manner and enjoy attractive properties such as consistency to prec@k
under various natural margin/noise conditions.
These surrogates are then used to design a class of novel perceptron
algorithms for optimizing prec@k with provable mistake bounds. We also devise
scalable stochastic gradient descent style methods for this problem with
provable convergence bounds. Our proofs rely on novel uniform convergence
bounds which require an in-depth analysis of the structural properties of
prec@k and its surrogates. We conclude with experimental results comparing our
algorithms with state-of-the-art cutting plane and stochastic gradient
algorithms for maximizing [email protected]: To appear in the the proceedings of the 32nd International Conference
on Machine Learning (ICML 2015
Information filtering via preferential diffusion
Recommender systems have shown great potential to address information
overload problem, namely to help users in finding interesting and relevant
objects within a huge information space. Some physical dynamics, including heat
conduction process and mass or energy diffusion on networks, have recently
found applications in personalized recommendation. Most of the previous studies
focus overwhelmingly on recommendation accuracy as the only important factor,
while overlook the significance of diversity and novelty which indeed provide
the vitality of the system. In this paper, we propose a recommendation
algorithm based on the preferential diffusion process on user-object bipartite
network. Numerical analyses on two benchmark datasets, MovieLens and Netflix,
indicate that our method outperforms the state-of-the-art methods.
Specifically, it can not only provide more accurate recommendations, but also
generate more diverse and novel recommendations by accurately recommending
unpopular objects.Comment: 12 pages, 10 figures, 2 table
Sound ranking algorithms for XML search
Ranking algorithms for XML should reflect the actual combined content and structure constraints of queries, while at the same time producing equal rankings for queries that are semantically equal. Ranking algorithms that produce different rankings for queries that are semantically equal are easily detected by tests on large databases: We call such algorithms not sound. We report the behavior of different approaches to ranking content-and-structure queries on pairs of queries for which we expect equal ranking results from the query semantics. We show that most of these approaches are not sound. Of the remaining approaches, only 3 adhere to the W3C XQuery Full-Text standard
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