Product search forms an indispensable component of any e-commerce service, and helps customers find products of their interest from a large catalog on these websites. When products that are irrelevant to the search query are surfaced, it leads to a poor customer experience, thus reducing user trust and increasing the likelihood of churn. While identifying and removing such results from product search is crucial, doing so is a burdensome task that requires large amounts of human annotated data to train accurate models. This problem is exacerbated when products are cross-listed across countries that speak multiple languages, and customers specify queries in multiple languages and from different cultural contexts. In this work, we propose a novel multi-lingual multi-task learning framework, to jointly train product search models on multiple languages, with limited amount of training data from each language. By aligning the query and product representations from different languages into a language-independent vector space of queries and products, respectively, the proposed model improves the performance over baseline search models in any given language. We evaluate the performance of our model on real data collected from a leading e-commerce service. Our experimental evaluation demonstrates up to 23% relative improvement in the classification F1-score compared to the state-of-the-art baseline models.

The recent increase in online user generated content (UGC) has led to the availability of a large number of posts about products and services. Often, these posts contain complaints that the consumers purchasing the products and services have. However, discovering and summarizing product defects and the related knowledge from large quantities of user posts is a difficult task. Traditional aspect opinion mining models, that aim to discover the product aspects and their corresponding opinions, are not sufficient to discover the product defect information from the user posts. In this paper, we propose the Product Defect Latent Dirichlet Allocation model (PDLDA), a probabilistic model that identifies domain-specific knowledge about product issues using interdependent three-dimensional topics: Component, Symptom, and Resolution. A Gibbs sampling based inference method for PDLDA is also introduced. To evaluate our model, we introduce three novel product review datasets. Both qualitative and quantitative evaluations show that the proposed model results in apparent improvement in the quality of discovered product defect information. Our model has the potential to benefit customers, manufacturers, and policy makers, by automatically discovering product defects from online data.

The growth of the Web in recent years has resulted in the development of various online platforms that provide healthcare information services. These platforms contain an enormous amount of information, which could be beneficial for a large number of people. However, navigating through such knowledgebases to answer specific queries of healthcare consumers is a challenging task. A majority of such queries might be non-factoid in nature, and hence, traditional keyword-based retrieval models do not work well for such cases. Furthermore, in many scenarios, it might be desirable to get a short answer that sufficiently answers the query, instead of a long document with only a small amount of useful information. In this paper, we propose a neural network model for ranking documents for question answering in the healthcare domain. The proposed model uses a deep attention mechanism at word, sentence, and document levels, for efficient retrieval for both factoid and non-factoid queries, on documents of varied lengths. Specifically, the word-level cross-attention allows the model to identify words that might be most relevant for a query, and the hierarchical attention at sentence and document levels allows it to do effective retrieval on both long and short documents. We also construct a new large-scale healthcare question-answering dataset, which we use to evaluate our model. Experimental evaluation results against several state-of-the-art baselines show that our model outperforms the existing retrieval techniques.

Online reviews have become an inevitable part of a consumer's decision making process, where the likelihood of purchase not only depends on the product's overall rating, but also on the description of its aspects. Therefore, e-commerce websites such as Amazon and Walmart constantly encourage users to write good quality re- views and categorically summarize different facets of the products. However, despite such attempts, it takes a significant effort to skim through thousands of reviews and look for answers that address the query of consumers. For example, a gamer might be interested in buying a monitor with fast refresh rates and support for Gsync and Freesync technologies, while a photographer might be interested in aspects such as color depth and accuracy. To address these chal- lenges, in this paper, we propose a generative aspect summarization model called APSUM that is capable of providing fine-grained sum- maries of online reviews. To overcome the inherent problem of aspect sparsity, we impose dual constraints: (a) a spike-and-slab prior over the document-topic distribution and (b) a linguistic su- pervision over the word-topic distribution. Using a rigorous set of experiments, we show that the proposed model is capable of out- performing the state-of-the-art aspect summarization model over a variety of datasets and deliver intuitive fine-grained summaries that could simplify the purchase decisions of consumers.

Due to the rapid increase in the number of users owning location-based devices, there is a considerable amount of geo-tagged data available on social media websites, such as Twitter and Facebook. This geo-tagged data can be useful in a variety of ways to extract location-specific information, as well as to comprehend the variation of information across different geographical regions. A lot of techniques have been proposed for extracting location-based information from social media, but none of these techniques aim to utilize an important characteristic of this data, which is the presence of aspects and their opinions, expressed by the users on these platforms. In this paper, we propose Geographic Aspect Opinion model (GASPOP), a probabilistic model that jointly discovers the variation of aspect and opinion, that correspond to different topics across various geographical regions from geo-tagged social media data. It incorporates the syntactic features of text in the generative process to differentiate aspect and opinion words from general background words. The user-based modeling of topics, also enables it to determine the interest distribution of various users. Furthermore, our model can be used to predict the location of different tweets based on their text. We evaluated our model on Twitter data, and our experimental results show that GASPOP can jointly discover latent aspect and opinion words for different topics across latent geographical regions. Moreover, a quantitative analysis of GASPOP using widely used evaluation metrics shows that it outperforms the state-of-the-art methods.