Here, There, and Everywhere: Building a Scaffolding for Children’s Learning Through Recommendations

Reading and literacy are on the decline among children. This is compounded by the fact that children have trouble with the discovery of resources that are appropriate, diverse, and appealing. With technology becoming an evermore presence in children’s lives, tools that can minimize choice overload and ease access to online resources become a must. A powerful but underutilized tool in regards to children that could assist in this situation is a recommender system (RS). We posit that RS could be used to impact children’s learning, using them to not only suggest what children might like but what they need in regards to learning. At the same time, if scoped inappropriately, outcomes from RS could be used to alter children’s outlook. The goal instead is to strive for RS that offer suggestions based off children’s evolving knowledge, preferences, reading level, etc., so that with the proper intervention from an expert-in-the-loop (e.g., parents/teachers) could impact not only children’s educational performance, but help them to reach the goal of learning to learn

Towards a Unified Conversational Recommendation System: Multi-task Learning via Contextualized Knowledge Distillation

In Conversational Recommendation System (CRS), an agent is asked to recommend a set of items to users within natural language conversations. To address the need for both conversational capability and personalized recommendations, prior works have utilized separate recommendation and dialogue modules. However, such approach inevitably results in a discrepancy between recommendation results and generated responses. To bridge the gap, we propose a multi-task learning for a unified CRS, where a single model jointly learns both tasks via Contextualized Knowledge Distillation (ConKD). We introduce two versions of ConKD: hard gate and soft gate. The former selectively gates between two task-specific teachers, while the latter integrates knowledge from both teachers. Our gates are computed on-the-fly in a context-specific manner, facilitating flexible integration of relevant knowledge. Extensive experiments demonstrate that our single model significantly improves recommendation performance while enhancing fluency, and achieves comparable results in terms of diversity.Comment: EMNLP 2023 Main Conferenc

MCP: Self-supervised Pre-training for Personalized Chatbots with Multi-level Contrastive Sampling

Personalized chatbots focus on endowing the chatbots with a consistent personality to behave like real users and further act as personal assistants. Previous studies have explored generating implicit user profiles from the user's dialogue history for building personalized chatbots. However, these studies only use the response generation loss to train the entire model, thus it is prone to suffer from the problem of data sparsity. Besides, they overemphasize the final generated response's quality while ignoring the correlations and fusions between the user's dialogue history, leading to rough data representations and performance degradation. To tackle these problems, we propose a self-supervised learning framework MCP for capturing better representations from users' dialogue history for personalized chatbots. Specifically, we apply contrastive sampling methods to leverage the supervised signals hidden in user dialog history, and generate the pre-training samples for enhancing the model. We design three pre-training tasks based on three types of contrastive pairs from user dialogue history, namely response pairs, sequence augmentation pairs, and user pairs. We pre-train the utterance encoder and the history encoder towards the contrastive objectives and use these pre-trained encoders for generating user profiles while personalized response generation. Experimental results on two real-world datasets show a significant improvement in our proposed model MCP compared with the existing methods

Taxonomy of Usage Issues for Consumer-centric Online Health Information Provision

Consumers are increasingly using Internet portals when searching for relevant health information. Despite the broad range of health information portals (HIPs) available, usage problems with such portals are still widely recognized and reported. In this study, we analyzed usage data from an operational health information portal and identified ways in which these problems can be addressed. While previous usage data and log analysis research has focused more on user behaviors, query structures, and human-computer interaction issues, this study covers more comprehensive issues such as content. We describe a taxonomy of usage issues derived from a literature analysis. We describe how we validated and refined the taxonomy based on analyzing the usage data from an operational health portal. Findings from the usage data indicate that a range of content issues exist that lead to unsuccessful searches. The analysis also highlights that users’ ineffective information seeking strategies are not well supported by the system’s design. We use this taxonomy to propose a usage-driven, consumer-centered approach for dynamic improvements of HIPs. We also discuss the study’s limitations and directions for future research