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1. Using Topic Modeling for Code Discovery in Large Scale Text Data

   https://doi.org/https://doi.org/10.1007/978-3-030-67788-6_2  

   Cai, Zhiqiang; Siebert-Evenstone, Amanda; Eagan, Brendan R.; Shaffer, David W. (January 2021, Advances in Quantitative Ethnography: Second International Conference, ICQE 2020, Malibu, CA, USA, February 1-3, 2021, Proceedings)

   Ruis, Andrew; Lee, Seung B. (Ed.)

   When text datasets are very large, manually coding line by line becomes impractical. As a result, researchers sometimes try to use machine learning algorithms to automatically code text data. One of the most popular algorithms is topic modeling. For a given text dataset, a topic model provides probability distributions of words for a set of “topics” in the data, which researchers then use to interpret meaning of the topics. A topic model also gives each document in the dataset a score for each topic, which can be used as a non-binary coding for what proportion of a topic is in the document. Unfortunately, it is often difficult to interpret what the topics mean in a defensible way, or to validate document topic proportion scores as meaningful codes. In this study, we examine how keywords from codes developed by human experts were distributed in topics generated from topic modeling. The results show that (1) top keywords of a single topic often contain words from multiple human-generated codes; and conversely, (2) words from human-generated codes appear as high-probability keywords in multiple topic. These results explain why directly using topics from topic models as codes is problematic. However, they also imply that topic modeling makes it possible for researchers to discover codes from short word lists. 

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2. XPL-CF: Explainable Embeddings for Feature-based Collaborative Filtering

   https://doi.org/10.1145/3459637.3482221  

   Almutairi, Faisal M.; Sidiropoulos, Nicholas D.; Yang, Bo (October 2021, 30th ACM International Conference on Information and Knowledge Management (CIKM))

   Collaborative filtering (CF) methods are making an impact on our daily lives in a wide range of applications, including recommender systems and personalization. Latent factor methods, e.g., matrix factorization (MF), have been the state-of-the-art in CF, however they lack interpretability and do not provide a straightforward explanation for their predictions. Explainability is gaining momentum in recommender systems for accountability, and because a good explanation can swing an undecided user. Most recent explainable recommendation methods require auxiliary data such as review text or item content on top of item ratings. In this paper, we address the case where no additional data are available and propose augmenting the classical MF framework for CF with a prior that encodes each user's embedding as a sparse linear combination of item embeddings, and vice versa for each item embedding. Our XPL-CF approach automatically reveals these user-item relationships, which underpin the latent factors and explain how the resulting recommendations are formed. We showcase the effectiveness of XPL-CF on real data from various application domains. We also evaluate the explainability of the user-item relationship obtained from XPL-CF through numeric evaluation and case study examples. 

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3. Toward Optimal Selection of Information Retrieval Models for Software Engineering Tasks

   Rahman, Md Masudur; Chakraborty, Saikat; Kaiser, Gail; Ray, Baishakhi (September 2019, 19th IEEE International Working Conference on Source Code Analysis and Manipulation (SCAM))

   Information Retrieval (IR) plays a pivotal role indiverse Software Engineering (SE) tasks, e.g., bug localization and triaging, bug report routing, code retrieval, requirements analysis, etc. SE tasks operate on diverse types of documents including code, text, stack-traces, and structured, semi-structured and unstructured meta-data that often contain specialized vocabularies. As the performance of any IR-based tool critically depends on the underlying document types, and given the diversity of SE corpora, it is essential to understand which models work best for which types of SE documents and tasks.We empirically investigate the interaction between IR models and document types for two representative SE tasks (bug localization and relevant project search), carefully chosen as they require a diverse set of SE artifacts (mixtures of code and text),and confirm that the models’ performance varies significantly with mix of document types. Leveraging this insight, we propose a generalized framework, SRCH, to automatically select the most favorable IR model(s) for a given SE task. We evaluate SRCH w.r.t. these two tasks and confirm its effectiveness. Our preliminary user study shows that SRCH’s intelligent adaption of the IR model(s) to the task at hand not only improves precision and recall for SE tasks but may also improve users’ satisfaction. 

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4. BATS: A Spectral Biclustering Approach to Single Document Topic Modeling and Segmentation

   https://doi.org/10.1145/3468268  

   Wu, Qiong; Hare, Adam; Wang, Sirui; Tu, Yuwei; Liu, Zhenming; Brinton, Christopher G.; Li, Yanhua (October 2021, ACM Transactions on Intelligent Systems and Technology)

   Existing topic modeling and text segmentation methodologies generally require large datasets for training, limiting their capabilities when only small collections of text are available. In this work, we reexamine the inter-related problems of “topic identification” and “text segmentation” for sparse document learning, when there is a single new text of interest. In developing a methodology to handle single documents, we face two major challenges. First is sparse information : with access to only one document, we cannot train traditional topic models or deep learning algorithms. Second is significant noise : a considerable portion of words in any single document will produce only noise and not help discern topics or segments. To tackle these issues, we design an unsupervised, computationally efficient methodology called Biclustering Approach to Topic modeling and Segmentation (BATS). BATS leverages three key ideas to simultaneously identify topics and segment text: (i) a new mechanism that uses word order information to reduce sample complexity, (ii) a statistically sound graph-based biclustering technique that identifies latent structures of words and sentences, and (iii) a collection of effective heuristics that remove noise words and award important words to further improve performance. Experiments on six datasets show that our approach outperforms several state-of-the-art baselines when considering topic coherence, topic diversity, segmentation, and runtime comparison metrics. 

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5. JNET: Learning User Representations via Joint Network Embedding and Topic Embedding

   https://doi.org/10.1145/3336191.3371770  

   Gong, Lin; Lin, Lu; Song, Weihao; Wang, Hongning (January 2020, Proceedings of the 13th International Conference on Web Search and Data Mining)

   User representation learning is vital to capture diverse user preferences, while it is also challenging as user intents are latent and scattered among complex and different modalities of user-generated data, thus, not directly measurable. Inspired by the concept of user schema in social psychology, we take a new perspective to perform user representation learning by constructing a shared latent space to capture the dependency among different modalities of user-generated data. Both users and topics are embedded to the same space to encode users' social connections and text content, to facilitate joint modeling of different modalities, via a probabilistic generative framework. We evaluated the proposed solution on large collections of Yelp reviews and StackOverflow discussion posts, with their associated network structures. The proposed model outperformed several state-of-the-art topic modeling based user models with better predictive power in unseen documents, and state-of-the-art network embedding based user models with improved link prediction quality in unseen nodes. The learnt user representations are also proved to be useful in content recommendation, e.g., expert finding in StackOverflow. 

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