Dynamic Online Conversation Recommendation (2020)

Abstract: Trending topics in social media content evolve over time, and it is therefore crucial to understand social media users and their interpersonal communications in a dynamic manner. Here we study dynamic online conversation recommendation, to help users engage in conversations that satisfy their evolving interests. While most prior work assumes static user interests, our model is able to capture the temporal aspects of user interests, and further handle future conversations that are unseen during training time. Concretely, we propose a neural architecture to exploit changes of user interactions and interests over time, to predict which discussions they are likely to enter. We conduct experiments on large-scale collections of Reddit conversations, and results on three subreddits show that our model significantly outperforms state-of-the-art models that make a static assumption of user interests. We further evaluate on handling “cold start”, and observe consistently better performance by our model when considering various degrees of sparsity of user’s chatting history and conversation contexts. Lastly, analyses on our model outputs indicate user interest change, explaining the advantage and efficacy of our approach.

## introduction online social media platforms are popular outlets for individuals to exchange viewpoints and discuss topics they are interested in. however, the huge volume of online conversations produced daily hinders people's capability of finding the information they are interested in. as a result, there is pressing demand for developing a conversation recommendation engine that tracks ongoing conversations and recommends suitable ones to users. viewing the deluge of information streaming through social media, it is not hard to envision that @xcite in the uk they can request your encryption keys… …… [t2] … i doubt we are seeing the banning of encryption… in the ease of the authorities to go rummaging about your privacy. to overcome this predicament, we explore dynamic conversation recommendation, which can model the change of user interests over time (henceforth user interest dynamics). to illustrate such change, figure 1 shows multiple conversation turns posted by user u in four reddit discussion snippets: c 1 to c 4 in the chronological order. as can be seen, u used to like discussing internet security, indicated by "encryption", "privacy", and "surveillance" in c 1 and c 2 . after a period of time, u 's interests changed to a different topic, operating system, as "ksplice", "oracle", and "ubuntu" were later mentioned in c 3 and c 4 . we design the model to capture user interests from both what they said in the past, and how they interacted with each other in the conversation structure. we first capture time-variant representations from user chatting history, where we assume user interests may change over time and therefore apply a gated recurrent unit (gru) @xcite to model time dependency. user interactions in the conversation context are then explored with both bidirectional gated recurrent unit (bi-gru) @xcite for conversation turns' chronological order and graph convolutional networks (gcn) @xcite for in-reply-to relations. both representations are learned to encode how participants formed the conversation structure, including what they said and whom they replied to. next, we propose a user-aware attention to convey the user interest dynamics, which is further put over an interactionencoded conversation to measure whether its ongoing contexts fit a user's current interests. finally, we predict how likely a user will engage in a conversation, as a result of recommendation. to the best of our knowledge, we are the first to study dynamic online conversation recommendation and to explore the effects of user interests change over time learned from both chatting content and interaction behavior. for this reason, we are capable of recommending future conversations based on users' interests at the time. for experiments foot_0 , we collect reddit conversations from three subreddits -"technology", "todayilearned", and "funny", each exhibiting different data statistics, discussion topics, and language styles. an absolute date is used to separate training data (before the date) from test and validation data (after the date). in this way, most conversations in the test and validation parts are new conversations that have not been counted before. this presents a more realistic setup than previous studies @xcite , which let training data contain partial context for any conversations to allow the possibility of predicting users' future engagement for recommendation. experimental results in main comparisons show that our model significantly outperforms all previous methods that ignore the change of user interests or interactions within contexts. for example, we achieve 0.375 map in discussions of "technology", compared with 0.222 yielded by our previous stateof-the-art model @xcite . further study shows that we consistently perform better both in conversation cold start and with varying degrees of sparsity of user history and conversation contexts. lastly, to provide more insights into user interest dynamics, we inspect our model outputs and find that users indeed tend to engage in different types of conversations at different times, confirming the usefulness of tracking user preferences in real-time for conversation recommendation. ## our dynamic conversation recommendation model this section describes our dynamic conversation recommendation model, whose overall structure is shown in figure 2 . in the following, we will first introduce how we model the user interest dynamics with their chatting history in section 3.1, followed by the description of conversation modeling in section 3.2. afterwards, section 3.3 will present how we produce final recommendation outputs. objective function and learning procedures will be finally presented in section 3.4. ## experimental results we first report the main comparison results in section 5.1, and then discuss the effects of sparsity and cold start in section 5.2. lastly, in section 5.3, we probe into our model outputs to provide more insights into user interest dynamics. ## conclusion this paper presents a dynamic conversation recommendation model learned from the change of content and user interactions over time. experimental results on three new datasets from reddit show that our model significantly outperforms all comparisons, including previous state of the arts. further discussion demonstrates the robustness of our model against history sparsity and cold start. we also analyze our model's outputs to get more insights into user interest dynamics.