Hierarchical Attention Network for Visually-aware Food Recommendation

Food recommender systems play an important role in assisting users to identify the desired food to eat. Deciding what food to eat is a complex and multi-faceted process, which is influenced by many factors such as the ingredients, appearance of the recipe, the user's personal preference on food, and various contexts like what had been eaten in the past meals. In this work, we formulate the food recommendation problem as predicting user preference on recipes based on three key factors that determine a user's choice on food, namely, 1) the user's (and other users') history; 2) the ingredients of a recipe; and 3) the descriptive image of a recipe. To address this challenging problem, we develop a dedicated neural network based solution Hierarchical Attention based Food Recommendation (HAFR) which is capable of: 1) capturing the collaborative filtering effect like what similar users tend to eat; 2) inferring a user's preference at the ingredient level; and 3) learning user preference from the recipe's visual images. To evaluate our proposed method, we construct a large-scale dataset consisting of millions of ratings from AllRecipes.com. Extensive experiments show that our method outperforms several competing recommender solutions like Factorization Machine and Visual Bayesian Personalized Ranking with an average improvement of 12%, offering promising results in predicting user preference for food. Codes and dataset will be released upon acceptance

NAIS: Neural Attentive Item Similarity Model for Recommendation

Item-to-item collaborative filtering (aka. item-based CF) has been long used for building recommender systems in industrial settings, owing to its interpretability and efficiency in real-time personalization. It builds a user's profile as her historically interacted items, recommending new items that are similar to the user's profile. As such, the key to an item-based CF method is in the estimation of item similarities. Early approaches use statistical measures such as cosine similarity and Pearson coefficient to estimate item similarities, which are less accurate since they lack tailored optimization for the recommendation task. In recent years, several works attempt to learn item similarities from data, by expressing the similarity as an underlying model and estimating model parameters by optimizing a recommendation-aware objective function. While extensive efforts have been made to use shallow linear models for learning item similarities, there has been relatively less work exploring nonlinear neural network models for item-based CF. In this work, we propose a neural network model named Neural Attentive Item Similarity model (NAIS) for item-based CF. The key to our design of NAIS is an attention network, which is capable of distinguishing which historical items in a user profile are more important for a prediction. Compared to the state-of-the-art item-based CF method Factored Item Similarity Model (FISM), our NAIS has stronger representation power with only a few additional parameters brought by the attention network. Extensive experiments on two public benchmarks demonstrate the effectiveness of NAIS. This work is the first attempt that designs neural network models for item-based CF, opening up new research possibilities for future developments of neural recommender systems

Utilizing a 3D game engine to develop a virtual design review system

A design review process is where information is exchanged between the designers and design reviewers to resolve any potential design related issues, and to ensure that the interests and goals of the owner are met. The effective execution of design review will minimize potential errors or conflicts, reduce the time for review, shorten the project life-cycle, allow for earlier occupancy, and ultimately translate into significant total project savings to the owner. However, the current methods of design review are still heavily relying on 2D paper-based format, sequential and lack central and integrated information base for efficient exchange and flow of information. There is thus a need for the use of a new medium that allow for 3D visualization of designs, collaboration among designers and design reviewers, and early and easy access to design review information. This paper documents the innovative utilization of a 3D game engine, the Torque Game Engine as the underlying tool and enabling technology for a design review system, the Virtual Design Review System for architectural designs. Two major elements are incorporated; 1) a 3D game engine as the driving tool for the development and implementation of design review processes, and 2) a virtual environment as the medium for design review, where visualization of design and design review information is based on sound principles of GUI design. The development of the VDRS involves two major phases; firstly, the creation of the assets and the assembly of the virtual environment, and secondly, the modification of existing functions or introducing new functionality through programming of the 3D game engine in order to support design review in a virtual environment. The features that are included in the VDRS are support for database, real-time collaboration across network, viewing and navigation modes, 3D object manipulation, parametric input, GUI, and organization for 3D objects