User Personalized Satisfaction Prediction via Multiple Instance Deep Learning

Abstract

Community-based question answering(CQA) services have arisen as a popular knowledge sharing pattern for netizens. With abundant interactions among users, individuals are capable of obtaining satisfactory information. However, it is not effective for users to attain answers within minutes. Users have to check the progress over time until the satisfying answers submitted. We address this problem as a user personalized satisfaction prediction task. Existing methods usually exploit manual feature selection. It is not desirable as it requires careful design and is labor intensive. In this paper, we settle this issue by developing a new multiple instance deep learning framework. Specifically, in our settings, each question follows a weakly supervised learning (multiple instance learning) assumption, where its obtained answers can be regarded as instance sets and we define the question resolved with at least one satisfactory answer. We thus design an efficient framework exploiting multiple instance learning property with deep learning tactic to model the question-answer pairs relevance and rank the asker's satisfaction possibility. Extensive experiments on large-scale datasets from Stack Exchange demonstrate the feasibility of our proposed framework in predicting askers personalized satisfaction. Our framework can be extended to numerous applications such as UI satisfaction Prediction, multi-armed bandit problem, expert finding and so on.

Towards Deep Learning Prospects: Insights for Social Media Analytics

“…Chen et al [45] proposed an approach to predict users' personalized satisfaction using multiple instance DL frameworks. The authors presented a novel model, Multiple Instance Deep Learning (MIDL) framework to predict personalized user satisfaction.…”

“…There a number of ways to evaluate the performance of DL methods. The recall, precision, and F1-score are a noteworthy performance measure for prediction, ranking and classification-based tasks [38], [45]. The F1-score stabilizes the recall and precision as:…”

Trajectory-based social circle inference

“…An illustration is provided in Figure 2. We note that: (a) LSTM has been widely studied and has demonstrated a superb performance in dealing with long sequences [20]; and (b) some alternative variant RNNs can also be adopted here, such as GRU [9,16] and bidirectional RNNs [7,25]. However, these are not the core parts of the current work.…”

Discourse Marker Augmented Network with Reinforcement Learning for Natural Language Inference

“…Large and complicated networks have been successful in many natural language processing tasks (Zhu et al, 2017;Chen et al, 2017e;Pan et al, 2017a). Recently, Bowman et al (2015) released Stanford Natural language Inference (SNLI) dataset, which is a high-quality and large-scale benchmark, thus inspired many significant works (Bowman et al, 2016;Mou et al, 2016;Vendrov et al, 2016;Conneau et al, 2017;Gong et al, 2018;McCann et al, 2017;Chen et al, 2017b;Choi et al, 2017;Tay et al, 2017).…”