When we sequentially recommend top-k items to users, how can we recommend them diversely while maintaining accuracy? Aggregate-level diversity is an important topic in recommender system since it is essential to maximize the potential profit of platforms by exposing a variety of items to users. However, previous studies do not consider the order of users receiving recommendations and assume that all users receive recommendations at once. In reality, users do not simultaneously receive recommendations so the preferences of the latter users are not given during recommending to the former users. In this work, we introduce the problem of sequentially diversified recommendation and propose SAPID, an accurate method to address the problem. SAPID removes the popularity bias from the model through a negative sampling mechanism based on temporal popularities. Then, SAPID collects candidate items to recommend based on the distribution of preference scores. Finally, SAPID decides which items to recommend immediately or later according to their estimated exposure opportunities. Extensive experiments show that SAPID shows the state-of-the-art performance in real-world datasets by achieving up to 61.0% increased diversity with 38.9% higher accuracy compared to the second-best competitor.

Given a multidimensional array, how can we optimize the computation process for a part of Fourier coefficients? Discrete Fourier transform plays an overarching role in various data mining tasks. Recent interest has focused on efficiently calculating a small part of Fourier coefficients, exploiting the energy compaction property of real-world data. Current methods for partial Fourier transform frequently encounter efficiency issues, yet the adoption of pre-computation techniques within the PFT algorithm has shown promising performance. However, PFT still faces limitations in handling multidimensional data efficiently and requires manual hyperparameter tuning, leading to additional costs.

How can we accurately recommend actions for users to control their devices at home? Action recommendation for smart home has attracted increasing attention due to its potential impact on the markets of Internet of Things (IoT). However, designing an effective action recommender system is challenging because it requires handling context correlations, considering both queried contexts and previous histories of users, and dealing with capricious intentions in history. In this work, we propose SmartSense, an accurate action recommendation method for smart home. For individual action, SmartSense summarizes its device control and temporal contexts in a self-attentive manner, to reflect the importance of the correlation between them. SmartSense then summarizes sequences considering queried contexts in a query-attentive manner to extract the query-related patterns from the sequential actions. SmartSense also transfers the commonsense knowledge from routine data to better handle intentions in action sequences. As a result, SmartSense addresses all three main challenges of action recommendation for smart home, and achieves the state-of-the-art performance giving up to 9.8% higher [email protected] than the best competitor.