Fabiano Muniz Belém

Associative tag recommendation exploiting multiple textual features

This work addresses the task of recommending relevant tags to a target object by jointly exploiting three dimensions of the problem: (i) term co-occurrence with tags pre-assigned to the target object, (ii) terms extracted from multiple textual features, and (iii) several metrics of tag relevance. In particular, we propose several new heuristic methods, which extend state-of-the-art strategies by including new metrics that try to capture how accurately a candidate term describes the objects content. We also exploit two learning-to-rank (L2R) techniques, namely RankSVM and Genetic Programming, for the task of generating ranking functions that combine multiple metrics to accurately estimate the relevance of a tag to a given object. We evaluate all proposed methods in various scenarios for three popular Web 2.0 applications, namely, LastFM, YouTube and YahooVideo. We found that our new heuristics greatly outperform the methods on which they are based, producing gains in precision of up to 181%, as well as another state-of-the-art technique, with improvements in precision of up to 40% over the best baseline in any scenario. Further improvements can also be achieved with the new L2R strategies, which have the additional advantage of being quite flexible and extensible to exploit other aspects of the tag recommendation problem.

Assessing the quality of textual features in social media

Social media is increasingly becoming a significant fraction of the content retrieved daily by Web users. However, the potential lack of quality of user generated content poses a challenge to information retrieval services, which rely mostly on textual features generated by users (particularly tags) commonly associated with the multimedia objects. This paper presents what, to the best of our knowledge, is currently the most comprehensive study of the relative quality of textual features in social media. We analyze four different features, namely, title, tags, description and comments posted by users, in four popular applications, namely, YouTube, Yahoo! Video, LastFM and CiteULike. Our study is based on an extensive characterization of data crawled from the four applications with respect to usage, amount and semantics of content, descriptive and discriminative power as well as content and information diversity across features. It also includes a series of object classification and tag recommendation experiments as case studies of two important information retrieval tasks, aiming at analyzing how these tasks are affected by the quality of the textual features. Classification and recommendation effectiveness is analyzed in light of our characterization results. Our findings provide valuable insights for future research and design of Web 2.0 applications and services.

Demand-driven tag recommendation

Collaborative tagging allows users to assign arbitrary keywords (or tags) describing the content of objects, which facilitates navigation and improves searching without dependence on pre-configured categories. In large-scale tag-based systems, tag recommendation services can assist a user in the assignment of tags to objects and help consolidate the vocabulary of tags across users. A promising approach for tag recommendation is to exploit the co-occurrence of tags. However, these methods are challenged by the huge size of the tag vocabulary, either because (1) the computational complexity may increase exponentially with the number of tags or (2) the score associated with each tag may become distorted since different tags may operate in different scales and the scores are not directly comparable. In this paper we propose a novel method that recommends tags on a demand-driven basis according to an initial set of tags applied to an object. It reduces the space of possible solutions, so that its complexity increases polynomially with the size of the tag vocabulary. Further, the score of each tag is calibrated using an entropy minimization approach which corrects possible distortions and provides more precise recommendations. We conducted a systematic evaluation of the proposed method using three types of media: audio, bookmarks and video. The experimental results show that the proposed method is fast and boosts recommendation quality on different experimental scenarios. For instance, in the case of a popular audio site it provides improvements in precision (p@5) ranging from 6.4% to 46.7% (depending on the number of tags given as input), outperforming a recently proposed co-occurrence based tag recommendation method.

Evidence of quality of textual features on the web 2.0

The growth of popularity of Web 2.0 applications greatly increased the amount of social media content available on the Internet. However, the unsupervised, user-oriented nature of this source of information, and thus, its potential lack of quality, have posed a challenge to information retrieval (IR) services. Previous work focuses mostly only on tags, although a consensus about its effectiveness as supporting information for IR services has not yet been reached. Moreover, other textual features of the Web 2.0 are generally overseen by previous research. In this context, this work aims at assessing the relative quality of distinct textual features available on the Web 2.0. Towards this goal, we analyzed four features (title, tags, description and comments) in four popular applications (CiteULike, Last.FM, Yahoo! Video, and Youtube). Firstly, we characterized data from these applications in order to extract evidence of quality of each feature with respect to usage, amount of content, descriptive and discriminative power as well as of content diversity across features. Afterwards, a series of classification experiments were conducted as a case study for quality evaluation. Characterization and classification results indicate that: 1) when considered separately, tags is the most promising feature, achieving the best classification results, although its absence in a non-negligible fraction of objects may affect its potential use; and 2) each feature may bring different pieces of information, and combining their contents can improve classification.

Personalized and object-centered tag recommendation methods for Web 2.0 applications

Abstract Several Web 2.0 applications allow users to assign keywords (or tags) to provide better organization and description of the shared content. Tag recommendation methods may assist users in this task, improving the quality of the available information and, thus, the effectiveness of various tag-based information retrieval services, such as searching, content recommendation and classification. This work addresses the tag recommendation problem from two perspectives. The first perspective, centered at the object, aims at suggesting relevant tags to a target object, jointly exploiting the following three dimensions: (i) tag co-occurrences, (ii) terms extracted from multiple textual features (e.g., title, description), and (iii) various metrics to estimate tag relevance. The second perspective, centered at both object and user, aims at performing personalized tag recommendation to a target object-user pair, exploiting, in addition to the three aforementioned dimensions, a metric that captures user interests. In particular, we propose new heuristic methods that extend state-of-the-art strategies by including new metrics that estimate how accurately a candidate tag describes the target object. We also exploit three learning-to-rank (L2R) based techniques, namely, RankSVM, Genetic Programming (GP) and Random Forest (RF), for generating ranking functions that exploit multiple metrics as attributes to estimate the relevance of a tag to a given object or object-user pair. We evaluate the proposed methods using data from four popular Web 2.0 applications, namely, Bibsonomy, LastFM, YouTube and YahooVideo. Our new heuristics for object-centered tag recommendation provide improvements in precision over the best state-of-the-art alternative of 12% on average (up to 20% in any single dataset), while our new heuristics for personalized tag recommendation produce average gains in precision of 121% over the baseline. Similar performance gains are also achieved in terms of other metrics, notably recall, Normalized Discounted Cumulative Gain (NDCG) and Mean-Reciprocal Rank (MRR). Further improvements, for both object-centered (up to 23% in precision) and personalized tag recommendation (up to 13% in precision), can also be achieved with our new L2R-based strategies, which are flexible and can be easily extended to exploit other aspects of the tag recommendation problem. Finally, we also quantify the benefits of personalized tag recommendation to provide better descriptions of the target object when compared to object-centered recommendation by focusing only on the relevance of the suggested tags to the object. We find that our best personalized method outperforms the best object-centered strategy, with average gains in precision of 10%.

On the Quality of Information for Web 2.0 Services

Most Web 2.0 applications let users associate textual information with multimedia content. Despite each applications lack of editorial control, these textual features are still the primary source of information for many relevant services such as search. Previous efforts in assessing the quality of these features target, mostly, single applications, and mainly focus on tags, thus neglecting the potential of other features. The current study assesses and compares the quality of four textual features (title, tags, description, and comments) for supporting information services using data from YouTube, YahooVideo, and LastFM.

Topic diversity in tag recommendation

Tag recommendation approaches have historically focused on maximizing the relevance of the recommended tags for a given object, such as a movie or a song. Nevertheless, different users may be interested in the same object for different reasons---for instance, the Star Wars movies may appeal to both adventure as well as to fantasy movie fans. In this situation, a sensible strategy is to provide a user with diverse recommendations of how to tag the object. In this paper, we address the problem of recommending relevant and diverse tags as a ranking problem. In particular, we propose a novel tag recommendation approach that explicitly takes into account the possible topics (e.g., categories) underlying an object in order to promote tags with high coverage and low redundancy with respect to these topics. We thoroughly evaluate our proposed approach using data collected from two popular Web 2.0 applications, namely, LastFM and MovieLens. Our experimental results attest the effectiveness of our approach at promoting more relevant and diverse tags in contrast to state-of-the-art relevance-based methods as well as a recently proposed method that takes both relevance and diversity into account.

Exploiting novelty and diversity in tag recommendation

The design and evaluation of tag recommendation methods have focused only on relevance. However, other aspects such as novelty and diversity may be as important to evaluate the usefulness of the recommendations. In this work, we define these two aspects in the context of tag recommendation and propose a novel recommendation strategy that considers them jointly with relevance. This strategy extends a state-of-the-art method based on Genetic Programming to include novelty and diversity metrics both as attributes and as part of the objective function. We evaluate the proposed strategy using data collected from 3 popular Web 2.0 applications: LastFM, YouTube and YahooVideo. Our experiments show that our strategy outperforms the state-of-the-art alternative in terms of novelty and diversity, without harming relevance.

Automatic query expansion based on tag recommendation

We here propose a new method for expanding entity related queries that automatically filters, weights and ranks candidate expasion terms extracted from Wikipedia articles related to the original query. Our method is based on state-of-the-art tag recommendation methods that exploit heuristic metrics to estimate the descriptive capacity of a given term. Originally proposed for the context of tags, we here apply these recommendation methods to weight and rank terms extracted from multiple fields of Wikipedia articles according to their relevance for the article. We evaluate our method comparing it against three state-of-the-art baselines in three collections. Our results indicate that our method outperforms all baselines in all collections, with relative gains in MAP of up to 14% against the best ones.

Exploiting relevance, novelty and diversity in tag recommendation

Tag recommendation methods have mostly focused on maximizing relevance, but other aspects may be as important for recommendation usefulness. We here define novelty and diversity for tag recommendation, and propose two new recommendation strategies that consider these aspects jointly with relevance. We evaluate the proposed strategies using real datasets from 3 popular Web 2.0 applications, achieving gains over the state-of-the-art of up to 21% in relevance, 45% in novelty and 2.5\% in diversity.