Multi-domain Recommendation with Embedding Disentangling and Domain Alignment

Multi-domain recommendation (MDR) aims to provide recommendations for different domains (e.g., types of products) with overlapping users/items and is common for platforms such as Amazon, Facebook, and LinkedIn that host multiple services. Existing MDR models face two challenges: First, it is difficult to disentangle knowledge that generalizes across domains (e.g., a user likes cheap items) and knowledge specific to a single domain (e.g., a user likes blue clothing but not blue cars). Second, they have limited ability to transfer knowledge across domains with small overlaps. We propose a new MDR method named EDDA with two key components, i.e., embedding disentangling recommender and domain alignment, to tackle the two challenges respectively. In particular, the embedding disentangling recommender separates both the model and embedding for the inter-domain part and the intra-domain part, while most existing MDR methods only focus on model-level disentangling. The domain alignment leverages random walks from graph processing to identify similar user/item pairs from different domains and encourages similar user/item pairs to have similar embeddings, enhancing knowledge transfer. We compare EDDA with 12 state-of-the-art baselines on 3 real datasets. The results show that EDDA consistently outperforms the baselines on all datasets and domains. All datasets and codes are available at https://github.com/Stevenn9981/EDDA.Comment: Accepted by CIKM'23 as a Long pape

Property Recommendation System With Geospatial Data Analytics Andnatural Language Processing For Urban Land Use

Recently Cuyahoga County has been tremendously improved as properties are being constructed, renovated, or altered for new land use transactions on a nearly daily basis. Most existing property recommendation systems for the area simply rely on surface-level information and user history data to produce recommendations while failing to prioritize factors according to their importance and utilizing the location based complex information efficiently. This is leading them to become stagnant and simplistic in their approach and their accuracy is worsening as there are too many factors to be considered and location based complex yet useful information such as land use aspects of neighboring areas or information about people who are living or working in the area are often hard to be discovered. To combat these issues, this thesis proposes a modern property recommendation system with new approaches: 1) Employing data analytic methods to discover complex location based geospatial knowledge from big data processing, 2) Collecting and deriving summary information on people demographic data in the neighbor, and 3) Adopting natural language processing techniques for a user given phrase query to generate accurate candidate sets. Our recommendation system consists of three key components: 1) Using derived geospatial knowledge as new features and viewpoints for a better overall understanding of neighbor for a given property. 2) Incorporating Hotspot Analysis and data analytic methods to identify which areas are the v most ideal for each type of properties based on current and history data. 3) Allowing a user query in a sentence or phrase through natural language text processing techniques to create accurate candidates to tailor recommendations to a given individual user to return the Top-N ranked results. The experimental results show the effectiveness of these new approaches

On-Device Recommender Systems: A Comprehensive Survey

Recommender systems have been widely deployed in various real-world applications to help users identify content of interest from massive amounts of information. Traditional recommender systems work by collecting user-item interaction data in a cloud-based data center and training a centralized model to perform the recommendation service. However, such cloud-based recommender systems (CloudRSs) inevitably suffer from excessive resource consumption, response latency, as well as privacy and security risks concerning both data and models. Recently, driven by the advances in storage, communication, and computation capabilities of edge devices, there has been a shift of focus from CloudRSs to on-device recommender systems (DeviceRSs), which leverage the capabilities of edge devices to minimize centralized data storage requirements, reduce the response latency caused by communication overheads, and enhance user privacy and security by localizing data processing and model training. Despite the rapid rise of DeviceRSs, there is a clear absence of timely literature reviews that systematically introduce, categorize and contrast these methods. To bridge this gap, we aim to provide a comprehensive survey of DeviceRSs, covering three main aspects: (1) the deployment and inference of DeviceRSs (2) the training and update of DeviceRSs (3) the security and privacy of DeviceRSs. Furthermore, we provide a fine-grained and systematic taxonomy of the methods involved in each aspect, followed by a discussion regarding challenges and future research directions. This is the first comprehensive survey on DeviceRSs that covers a spectrum of tasks to fit various needs. We believe this survey will help readers effectively grasp the current research status in this field, equip them with relevant technical foundations, and stimulate new research ideas for developing DeviceRSs