9 research outputs found
Finding Subcube Heavy Hitters in Analytics Data Streams
Data streams typically have items of large number of dimensions. We study the
fundamental heavy-hitters problem in this setting. Formally, the data stream
consists of -dimensional items . A -dimensional
subcube is a subset of distinct coordinates . A subcube heavy hitter query , , outputs
YES if and NO if , where is the
ratio of number of stream items whose coordinates have joint values .
The all subcube heavy hitters query outputs all joint
values that return YES to . The one dimensional version
of this problem where was heavily studied in data stream theory,
databases, networking and signal processing. The subcube heavy hitters problem
is applicable in all these cases.
We present a simple reservoir sampling based one-pass streaming algorithm to
solve the subcube heavy hitters problem in space. This
is optimal up to poly-logarithmic factors given the established lower bound. In
the worst case, this is which is prohibitive for large
, and our goal is to circumvent this quadratic bottleneck.
Our main contribution is a model-based approach to the subcube heavy hitters
problem. In particular, we assume that the dimensions are related to each other
via the Naive Bayes model, with or without a latent dimension. Under this
assumption, we present a new two-pass, -space algorithm
for our problem, and a fast algorithm for answering in
time. Our work develops the direction of model-based data
stream analysis, with much that remains to be explored.Comment: To appear in WWW 201
Reinforcement Knowledge Graph Reasoning for Explainable Recommendation
Recent advances in personalized recommendation have sparked great interest in
the exploitation of rich structured information provided by knowledge graphs.
Unlike most existing approaches that only focus on leveraging knowledge graphs
for more accurate recommendation, we perform explicit reasoning with knowledge
for decision making so that the recommendations are generated and supported by
an interpretable causal inference procedure. To this end, we propose a method
called Policy-Guided Path Reasoning (PGPR), which couples recommendation and
interpretability by providing actual paths in a knowledge graph. Our
contributions include four aspects. We first highlight the significance of
incorporating knowledge graphs into recommendation to formally define and
interpret the reasoning process. Second, we propose a reinforcement learning
(RL) approach featuring an innovative soft reward strategy, user-conditional
action pruning and a multi-hop scoring function. Third, we design a
policy-guided graph search algorithm to efficiently and effectively sample
reasoning paths for recommendation. Finally, we extensively evaluate our method
on several large-scale real-world benchmark datasets, obtaining favorable
results compared with state-of-the-art methods.Comment: Accepted in SIGIR 201
ABSent: Cross-Lingual Sentence Representation Mapping with Bidirectional GANs
A number of cross-lingual transfer learning approaches based on neural
networks have been proposed for the case when large amounts of parallel text
are at our disposal. However, in many real-world settings, the size of parallel
annotated training data is restricted. Additionally, prior cross-lingual
mapping research has mainly focused on the word level. This raises the question
of whether such techniques can also be applied to effortlessly obtain
cross-lingually aligned sentence representations. To this end, we propose an
Adversarial Bi-directional Sentence Embedding Mapping (ABSent) framework, which
learns mappings of cross-lingual sentence representations from limited
quantities of parallel data
CAFE: Coarse-to-Fine Neural Symbolic Reasoning for Explainable Recommendation
Recent research explores incorporating knowledge graphs (KG) into e-commerce
recommender systems, not only to achieve better recommendation performance, but
more importantly to generate explanations of why particular decisions are made.
This can be achieved by explicit KG reasoning, where a model starts from a user
node, sequentially determines the next step, and walks towards an item node of
potential interest to the user. However, this is challenging due to the huge
search space, unknown destination, and sparse signals over the KG, so
informative and effective guidance is needed to achieve a satisfactory
recommendation quality. To this end, we propose a CoArse-to-FinE neural
symbolic reasoning approach (CAFE). It first generates user profiles as coarse
sketches of user behaviors, which subsequently guide a path-finding process to
derive reasoning paths for recommendations as fine-grained predictions. User
profiles can capture prominent user behaviors from the history, and provide
valuable signals about which kinds of path patterns are more likely to lead to
potential items of interest for the user. To better exploit the user profiles,
an improved path-finding algorithm called Profile-guided Path Reasoning (PPR)
is also developed, which leverages an inventory of neural symbolic reasoning
modules to effectively and efficiently find a batch of paths over a large-scale
KG. We extensively experiment on four real-world benchmarks and observe
substantial gains in the recommendation performance compared with
state-of-the-art methods.Comment: Accepted in CIKM 202
Semantic Search with Information Integration
Since the search engine was first released in 1993, the development has never been slow down and various search engines emerged to vied for popularity. However, current traditional search engines like Google and Yahoo! are based on key words which lead to results impreciseness and information redundancy. A new search engine with semantic analysis can be the alternate solution in the future. It is more intelligent and informative, and provides better interaction with users. Â Â Â Â Â Â This thesis discusses the detail on semantic search, explains advantages of semantic search over other key-word-based search and introduces how to integrate semantic analysis with common search engines. At the end of this thesis, there is an example of implementation of a simple semantic search engine