Physical facility design faces a fundamental cold-start problem: predicting human behavior in non-existent spaces. Traditional surveys and observational studies create gaps between stated preferences and actual usage, while existing simulation tools require significant technical expertise, limiting accessibility. We introduce PlaceSim, a web-based platform leveraging Large Language Models (LLMs) to simulate realistic human behavior in facilities through a zero-code interface. PlaceSim employs a Persona-Environment-Scenario (P.E.S.) framework that structures LLM reasoning through context-aware AI personas with transparent decision-making processes. The platform provides interactive facility design, AI-driven persona generation, live simulation with reasoning visualization, and what-if analysis for scenario comparison. Evaluated on 18 months of real-world apartment facility data (789,238 usage records from 8,435 residents), our zero-shot approach achieves Jensen-Shannon Divergence scores as low as 0.006, outperforming both supervised learning methods and existing LLM-based tools like SocioVerse without requiring training data. PlaceSim establishes new benchmarks for spatial behavior prediction while providing immediate, actionable insights for architects, urban planners, and facility managers through systematic simulation. The platform is available at https://simulation-viewer.vercel.app/

Popular microblogging sites such as Tumblr have attracted hundreds of millions of users as a content sharing platform, where users can create rich content in the form of posts that are shared with other users who follow them. Due to the sheer amount of posts created on such services, an important task is to make quality recommendations of blogs for users to follow. Apart from traditional recommender system settings where the follower graph is the main data source, additional side-information of users and blogs such as user activity (e.g., like and reblog) and rich content (e.g., text and images) are also available to be exploited for enhanced recommendation performance. In this paper, we propose a novel boosted inductive matrix completion method (BIMC) for blog recommendation. BIMC is an additive low-rank model for user-blog preferences consisting of two components; one component captures the low-rank structure of follow relationships and the other captures the latent structure using side-information. Our model formulation combines the power of the recently proposed inductive matrix completion (IMC) model (for side-information) together with a standard matrix completion (MC) model (for low-rank structure). Furthermore, we utilize recently developed deep learning techniques to obtain semantically rich feature representations of text and images that are incorporated in BIMC. Experiments on a large-scale real-world dataset from Tumblr illustrate the effectiveness of the proposed BIMC method.

The application a smart phone user will launch next intuitively depends on the sequence of apps used recently. More generally, when users interact with systems such as shopping websites or online radio, they click on items that are of interest in the current context. We call the sequence of clicks made in the current session interactional context. It is desirable for a recommender system to use the context set by the user to update recommendations. Most current context-aware recommender systems focus on a relatively less dynamic representational context defined by attributes such as season, location and tastes. In this paper, we study the problem of collaborative filtering with interactional context, where the goal is to make personalized and dynamic recommendations to a user engaged in a session. To this end, we propose the methodname algorithm that works in two stages. First, users are clustered by their transition behavior (one-step Markov transition probabilities between items), and cluster-level Markov models are computed. Then personalized PageRank is computed for a given user on the corresponding cluster Markov graph, with a personalization vector derived from the current context. We give an interpretation of the second stage of the algorithm as adding an appropriate context bias, in addition to click bias (or rating bias), to a classical neighborhood-based collaborative filtering model, where the neighborhood is determined from a Markov graph. Experimental results on two real-life datasets demonstrate the superior performance of our algorithm, where we achieve at least 20% (up to 37%) improvement over competitive methods in the recall level at top-20.

The automated analysis of social networks has become an important problem due to the proliferation of social networks, such as LiveJournal, Flickr and Facebook. The scale of these social networks is massive and continues to grow rapidly. An important problem in social network analysis is proximity estimation that infers the closeness of different users. Link prediction, in turn, is an important application of proximity estimation. However, many methods for computing proximity measures have high computational complexity and are thus prohibitive for large-scale link prediction problems. One way to address this problem is to estimate proximity measures via low-rank approximation. However, a single low-rank approximation may not be sufficient to represent the behavior of the entire network. In this paper, we propose Multi-Scale Link Prediction (MSLP), a framework for link prediction, which can handle massive networks. The basic idea of MSLP is to construct low-rank approximations of the network at multiple scales in an efficient manner. To achieve this, we propose a fast tree-structured approximation algorithm.

Low-density parity-check (LDPC) codes have an inherent stopping criterion, parity-check constraints (equations). By testing the parity-check constraints, an LDPC decoder can detect successful decoding and stop their decoding, which is, however, not possible with turbo codes. In this paper, we propose a stopping criterion to predict decoding failure of LDPC codes, instead of detecting successful decoding. If the decoder predicts the decoding failure in advance, the receiver can more rapidly response to the transmitter and request for additional parity bits with an automatic repeat request (ARQ) protocol, which reduces overall system latency. The receiver can also save power consumption by avoiding unnecessary decoder iterations. The proposed stopping criterion makes use of the variations of the number of satisfied parity-check constraints in the belief-propagation (BP) decoding which is always tested in the conventional BP decoding to detect successful decoding. Thus, the proposed stopping criterion does not require any additional complexity. The counting of satisfied parity-check constraints shows behaviors of the BP decoding, which comes, otherwise, from the observations of changes of log-likelihood ratio (LLR) values in multi-bit resolution with additional complexity.