3 research outputs found
Sequential Recommendation in Online Games with Multiple Sequences, Tasks and User Levels
Online gaming is a multi-billion-dollar industry, which is growing faster
than ever before. Recommender systems (RS) for online games face unique
challenges since they must fulfill players' distinct desires, at different user
levels, based on their action sequences of various action types. Although many
sequential RS already exist, they are mainly single-sequence, single-task, and
single-user-level. In this paper, we introduce a new sequential recommendation
model for multiple sequences, multiple tasks, and multiple user levels
(abbreviated as MRec) in Tencent Games platform, which can fully utilize
complex data in online games. We leverage Graph Neural Network and multi-task
learning to design MRec in order to model the complex information in the
heterogeneous sequential recommendation scenario of Tencent Games. We verify
the effectiveness of MRec on three online games of Tencent Games platform,
in both offline and online evaluations. The results show that MRec
successfully addresses the challenges of recommendation in online games, and it
generates superior recommendations compared with state-of-the-art sequential
recommendation approaches.Comment: 10 pages,4 figure
Efficient Algorithms for Approximate Single-Source Personalized PageRank Queries
Given a graph , a source node and a target node , the personalized
PageRank (PPR) of with respect to is the probability that a random walk
starting from terminates at . An important variant of the PPR query is
single-source PPR (SSPPR), which enumerates all nodes in , and returns the
top- nodes with the highest PPR values with respect to a given source .
PPR in general and SSPPR in particular have important applications in web
search and social networks, e.g., in Twitter's Who-To-Follow recommendation
service. However, PPR computation is known to be expensive on large graphs, and
resistant to indexing. Consequently, previous solutions either use heuristics,
which do not guarantee result quality, or rely on the strong computing power of
modern data centers, which is costly.
Motivated by this, we propose effective index-free and index-based algorithms
for approximate PPR processing, with rigorous guarantees on result quality. We
first present FORA, an approximate SSPPR solution that combines two existing
methods Forward Push (which is fast but does not guarantee quality) and Monte
Carlo Random Walk (accurate but slow) in a simple and yet non-trivial way,
leading to both high accuracy and efficiency. Further, FORA includes a simple
and effective indexing scheme, as well as a module for top- selection with
high pruning power. Extensive experiments demonstrate that the proposed
solutions are orders of magnitude more efficient than their respective
competitors. Notably, on a billion-edge Twitter dataset, FORA answers a top-500
approximate SSPPR query within 1 second, using a single commodity server.Comment: Accepted in the ACM Transactions on Database Systems (TODS
C-SAW: A Framework for Graph Sampling and Random Walk on GPUs
Many applications require to learn, mine, analyze and visualize large-scale
graphs. These graphs are often too large to be addressed efficiently using
conventional graph processing technologies. Many applications have requirements
to analyze, transform, visualize and learn large scale graphs. These graphs are
often too large to be addressed efficiently using conventional graph processing
technologies. Recent literatures convey that graph sampling/random walk could
be an efficient solution. In this paper, we propose, to the best of our
knowledge, the first GPU-based framework for graph sampling/random walk. First,
our framework provides a generic API which allows users to implement a wide
range of sampling and random walk algorithms with ease. Second, offloading this
framework on GPU, we introduce warp-centric parallel selection, and two
optimizations for collision migration. Third, towards supporting graphs that
exceed GPU memory capacity, we introduce efficient data transfer optimizations
for out-of-memory sampling, such as workload-aware scheduling and batched
multi-instance sampling. In its entirety, our framework constantly outperforms
the state-of-the-art projects. First, our framework provides a generic API
which allows users to implement a wide range of sampling and random walk
algorithms with ease. Second, offloading this framework on GPU, we introduce
warp-centric parallel selection, and two novel optimizations for collision
migration. Third, towards supporting graphs that exceed the GPU memory
capacity, we introduce efficient data transfer optimizations for out-of-memory
and multi-GPU sampling, such as workload-aware scheduling and batched
multi-instance sampling. Taken together, our framework constantly outperforms
the state of the art projects in addition to the capability of supporting a
wide range of sampling and random walk algorithms.Comment: 12 pages,IEEE Proceedings of the International Conference for High
Performance Computing, Networking, Storage and Analysis (SC20