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1. Personalized Remedial Recommendations for SQL Programming Practice System

   https://doi.org/10.1145/3386392.3399312  

   Barria-Pineda, Jordan; Akhuseyinoglu, Kamil; Brusilovsky, Peter; Pollari-Malmi, Kerttu; Sirkiä, Teemu; Malmi, Lauri (July 2020, Proceedings of Workshop on Adaptation and Personalization in Computer Science Education at the 28th ACM Conference on User Modeling, Adaptation and Personalization)

   Personalized recommendation of learning content is one of the most frequently cited benefits of personalized online learning. It is expected that with personalized content recommendation students will be able to build their own unique and optimal learning paths and to achieve course goals in the most optimal way. However, in many practical cases students search for learning content not to expand their knowledge, but to address problems encountered in the learning process, such as failures to solve a problem. In these cases, students could be better assisted by remedial recommendations focused on content that could help in resolving current problems. This paper presents a transparent and explainable interface for remedial recommendations in an online programming practice system. The interface was implemented to support SQL programming practice and evaluated in the context of a large database course. The paper summarizes the insights obtained from the study and discusses future work on remedial recommendations. 

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2. Task-optimized User Clustering based on Mobile App Usage for Cold-start Recommendations

   Liu, Bulou; Bai, Bing; Xie, Weibang; Guo, Yiwen; Chen, Hao (August 2022, ACM SIGKDD Conference on Knowledge Discovery and Data Mining)

   This paper reports our recent practice of recommending articles to cold-start users at Tencent. Transferring knowledge from information-rich domains to help user modeling is an effective way to address the user-side cold-start problem. Our previous work demonstrated that general-purpose user embeddings based on mobile app usage helped article recommendations. However, high-dimensional embeddings are cumbersome for online usage, thus limiting the adoption. On the other hand, user clustering, which partitions users into several groups, can provide a lightweight, online-friendly, and explainable way to help recommendations. Effective user clustering for article recommendations based on mobile app usage faces unique challenges, including (1) the gap between an active user’s behavior of mobile app usage and article reading, and (2) the gap between mobile app usage patterns of active and cold-start users. To address the challenges, we propose a tailored Dual Alignment User Clustering (DAUC) model, which applies a sample-wise contrastive alignment to liminate the gap between active users’ mobile app usage and article reading behavior, and a distribution-wise adversarial alignment to eliminate the gap between active users’ and cold-start users’ app usage behavior. With DAUC, cold-start recommendation-optimized user clustering based on mobile app usage can be achieved. On top of the user clusters, we further build candidate generation strategies, real-time features, and corresponding ranking models without much engineering difficulty. Both online and offline experiments demonstrate the effectiveness of our work. 

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3. Debiased Explainable Pairwise Ranking from Implicit Feedback

   K. Damak, S. Khenissi (January 2021, Proceedings of the ACM Conference on Recommender Systems (ACM RecSys))

   null (Ed.)

   Recent work in recommender systems has emphasized the importance of fairness, with a particular interest in bias and transparency, in addition to predictive accuracy. In this paper, we focus on the state of the art pairwise ranking model, Bayesian Personalized Ranking (BPR), which has previously been found to outperform pointwise models in predictive accuracy, while also being able to handle implicit feedback. Specifically, we address two limitations of BPR: (1) BPR is a black box model that does not explain its outputs, thus limiting the user's trust in the recommendations, and the analyst's ability to scrutinize a model's outputs; and (2) BPR is vulnerable to exposure bias due to the data being Missing Not At Random (MNAR). This exposure bias usually translates into an unfairness against the least popular items because they risk being under-exposed by the recommender system. In this work, we first propose a novel explainable loss function and a corresponding Matrix Factorization-based model called Explainable Bayesian Personalized Ranking (EBPR) that generates recommendations along with item-based explanations. Then, we theoretically quantify additional exposure bias resulting from the explainability, and use it as a basis to propose an unbiased estimator for the ideal EBPR loss. The result is a ranking model that aptly captures both debiased and explainable user preferences. Finally, we perform an empirical study on three real-world datasets that demonstrate the advantages of our proposed models. 

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4. Explainable Recommendation via Multi-Task Learning in Opinionated Text Data

   https://doi.org/10.1145/3209978.3210010  

   Wang, Nan; Wang, Hongning; Jia, Yiling; Yin, Yue (July 2018, SIGIR '18 The 41st International ACM SIGIR Conference on Research & Development in Information Retrieval)

   Explaining automatically generated recommendations allows users to make more informed and accurate decisions about which results to utilize, and therefore improves their satisfaction. In this work, we develop a multi-task learning solution for explainable recommendation. Two companion learning tasks of user preference modeling for recommendation and opinionated content modeling for explanation are integrated via a joint tensor factorization. As a result, the algorithm predicts not only a user's preference over a list of items, i.e., recommendation, but also how the user would appreciate a particular item at the feature level, i.e., opinionated textual explanation. Extensive experiments on two large collections of Amazon and Yelp reviews confirmed the effectiveness of our solution in both recommendation and explanation tasks, compared with several existing recommendation algorithms. And our extensive user study clearly demonstrates the practical value of the explainable recommendations generated by our algorithm. 

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5. "If it is easy to understand then it will have value": Examining Perceptions of Explainable AI with Community Health Workers in Rural India

   https://doi.org/10.1145/3637348  

   Okolo, Chinasa T; Agarwal, Dhruv; Dell, Nicola; Vashistha, Aditya (April 2024, Proceedings of the ACM on Human-Computer Interaction)

   AI-driven tools are increasingly deployed to support low-skilled community health workers (CHWs) in hard-to-reach communities in the Global South. This paper examines how CHWs in rural India engage with and perceive AI explanations and how we might design explainable AI (XAI) interfaces that are more understandable to them. We conducted semi-structured interviews with CHWs who interacted with a design probe to predict neonatal jaundice in which AI recommendations are accompanied by explanations. We (1) identify how CHWs interpreted AI predictions and the associated explanations, (2) unpack the benefits and pitfalls they perceived of the explanations, and (3) detail how different design elements of the explanations impacted their AI understanding. Our findings demonstrate that while CHWs struggled to understand the AI explanations, they nevertheless expressed a strong preference for the explanations to be integrated into AI-driven tools and perceived several benefits of the explanations, such as helping CHWs learn new skills and improved patient trust in AI tools and in CHWs. We conclude by discussing what elements of AI need to be made explainable to novice AI users like CHWs and outline concrete design recommendations to improve the utility of XAI for novice AI users in non-Western contexts. 

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