Hardware Architecture Proposal for TEDA algorithm to Data Streaming Anomaly Detection

GPT4Rec：面向流式推荐的图提示调优

GPT4Rec: Graph Prompt Tuning for Streaming Recommendation

针对流式推荐中传统模型难适配动态需求、现有方法存隐私或过稳定性等问题，提出含节点、结构、视图三级提示的GPT4Rec图提示调优方法，经多数据集验证有效且高效。

Large-Scale Time-Shifted Streaming Delivery

An attractive new feature of connected TV systems consists in allowing users to access past portions of the TV channel. This feature, called time-shifted streaming, is now used by millions of TV viewers. We address in this paper the design of a large-scale delivery system for time-shifted streaming. We highlight the characteristics of time-shifted streaming that prevent known video delivery systems to be used. Then, we present two proposals that meet the demand for two radically different types of TV operator. First, the Peer-Assisted Catch-Up Streaming system, namely PACUS, aims at reducing t…

A Scalable Pipelined Dataflow Accelerator for Object Region Proposals on FPGA Platform

Region proposal is critical for object detection while it usually poses a bottleneck in improving the computation efficiency on traditional control-flow architectures. We have observed region proposal tasks are potentially suitable for performing pipelined parallelism by exploiting dataflow driven acceleration. In this paper, a scalable pipelined dataflow accelerator is proposed for efficient region proposals on FPGA platform. The accelerator processes image data by a streaming manner with three sequential stages: resizing, kernel computing and sorting. First, Ping-Pong cache strategy is adopt…

A session-based song recommendation approach involving user characterization along the play power-law distribution

In recent years, streaming music platforms have become very popular mainly due to the huge number of songs these systems make available to users. This enormous availability means that recommendation mechanisms that help users to select the music they like need to be incorporated. However, developing reliable recommender systems in the music field involves dealing with many problems, some of which are generic and widely studied in the literature, while others are specific to this application domain and are therefore less well-known. This work is focused on two important issues that have not rec…

Continuous Outlier Mining of Streaming Data in Flink

In this work, we focus on distance-based outliers in a metric space, where the status of an entity as to whether it is an outlier is based on the number of other entities in its neighborhood. In recent years, several solutions have tackled the problem of distance-based outliers in data streams, where outliers must be mined continuously as new elements become available. An interesting research problem is to combine the streaming environment with massively parallel systems to provide scalable streambased algorithms. However, none of the previously proposed techniques refer to a massively paralle…

Evaluation of Session-based Recommendation Algorithms

Recommender systems help users find relevant items of interest, for example on e-commerce or media streaming sites. Most academic research is concerned with approaches that personalize the recommendations according to long-term user profiles. In many real-world applications, however, such long-term profiles often do not exist and recommendations therefore have to be made solely based on the observed behavior of a user during an ongoing session. Given the high practical relevance of the problem, an increased interest in this problem can be observed in recent years, leading to a number of propos…