CompareLDA: A topic model for document comparison

A number of real-world applications require comparison of entities based on their textual representations. In this work, we develop a topic model supervised by pairwise comparisons of documents. Such a model seeks to yield topics that help to differentiate entities along some dimension of interest, which may vary from one application to another. While previous supervised topic models consider document labels in an independent and pointwise manner, our proposed Comparative Latent Dirichlet Allocation (CompareLDA) learns predictive topic distributions that comply with the pairwise comparison observations. To fit the model, we derive a maximum likelihood estimation method via augmented variational approximation algorithm. Evaluation on several public datasets underscores the strengths of CompareLDA in modelling document comparisons

VistaNet: Visual Aspect Attention Network for multimodal sentiment analysis

Detecting the sentiment expressed by a document is a key task for many applications, e.g., modeling user preferences, monitoring consumer behaviors, assessing product quality. Traditionally, the sentiment analysis task primarily relies on textual content. Fueled by the rise of mobile phones that are often the only cameras on hand, documents on the Web (e.g., reviews, blog posts, tweets) are increasingly multimodal in nature, with photos in addition to textual content. A question arises whether the visual component could be useful for sentiment analysis as well. In this work, we propose Visual Aspect Attention Network or VistaNet, leveraging both textual and visual components. We observe that in many cases, with respect to sentiment detection, images play a supporting role to text, highlighting the salient aspects of an entity, rather than expressing sentiments independently of the text. Therefore, instead of using visual information as features, VistaNet relies on visual information as alignment for pointing out the important sentences of a document using attention. Experiments on restaurant reviews showcase the effectiveness of visual aspect attention, vis-à-vis visual features or textual attention

Learning multiple maps from conditional ordinal triplets

Singapore National Research Foundatio

Mining Revenue-Maximizing Bundling Configuration

With greater prevalence of social media, there is an increas-ing amount of user-generated data revealing consumer pref-erences for various products and services. Businesses seek to harness this wealth of data to improve their marketing strategies. Bundling, or selling two or more items for one price is a highly-practiced marketing strategy. In this pa-per, we address the bundle configuration problem from the data-driven perspective. Given a set of items in a seller’s in-ventory, we seek to determine which items should belong to which bundle so as to maximize the total revenue, by mining consumer preferences data. We show that this problem is NP-hard when bundles are allowed to contain more than two items. Therefore, we describe an optimal solution for bundle sizes up to two items, and propose two heuristic solutions for bundles of any larger size. We investigate the effective-ness and the efficiency of the proposed algorithms through experimentations on real-life rating-based preferences data

Correlation-sensitive next-basket recommendation

Ministry of Education, Singapore under its Academic Research Funding Tier