Session-based recommendation aims to predict the user's next action based on anonymous sessions without using side information. Most of the real-world session datasets are sparse and have long-tail item distribution. Although long-tail item recommendation plays a crucial role in improving user satisfaction, only a few methods have been proposed to take the long-tail session recommendation into consideration. Previous works in handling data sparsity problems are mostly limited to self-supervised learning techniques with heuristic augmentation which can ruin the original characteristic of session datasets, sequential and co-occurrences, and make noisier short sessions by dropping items and cropping sequences. We propose a novel method, LOAM, improving LOng-tail session-based recommendation via niche walk Augmentation and tail session Mixup, that alleviates popularity bias and enhances long-tail recommendation performance. LOAM consists of two modules, Niche Walk Augmentation (NWA) and Tail Session Mixup (TSM). NWA can generate synthetic sessions considering long-tail distribution which are likely to be found in original datasets, unlike previous heuristic methods, and expose a recommender model to various item transitions with global information. This improves the item coverage of recommendations. TSM makes the model more generalized and robust by interpolating sessions at the representation level. It encourages the recommender system to predict niche items with more diversity and relevance. We conduct extensive experiments with four real-world datasets and verify that our methods greatly improve tail performance while balancing overall performance.

The objective of the study is to describe the development and user acceptance testing of a mobile-based application to monitor daily cannabis use of individuals at clinical high-risk for psychosis. Through participatory co-design, app development was staggered in two phases. In Phase 1 (app development), two focus groups were run to gather information on a) app content, and b) graphic design Participants named the application LooseLeaf, which comprised five features: home screen, inventory, daily questions, calculators, and stats. The application monitored daily cannabis consumption, cannabis-related experiences, other substances, and the perception of their day. In Phase 2 (user acceptance testing), a prototype of the application was debugged and tested by developers. Participants used the app for 1 week. Usability was measured with quantitative and qualitative data about the app through the Mobile Application Rating Scale user-version (uMARS). LooseLeaf received a good overall score in the MARS with the highest rating in esthetics, information, and functionality. In conclusion, co-design through focus groups with youth provided effective feedback for developing LooseLeaf, demonstrating initial acceptability and usability.