Hierarchical Expert Recommendation on Community Question Answering Platforms

The community question answering (CQA) platforms, such as Stack Overflow, have become the primary source of answers to most questions in various topics. CQA platforms offer an opportunity for sharing and acquiring knowledge at a low cost, where users, many of whom are experts in a specific topic, can potentially provide high-quality solutions to a given question. Many recommendation methods have been proposed to match questions to potential good answerers. However, most existing methods have focused on modelling the user-question interaction — a user might answer multiple questions and a question might be answered by multiple users — using simple collaborative filtering approaches, overlooking the rich information in the question’s title and body when modelling the users’ expertise. This project fills the research gap by thoroughly examining machine learning and deep learning approaches that can be applied to the expert recommendation problem. It proposes a Hierarchical Expert Recommendation (HER) model, a deep learning recommender system that recommends experts to answer a given question in the CQA platform. Although choosing a deep learning over a machine learning solution for this problem can be justified considering the degree of complexity of the available datasets, we assess performance of each family of methods and evaluate the trade-off between them to pick the perfect fit for our problem. We analyzed various machine learning algorithms to determine their performances in the expert recommendation problem, which narrows down the potential ways for tackling this problem using traditional recommendation methods. Furthermore, we investigate the recommendation models based on matrix factorization to establish the baselines for our proposed model and shed light on the weaknesses and strengths of matrix- based solutions, which shape our final deep learning model. In the last section, we introduce the Hierarchical Expert Recommendation System (HER) that utilizes hierarchical attention-based neural networks to rep- resent the questions better and ultimately model the users’ expertise through user-question interactions. We conducted extensive experiments on a large real-world Stack Overflow dataset and benchmarked HER against the state-of-the-art baselines. The results from our extensive experiments show that HER outperforms the state-of-the-art baselines in recommending experts to answer questions in Stack Overflow

Understanding and exploiting user intent in community question answering

A number of Community Question Answering (CQA) services have emerged and proliferated in the last decade. Typical examples include Yahoo! Answers, WikiAnswers, and also domain-specific forums like StackOverflow. These services help users obtain information from a community - a user can post his or her questions which may then be answered by other users. Such a paradigm of information seeking is particularly appealing when the question cannot be answered directly by Web search engines due to the unavailability of relevant online content. However, question submitted to a CQA service are often colloquial and ambiguous. An accurate understanding of the intent behind a question is important for satisfying the user's information need more effectively and efficiently. In this thesis, we analyse the intent of each question in CQA by classifying it into five dimensions, namely: subjectivity, locality, navigationality, procedurality, and causality. By making use of advanced machine learning techniques, such as Co-Training and PU-Learning, we are able to attain consistent and significant classification improvements over the state-of-the-art in this area. In addition to the textual features, a variety of metadata features (such as the category where the question was posted to) are used to model a user's intent, which in turn help the CQA service to perform better in finding similar questions, identifying relevant answers, and recommending the most relevant answerers. We validate the usefulness of user intent in two different CQA tasks. Our first application is question retrieval, where we present a hybrid approach which blends several language modelling techniques, namely, the classic (query-likelihood) language model, the state-of-the-art translation-based language model, and our proposed intent-based language model. Our second application is answer validation, where we present a two-stage model which first ranks similar questions by using our proposed hybrid approach, and then validates whether the answer of the top candidate can be served as an answer to a new question by leveraging sentiment analysis, query quality assessment, and search lists validation

Simplifying Sparse Expert Recommendation by Revisiting Graph Diffusion

Community Question Answering (CQA) websites have become valuable knowledge repositories where individuals exchange information by asking and answering questions. With an ever-increasing number of questions and high migration of users in and out of communities, a key challenge is to design effective strategies for recommending experts for new questions. In this paper, we propose a simple graph-diffusion expert recommendation model for CQA, that can outperform state-of-the art deep learning representatives and collaborative models. Our proposed method learns users' expertise in the context of both semantic and temporal information to capture their changing interest and activity levels with time. Experiments on five real-world datasets from the Stack Exchange network demonstrate that our approach outperforms competitive baseline methods. Further, experiments on cold-start users (users with a limited historical record) show our model achieves an average of ~ 30% performance gain compared to the best baseline method

Neural Methods for Answer Passage Retrieval over Sparse Collections

Recent advances in machine learning have allowed information retrieval (IR) techniques to advance beyond the stage of handcrafting domain specific features. Specifically, deep neural models incorporate varying levels of features to learn whether a document answers the information need of a query. However, these neural models rely on a large number of parameters to successfully learn a relation between a query and a relevant document. This reliance on a large number of parameters, combined with the current methods of optimization relying on small updates necessitates numerous samples to allow the neural model to converge on an effective relevance function. This presents a significant obstacle in the realm of IR as relevance judgements are often sparse or noisy and combined with a large class imbalance. This is especially true for short text retrieval where there is often only one relevant passage. This problem is exacerbated when training these artificial neural networks, as excessive negative sampling can result in poor performance. Thus, we propose approaching this task through multiple avenues and examining their effectiveness on a non-factoid question answering (QA) task.We first propose learning local embeddings specific to the relevance information of the collection to improve performance of an upstream neural model. In doing so, we find significantly improved results over standard pre-trained embeddings, despite only developing the embeddings on a small collection which would not be sufficient for a full language model. Leveraging this local representation, and inspired by recent work in machine translation, we introduce a hybrid embedding based model that incorporates both pre-trained embeddings while dynamically constructing local representations from character embeddings. The hybrid approach relies on pre-trained embeddings to achieve an effective retrieval model, and continually adjusts its character level abstraction to fit a local representation.We next approach methods to adapt neural models to multiple IR collections, therefore reducing the collection specific training required and alleviating the need to retrain a neural model\u27s parameters for a new subdomain of a collection. First, we propose an adversarial retrieval model which achieves state-of-the-art performance on out of subdomain queries while maintaining in-domain performance. Second, we establish an informed negative sampling approach using a reinforcement learning agent. The agent is trained to directly maximize the performance of a neural IR model using a predefined IR metric by choosing which ranking function from which to sample negative documents. This policy based sampling allows the neural model to be exposed to more of a collection and results in a more consistent neural retrieval model over multiple training instances. Lastly, we move towards a universal retrieval function. We initially introduce a probe-based inspection of neural relevance models through the lens of standard natural language processing tasks and establish that while seemingly similar QA collections require the same basic abstract information, the final layers that determine relevance differ significantly. We then introduce Universal Retrieval Functions, a method to incorporate new collections using a library of previously trained linear relevance models and a common neural representation