Multi-task Recommender Systems (MTRSs) has become increasingly prevalent in a variety of real-world applications due to their exceptional training efficiency and recommendation quality. However, conventional MTRSs often input all relevant feature fields without distinguishing their contributions to different tasks, which can lead to confusion and a decline in performance. Existing feature selection methods may neglect task relations or require significant computation during model training in multi-task setting. To this end, this paper proposes a novel Single-shot Feature Selection framework for MTRSs, referred to as MultiSFS, which is capable of selecting feature fields for each task while considering task relations in a single-shot manner. Specifically, MultiSFS first efficiently obtains task-specific feature importance through a single forward-backward pass. Then, a data-task bipartite graph is constructed to learn field-level task relations. Subsequently, MultiSFS merges the feature importance according to task relations and selects feature fields for different tasks. To demonstrate the effectiveness and properties of MultiSFS, we integrate it with representative MTRS models and evaluate on three real-world datasets. The implementation code is available online to ease reproducibility.

Single-shot Feature Selection for Multi-task Recommendations

Parameter reduction is a crucial procedure for enhancing the effectiveness of decision-making processes in the theory of interval-valued fuzzy soft set, which is a developing and excellent mathematical tool for processing blurry data. However, there exist severe gaps in the existing literatures. That is, the existing parameter reduction algorithms for interval-valued fuzzy soft sets have high computational complexity and low success rate of finding reduction. By considering these gaps, this study firstly develops the notions of the mean degree of membership and Euclidean distance between parameters. And then a new Euclidean Distance-based parameter reduction algorithm for interval-valued fuzzy soft set is proposed. In addition, we compare our proposed algorithm with the existing parameter reduction algorithms in terms of computational complexity and success rate of finding reduction on 35 randomly generated data sets and a real-life application of Five-Star Hotels evaluation. Through the comparison results, it is clear that our approach has the lower computational complexity and higher success rate of finding reduction. Consequently, our method which is substantiated on the validity and superiority is a good solution for parameter reduction of interval-valued fuzzy soft sets. 

A Euclidean Distance-based parameter reduction algorithm for interval-valued fuzzy soft sets

Multi-Source Domain Adaptation (MSDA) is widely used in various machine learning scenarios for domain shifts between labeled source domains and unlabeled target domains. Conventional MSDA methods are built on a strong hypothesis that data samples from the same source belong to the same domain with the same latent distribution. However, in practice sources and their latent domains are not necessarily one-to-one correspondence. To tackle this problem, a novel Multi-source Reconstructed Domain Adaptation (MRDA) framework for MSDA is proposed. We use an Expectation-Maximization (EM) mechanism that iteratively reconstructs the source domains to recover the latent domains and performs domain adaptation on the reconstructed domains. Specifically, in the E-step, we cluster the samples from multiple sources into different latent domains, and a soft assignment strategy is proposed to avoid cluster imbalance. In the M-step, we freeze the latent domains clustered in the E-step and optimize the objective function for domain adaptation, and a global-specific feature extractor is used to capture both domain-invariant and domain-specific features. Extensive experiments demonstrate that our approach can reconstruct source domains and perform domain adaptation on the reconstructed domains effectively, thus significantly outperforming state-of-the-art (SOTA) baselines (e.g., 1% to 3.1% absolute improvement in AUC).

Multi-Source Domain Adaptation via Latent Domain Reconstruction

A novel algorithm for all normal parameter reductions of a soft set based on object weighting and integer partition

Speed Prediction of Multiple Traffic Scenarios with Local Fluctuation

AbstractMulti-task learning (MTL), which jointly tackles multiple tasks through information sharing, has been widely applied to many recommendation applications. Recently, current efforts targeted for recommendation focus on learning task relationships based on the Multi-gate Mixture-of-Experts (MMoE) architecture with shared input features (i.e., subtle feature engineering for user-item interaction). Recent evidences suggest the Graph Neural Network (GNN) as a powerful component in characterizing deep interaction context for recommendation, greatly contributing to easing the data sparseness issue in online advertising services. Hence, we make the first attempt to explore the GNN towards multi-task recommendation, by designing Mixture of Graph enhanced Expert Networks (MoGENet). Specifically, we propose a novel multi-channel graph neural network to jointly model high-order information with the user-item bipartite graph as well as derived collaborative similarity graphs for users and items. On the top of the learned deep interaction context, a group of graph enhanced expert networks are introduced for contributing to the multi-task recommendation in a cooperative manner. Experimental results on three real-world datasets show that MoGENet consistently and significantly outperforms state-of-the-art baselines across all target tasks. KeywordsMulti-task learningGraph learningRecommender system 

Mixture of Graph Enhanced Expert Networks for Multi-task Recommendation

This article investigates $k|U|$ parameter subsets of a soft set matrix whose column sums are integral multiples of $|U|$ (i.e., the number of objects in the soft set domain $U$ ). This kind of parameter subset represents an important data structure. Particularly, as a necessary condition, it has been shown to be useful in the parameter reduction problems of soft sets. This article focuses on the minimal $k|U|$ parameter subsets, whose any proper subset cannot be a $k|U|$ parameter subset. An offline and online algorithm for minimal k|U| parameter subsets is proposed. Its basic function is based on integer partition in an offline way. When soft set data come online, the algorithm only needs to filter the factorization results according to the related constraints within the input soft set. We also bring in combinatorial formulas for computing the number of $k|U|$ parameter subsets and the approximate number of minimal $k|U|$ parameter subsets. As an application of $k|U|$ parameter subsets, the method of integer partition is also extended for normal parameter reduction problems of soft sets. The experimental results show that the proposed method does result in better performance.

/pdf/an-offline-and-online-algorithm-for-all-minimal-k-u-2nqa11jda9.pdf

An Offline and Online Algorithm for All Minimal k|U| Parameter Subsets of a Soft Set Based on Integer Partition

Multi-task learning (MTL) has experienced rapid growth in recent years. A typical way of conducting MTL with deep neural networks (DNNs) is either establishing a sort of global feature sharing mechanism across all tasks or assigning each task an individual set of parameters with cross-connections. However, these existing approaches leverage DNNs only to share features of a certain order. Several modelsdemonstrated that explicitly modeling feature sharing with both low-order and high-order features can boost performance. To this end, we propose a model-agnostic sparse routing architecture called MASR, which emphasizes arbitrary order feature sharing for multi-task learning. It is able to choose specific orders of features to route for a given task through learnable latent variables. Moreover, MASR is model-agnostic and can be combined with existing MTL models to share features of both low-order and high-order. Extensive experimental results on several real-world datasets not only confirm the significant improvement of MASR performed to existing MTL models but also outperform existing hybrid architectures in terms of AUC metric.

MASR: A Model-Agnostic Sparse Routing Architecture for Arbitrary Order Feature Sharing in Multi-Task Learning

Multi-task learning (MTL) aims at solving multiple related tasks simultaneously and has experienced rapid growth in recent years. However, MTL models often suffer from performance degeneration with negative transfer due to learning several tasks simultaneously. Some related work attributed the source of the problem is the conflicting gradients. In this case, it is needed to select useful gradient updates for all tasks carefully. To this end, we propose a novel optimization approach for MTL, named GDOD, which manipulates gradients of each task using an orthogonal basis decomposed from the span of all task gradients. GDOD decomposes gradients into task-shared and task-conflict components explicitly and adopts a general update rule for avoiding interference across all task gradients. This allows guiding the update directions depending on the task-shared components. Moreover, we prove the convergence of GDOD theoretically under both convex and non-convex assumptions. Experiment results on several multi-task datasets not only demonstrate the significant improvement of GDOD performed to existing MTL models but also prove that our algorithm outperforms state-of-the-art optimization methods in terms of AUC and Logloss metrics. 

Gdod

Handle everyday research tasks with reliable, citation-backed results

Your personal Research Agent to handle research tasks with citation-backed results

Popular Tasks used by Researchers

How can I help with your research?

Meet SciSpace

Get more enhanced response by uploading the PDFs you want me to reference.

No relevant PDFs in your library

SciSpace is the AI research assistant for academics. Run systematic literature reviews on 280M+ papers, and write papers with cited sources. Trusted by 1M+ students, PhDs & researchers.

SciSpace | AI for Research

Analyze PDFs

Code & Manuscripts

Funding & Grants

Literature & Patents

Medical & Clinical Data

Systematic Review

Visualize & Present

Web & Data

Build a Google Scholar-like website for your research.

Build a website

Create charts and images for your research

Create a Chart

Write a paper for submission to a journal

Draft a manuscript

Patent Search

Design eye-catching scientific posters in minutes.

Scientific Poster Generation

Systematic Literature Review

One task is running at the moment. Your messages will be shown right after.

Drag and drop or click here to browse

Loved by <highlight>1 million+</highlight> researchers

Extract a list of specific topics and their sources from unstructured text

Topics

Compare and analyze relevant papers that matches with your search

Papers

Get insights from PDFs and bookmarked papers from your library

My library

Recent searches

Try searching for:

Catch AI-generated content in scholarly and non-scholarly content

{ai} Detector

Ai Writer

Get PDF Summaries, highlighted text explanations 

Chat with PDF

Effortlessly create in-text citations and bibliographies in APA and 2,500 other formats

Citation generator

Get explanations, summaries, and answers on academic papers

Ease up your research workflow with {scispace}'s cohort of exciting AI tools

Elevate your academic writing skills and convey your ideas the way you want

Paraphraser

Explore our range of reading and writing tools

Your file is being prepared and should be ready in a few minutes. If it's a large file, it might take a bit longer. You can close this window, and we'll email you the file when it's done.

You have reached a maximum limit of <strong>{limit}</strong> columns in the table. Remove at least <strong>1</strong> column to add or create another one.

Ruize Wu

Author Tools

Chat about Author