Ricardo Marcondes Marcacini

Incremental hierarchical text clustering with privileged information

In many text clustering tasks, there is some valuable knowledge about the problem domain, in addition to the original textual data involved in the clustering process. Traditional text clustering methods are unable to incorporate such additional (privileged) information into data clustering. Recently, a new paradigm called LUPI - Learning Using Privileged Information - was proposed by Vapnik to incorporate privileged information in classification tasks. In this paper, we extend the LUPI paradigm to deal with text clustering tasks. In particular, we show that the LUPI paradigm is potentially promising for incremental hierarchical text clustering, being very useful for organizing large textual databases. In our method, the privileged information about the text documents is applied to refine an initial clustering model by means of consensus clustering. The initial model is used for incremental clustering of the remaining text documents. We carried out an experimental evaluation on two benchmark text collections and the results showed that our method significantly improves the clustering accuracy when compared to a traditional hierarchical clustering method.

Using Contextual Information from Topic Hierarchies to Improve Context-Aware Recommender Systems

Unlike the traditional recommender systems, that make recommendations only by using the relation between user and item, a context-aware recommender system makes recommendations by incorporating available contextual information into the recommendation process as explicit additional categories of data to improve the recommendation process. In this paper, we propose to use contextual information from topic hierarchies to improve the accuracy of context-aware recommender systems. Additionally, we also propose two context-aware recommender algorithms for item recommendation. These are extensions from algorithms proposed in literature for rating prediction. The empirical results demonstrate that by using topic hierarchies our technique can provide better recommendations.

Improving Personalized Ranking in Recommender Systems with Topic Hierarchies and Implicit Feedback

The knowledge of semantic information about the content and users preferences is an important issue to improve recommender systems. However, the extraction of such meaningful metadata needs an intense and time-consuming human effort, which is impractical specially with large databases. In this paper, we mitigate this problem by proposing a recommendation model based on latent factors and implicit feedback which uses an unsupervised topic hierarchy constructor algorithm to organize and collect metadata at different granularities from unstructured textual content. We provide an empirical evaluation using a dataset of web pages written in Portuguese language, and the results show that personalized ranking with better quality can be generated using the extracted topics at medium granularity.

Exploiting Text Mining Techniques for Contextual Recommendations

Unlike traditional recommender systems, which make recommendations only by using the relation between users and items, a context-aware recommender system makes recommendations by incorporating available contextual information into the recommendation process. One problem of context-aware approaches is that it is required techniques to extract such additional information in an automatic manner. In this paper, we propose to use two text mining techniques which are applied to textual data to infer contextual information automatically: named entities recognition and topic hierarchies. We evaluate the proposed technique in four context-aware recommender systems. The empirical results demonstrate that by using named entities and topic hierarchies we can provide better recommendations.

On the use of consensus clustering for incremental learning of topic hierarchies

Incremental learning of topic hierarchies is very useful to organize and manage growing text collections, thereby summarizing the implicit knowledge from textual data. However, currently available methods have some limitations to perform the incremental learning phase. In particular, when the initial topic hierarchy is not suitable for modeling the data, new documents are inserted into inappropriate topics and this error gets propagated into future hierarchy updates, thus decreasing the quality of the knowledge extraction process. We introduce a method for obtaining more robust initial topic hierarchies by using consensus clustering. Experimental results on several text collections show that our method significantly reduces the degradation of the topic hierarchies during the incremental learning compared to a traditional method.

Using Topic Hierarchies with Privileged Information to Improve Context-Aware Recommender Systems

Recommender systems are designed to assist individuals to identify items of interest in a set of options. A context-aware recommender system makes recommendations by incorporating available contextual information into the recommendation process. One of the major challenges in context-aware recommender systems research is the lack of automatic methods to obtain contextual information for these systems. Considering this scenario, in this paper, we propose to use contextual information from topic hierarchies to improve the performance of context-aware recommender systems. Three different types of topic hierarchies are constructed by using the LUPI-based Incremental Hierarchical Clustering method: a topic hierarchy using only a traditional bag-of-words, a second topic hierarchy using a bag-of-words of named entities and a third topic hierarchy using both information. We evaluate the contextual information in four context-aware recommender systems. The empirical results demonstrate that by using topic hierarchies we can provide better recommendations.

Improving consensus clustering of texts using interactive feature selection

Consensus clustering and interactive feature selection are very useful methods to extract and manage knowledge from texts. While consensus clustering allows the aggregation of different clustering solutions into a single robust clustering solution, the interactive feature selection facilitates the incorporation of the users experience in text clustering tasks by selecting a set of high-level features. In this paper, we propose an approach to improve the robustness of consensus clustering using interactive feature selection. We have reported some experimental results on real-world datasets that show the effectiveness of our approach.

Constrained Hierarchical Clustering for News Events

Knowledge discovery from web news events has received great attention in recent years. In practice, this knowledge is a digital representation (virtual world) of various phenomena that occur in our physical world. Hierarchical clustering algorithms are used to organize related events into groups and subgroups according to some similarity measure. The main motivation for this organization is based on the hypothesis that if the user is interested in a specific event of a certain cluster, then the user may also be interested in other related events of this same cluster. However, existing event clustering methods do not effectively use the different types of information about events, such as temporal information, geographical data, name of people and organizations. In this paper, we propose the COH-KMeans algorithm (Constrained Hierarchical K-Means) that obtains a hierarchical clustering structure considering certain conditions imposed by the users, for example, events of similar content that occurred in nearby geographic locations or that occurred within a predefined time window. A statistical analysis of the experimental results reveals that the incorporation of constraints performed by COH-KMeans allows to obtain higher quality clusters when compared to a state-of-the-art unsupervised hierarchical clustering method. Moreover, we present our tool for exploratory analysis of events and we discuss how event clustering can be used to support the decision-making process from the perspective of a Data Analytics System.

On combining Websensors and DTW distance for kNN Time Series Forecasting

In the pattern recognition field, different approaches have been proposed to improve time series forecasting models. In this sense, k-Nearest-Neighbour (kNN) with DTW (Dynamic Time Warping) distance is one of the most representative methods, due to its effectiveness, simplicity and intuitiveness. The great advantage of the DTW distance is the robustness to distortions in the time axis by allowing stretching and squeezing (time warping) of the time series, while traditional measures require a linear alignment between each data point. However, as well as other traditional measures, the DTW distance has the limitation of focusing only on historical time series data to predict future values, thereby not considering additional external knowledge of the problem domain. In this paper, we propose an approach called TSFW (Time Series Forecasting with Websensors) that incorporates Websensors into DTW distance to improve kNN time series forecasting. Websensors are models that represent knowledge extracted from news about the problem domain as well as the temporal evolution of this knowledge. In our proposed TSFW approach, we show that Websensors allow a more robust non-linear alignment of the time series by using similar events (extracted from news) that have occurred in the both time series. Thus, distortions in the time axis among the time series can be corrected more accurately compared to the traditional technique that uses only the original values of the time series.

Cross-domain aspect extraction for sentiment analysis: A transductive learning approach

Abstract Aspect-Based Sentiment Analysis (ABSA) is a promising approach to analyze consumer reviews at a high level of detail, where the opinion about each feature of the product or service is considered. ABSA usually explores supervised inductive learning algorithms, which requires intense human effort for the labeling process. In this paper, we investigate Cross-Domain Transfer Learning approaches, in which aspects already labeled in some domains can be used to support the aspect extraction of another domain where there are no labeled aspects. Existing cross-domain transfer learning approaches learn classifiers from labeled aspects in the source domain and then apply these classifiers in the target domain, i.e., two separate stages that may cause inconsistency due to different feature spaces. To overcome this drawback, we present an innovative approach called CD-ALPHN (Cross-Domain Aspect Label Propagation through Heterogeneous Networks). First, we propose a heterogeneous network-based representation that combines different features (labeled aspects, unlabeled aspects, and linguistic features) from source and target domain as nodes in a single network. Second, we propose a label propagation algorithm for aspect extraction from heterogeneous networks, where the linguistic features are used as a bridge for this propagation. Our algorithm is based on a transductive learning process, where we explore both labeled and unlabeled aspects during the label propagation. Experimental results show that the CD-ALPHN outperforms the state-of-the-art methods in scenarios where there is a high-level of inconsistency between the source and target domains — the most common scenario in real-world applications.