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Silva, R.M.; Gonçalves, M.A.; Veloso, A.: ¬A Two-stage active learning method for learning to rank (2014)
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- Abstract
- Learning to rank (L2R) algorithms use a labeled training set to generate a ranking model that can later be used to rank new query results. These training sets are costly and laborious to produce, requiring human annotators to assess the relevance or order of the documents in relation to a query. Active learning algorithms are able to reduce the labeling effort by selectively sampling an unlabeled set and choosing data instances that maximize a learning function's effectiveness. In this article, we propose a novel two-stage active learning method for L2R that combines and exploits interesting properties of its constituent parts, thus being effective and practical. In the first stage, an association rule active sampling algorithm is used to select a very small but effective initial training set. In the second stage, a query-by-committee strategy trained with the first-stage set is used to iteratively select more examples until a preset labeling budget is met or a target effectiveness is achieved. We test our method with various LETOR benchmarking data sets and compare it with several baselines to show that it achieves good results using only a small portion of the original training sets.
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Ferreira, A.A.; Veloso, A.; Gonçalves, M.A.; Laender, A.H.F.: Self-training author name disambiguation for information scarce scenarios (2014)
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- Abstract
- We present a novel 3-step self-training method for author name disambiguation-SAND (self-training associative name disambiguator)-which requires no manual labeling, no parameterization (in real-world scenarios) and is particularly suitable for the common situation in which only the most basic information about a citation record is available (i.e., author names, and work and venue titles). During the first step, real-world heuristics on coauthors are able to produce highly pure (although fragmented) clusters. The most representative of these clusters are then selected to serve as training data for the third supervised author assignment step. The third step exploits a state-of-the-art transductive disambiguation method capable of detecting unseen authors not included in any training example and incorporating reliable predictions to the training data. Experiments conducted with standard public collections, using the minimum set of attributes present in a citation, demonstrate that our proposed method outperforms all representative unsupervised author grouping disambiguation methods and is very competitive with fully supervised author assignment methods. Thus, different from other bootstrapping methods that explore privileged, hard to obtain information such as self-citations and personal information, our proposed method produces topnotch performance with no (manual) training data or parameterization and in the presence of scarce information.
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Bessa, A.; Santos, R.L.T.; Veloso, A.; Ziviani, N.: Exploiting item co-utility to improve collaborative filtering recommendations (2017)
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- Abstract
- In this article we study the extent to which the interplay between recommended items affect recommendation effectiveness. We introduce and formalize the concept of co-utility as the property that any pair of recommended items has of being useful to a user, and exploit it to improve collaborative filtering recommendations. We present different techniques to estimate co-utility probabilities, all of them independent of content information, and compare them with each other. We use these probabilities, as well as normalized predicted ratings, in an instance of an NP-hard problem termed the Max-Sum Dispersion Problem (MSDP). A solution to MSDP hence corresponds to a set of items for recommendation. We study one heuristic and one exact solution to MSDP and perform comparisons among them. We also contrast our solutions (the best heuristic to MSDP) to different baselines by comparing the ratings users give to different recommendations. We obtain expressive gains in the utility of recommendations and our solutions also recommend higher-rated items to the majority of users. Finally, we show that our co-utility solutions are scalable in practice and do not harm recommendations' diversity.
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