With the advance of online education systems, accessibility to learning materials has increased. In these systems, students can practice independently and learn from different learning materials over long periods of time. As a result, it is essential to trace students' knowledge states over long learning sequences while maintaining a personalized model of each individual student's progress. However, the existing deep learning-based knowledge tracing models are either not personalized or not tailored for handling long sequences. Handling long sequences are especially essential in the online education environments, in where models are preferred to be updated with the newly collected user data in a timely manner as students could acquire knowledge on each learning activity. In this paper, we propose a knowledge tracing model, Continuous Personalized Knowledge Tracing (CPKT), that can mimic the real-world long-term continuous learning scenario by incorporating a novel online model training paradigm that is suitable for the knowledge tracing problem. To achieve personalized knowledge tracing, we propose two model components: 1) personalized memory slots to maintain learner's knowledge in a lifelong manner, and 2) personalized user embeddings that help to accurately predict the individual responses, correctly detect the personalized knowledge acquisition and forgetting patterns, and better interpret and analyze the learner's progress. Additionally, we propose transition-aware stochastic shared embedding according to the learning transition matrix to regularize the online model training. Extensive experiments on four real-world datasets showcase the effectiveness and superiority of CPKT, especially for students with longer sequences.

Accurate modeling of student knowledge is essential for large-scale online learning systems that are increasingly used for student training. Knowledge tracing aims to model student knowledge state given the student’s sequence of learning activities. Modern Knowledge tracing (KT) is usually formulated as a supervised sequence learning problem to predict students’ future practice performance according to their past observed practice scores by summarizing student knowledge state as a set of evolving hidden variables. Because of this formulation, many current KT solutions are not fit f or modeling student learning from non-assessed learning activities with no explicit feedback or score observation (e.g., watching video lectures that are not graded). Additionally, these models cannot explicitly represent the dynamics of knowledge transfer among different learning activities, particularly between the assessed (e.g., quizzes) and non-assessed (e.g., video lectures) learning activities. In this paper, we propose Transition-Aware Multi-activity Knowledge Tracing (TAMKOT), which models knowledge transfer between learning materials, in addition to student knowledge, when students transition between and within assessed and non-assessed learning materials. TAMKOT is formulated as a deep recurrent multi-activity learning model that explicitly learns knowledge transfer by activating and learning a set of knowledge transfer matrices, one for each transition type between student activities. Accordingly, our model allows for representing each material type in a different yet transferrable latent space while maintaining student knowledge in a shared space. We evaluate our model on three real-world publicly available datasets and demonstrate TAMKOT’s capability in predicting student performance and modeling knowledge transfer.

Recommender Systems are becoming an inherent part of today’s Internet. They can be found anywhere from e-commerce platforms (eBay, Amazon) to music or movie streaming (Spotify, Netflix), social media (Facebook, Instagram, TikTok), travel platforms (Booking.com, Expedia), and much more. Whether a recommendation is successful or not can rely on multiple objectives such as user satisfaction, business value, and societal issues. In addition, the long-term happiness (along with short-term excitements and delight) of the users is critical for a recommender system to be considered successful. MORS workshop brings together researchers and practitioners to discuss the importance of these aspects of recommender systems and find ways to develop algorithms to build multi-objective recommenders and also evaluation metrics to assess their success.

Historically, the main criterion for a successful recommender system was the relevance of the recommended items to the user. In other words, the only objective for the recommendation algorithm was to learn user’s preferences for different items and generate recommendations accordingly. However, real-world recommender systems are well beyond a simple objective and often need to take into account multiple objectives simultaneously. These objectives can be either from the users’ perspective or they could come from other stakeholders such as item providers or any party that could be impacted by the recommendations. Such multi-objective and multi-stakeholder recommenders present unique challenges and these challenges were the focus of the MORS workshop.

Student procrastination and cramming for deadlines are major challenges in online learning environments, with negative educational and well-being side effects. Modeling student activities in continuous time and predicting their next study time are important problems that can help in creating personalized timely interventions to mitigate these challenges. However, previous attempts on dynamic modeling of student procrastination suffer from major issues: they are unable to predict the next activity times, cannot deal with missing activity history, are not personalized, and disregard important course properties, such as assignment deadlines, that are essential in explaining the cramming behavior. To resolve these problems, we introduce a new personalized stimuli-sensitive Hawkes process model (SSHP), by jointly modeling all student-assignment pairs and utilizing their similarities, to predict students’ next activity times even when there are no historical observations. Unlike regular point processes that assume a constant external triggering effect from the environment, we model three dynamic types of external stimuli, according to assignment availabilities, assignment deadlines, and each student’s time management habits. Our experiments on two synthetic datasets and two real-world datasets show a superior performance of future activity prediction, comparing with state-of-the-art models. Moreover, we show that our model achieves a flexible and accurate parameterization of activity intensities in students.

Having data from multiple sources, cross-domain and context-aware recommender systems, with the help of transfer learning approaches, aim to integrate such data to improve recommendation quality and alleviate issues such as cold-start problem. With the advantages of these techniques, we host the second international workshop on intelligent recommender systems by knowledge transfer and learning (RecSysKTL) to provide such a forum for both academia and industry researchers as well as application developers from around the world to present their work and discuss exciting research ideas or outcomes. The workshop is held in conjunction with the ACM Conference on Recommender Systems 2018 on October 6th in Vancouver, Canada.

Cross-domain recommender systems and transfer learning approaches are useful to help integrate knowledge from different places, so that we alleviate some existing problems (such as the cold-start problem), or improve the quality of recommender systems. With the advantages of these techniques, we host the first international workshop on intelligent recommender systems by knowledge transfer and learning (RecSysKTL) to provide such a forum for academia researchers and application developers from around the world to present their work and discuss exciting research ideas or outcomes. The workshop is held in conjunction with the ACM Conference on Recommender Systems 2017 on August 27th at Como, Italy.

As the heterogeneity of data sources are increasing on the web, and due to the sparsity of data in each of these data sources, cross-domain recommendation is becoming an emerging research topic in the recent years. Cross-domain collaborative filtering aims to transfer the user rating pattern from source (auxiliary) domains to a target domain for the purpose of alleviating the sparsity problem and providing better target recommendations. However, the studies so far have either focused on a limited number of domains that are assumed to be related to each other (such as books and movies), or a division of the same dataset (such as movies) into different domains based on an item characteristic (such as genre). In this paper, we study a broad set of domains and their characteristics to understand the factors that affect the success or failure of cross-domain collaborative filtering, the amount of improvement in cross-domain approaches, and the selection of best source domains for a specific target domain. We propose to use Canonical Correlation Analysis (CCA) as a significant major factor in finding the most promising source domains for a target domain, and suggest a cross-domain collaborative filtering based on CCA (CD-CCA) that proves to be successful in using the shared information between domains in the target recommendations.