| Challenge: | Existing models that only use lexical features and ignore past user interactions in online conversations are inadequate to identify and engage in online discussions. |
| Approach: | They propose a framework that automatically recommends conversations based on user's prior conversation behaviors by exploring deep semantic features that measure how a user’s preferences match an ongoing conversation’s context. |
| Outcome: | The proposed model outperforms state-of-the-art models on two large-scale datasets from Twitter and Reddit showing that it incorporates deep semantic features that measure how a user’s preferences match an ongoing conversation’s context. |
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Dynamic Online Conversation Recommendation (2020.acl-main)
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| Challenge: | Existing models that assume static user interests are unable to capture the temporal aspects of user interactions and interest changes over time. |
| Approach: | They propose a neural architecture to exploit changes of user interactions and interests over time to predict which discussions they are likely to enter. |
| Outcome: | The proposed model outperforms state-of-the-art models that assume static user interests and handle future conversations that are unseen during training time. |
Microblog Conversation Recommendation via Joint Modeling of Topics and Discourse (N18-1)
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| Challenge: | Existing methods for recommendation focus on content of individual posts, but we exploit both context and user content and behavior preferences. |
| Approach: | They propose a method that captures conversational context and user content and behavior preferences. |
| Outcome: | The proposed method outperforms methods that only model content without considering discourse on two Twitter datasets. |
A Dynamic Speaker Model for Conversational Interactions (N19-1)
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| Challenge: | a neural model for characterizing individual differences in speakers is shown to be useful in human-computer interaction and dialog act prediction. |
| Approach: | They propose a neural model for learning a dynamically updated speaker embedding in a conversational context. |
| Outcome: | The proposed model is used for content ranking and dialog act prediction in human-human conversations. |
The Engage Corpus: A Social Media Dataset for Text-Based Recommender Systems (2022.lrec-1)
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| Challenge: | Existing studies have examined the impact of recommendation algorithms on how users discover and join online groups, but there are few standardized datasets for generating such models. |
| Approach: | They propose to use Reddit to build a dataset that can be used to build models of user engagement with online groups. |
| Outcome: | The proposed model is based on the behavior of subreddits banned in June 2020 as part of Reddit's efforts to stop the dissemination of hate speech. |
Joint Effects of Context and User History for Predicting Online Conversation Re-entries (P19-1)
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| Challenge: | Existing methods for predicting online conversation re-entry focus on modeling engagement patterns in ongoing conversations or ignoring the rich information in users' previous chatting history. |
| Approach: | They propose a neural framework with three main layers to model the conversation context and user history and their interactions with Twitter and Reddit to predict whether a user will return to a conversation they once participated in. |
| Outcome: | The proposed framework outperforms the state-of-the-art methods on two large-scale Twitter and Reddit conversations, and achieves an F1 score of 61.1 on Twitter conversations. |
RecInDial: A Unified Framework for Conversational Recommendation with Pretrained Language Models (2022.aacl-main)
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| Challenge: | Existing generative methods to recommend items are shallowly integrated into the model training and have poor chit-chat ability. |
| Approach: | They propose a framework that integrates recommendation into the dialog generation by introducing a vocabulary pointer. |
| Outcome: | The proposed framework outperforms the state-of-the-art models on a benchmark dataset. |
Improving Neural Conversational Models with Entropy-Based Data Filtering (P19-1)
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| Challenge: | Current neural network-based conversational models lack diversity and generate boring responses to open-ended utterances. |
| Approach: | They propose an unsupervised method of filtering dialog datasets by removing generic utterances from training data using an entropy-based approach that does not require human supervision. |
| Outcome: | The proposed method improves dialog quality as chatbots learn to output more diverse responses to open-ended utterances. |
You Sound Like Someone Who Watches Drama Movies: Towards Predicting Movie Preferences from Conversational Interactions (2021.naacl-main)
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| Challenge: | Existing methods for conversational recommendation include collaborative filtering, content-based filtering and user reviews. |
| Approach: | They propose to map a conversational user to most similar external reviewers, whose preferences are known, and adapt collaborative filtering techniques to estimate the current user’s preferences for new movies. |
| Outcome: | The proposed method can improve the accuracy of predicting user ratings for new movies by exploiting conversation content and external data. |
Predicting Helpful Posts in Open-Ended Discussion Forums: A Neural Architecture (N19-1)
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| Challenge: | Unlike Community Question Answering, where questions are mostly factoid based, forum threads are often open-ended and contain repetitive or irrelevant posts. |
| Approach: | They propose a recurrent neural network-based architecture to model the relevance of a post regarding the original post starting the thread and the novelty it brings to the discussion. |
| Outcome: | The proposed model outperforms the state-of-the-art models for text classification on different types of online forum datasets. |
User Memory Reasoning for Conversational Recommendation (2020.coling-main)
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| Challenge: | Existing systems that update user preferences via asking relevant questions are unable to dynamically maintain and reason over their knowledge for current (and possibly future) recommendations. |
| Approach: | They propose a new memory graph (MG) -> Conversational Recommendation parallel corpus with 7K+ human-to-human role-playing dialogs and a graph-based reasoning model that updates MG from unstructured utterances and predicts optimal dialog policies based on updated MG. |
| Outcome: | The proposed model is based on a large-scale user memory bootstrapped from real-world user scenarios and can be easily updated from unstructured utterances. |