2,224 research outputs found
Exploiting Behavioral Consistence for Universal User Representation
User modeling is critical for developing personalized services in industry. A
common way for user modeling is to learn user representations that can be
distinguished by their interests or preferences. In this work, we focus on
developing universal user representation model. The obtained universal
representations are expected to contain rich information, and be applicable to
various downstream applications without further modifications (e.g., user
preference prediction and user profiling). Accordingly, we can be free from the
heavy work of training task-specific models for every downstream task as in
previous works. In specific, we propose Self-supervised User Modeling Network
(SUMN) to encode behavior data into the universal representation. It includes
two key components. The first one is a new learning objective, which guides the
model to fully identify and preserve valuable user information under a
self-supervised learning framework. The other one is a multi-hop aggregation
layer, which benefits the model capacity in aggregating diverse behaviors.
Extensive experiments on benchmark datasets show that our approach can
outperform state-of-the-art unsupervised representation methods, and even
compete with supervised ones.Comment: Preprint of accepted AAAI2021 pape
Towards Query Logs for Privacy Studies: On Deriving Search Queries from Questions
Translating verbose information needs into crisp search queries is a
phenomenon that is ubiquitous but hardly understood. Insights into this process
could be valuable in several applications, including synthesizing large
privacy-friendly query logs from public Web sources which are readily available
to the academic research community. In this work, we take a step towards
understanding query formulation by tapping into the rich potential of community
question answering (CQA) forums. Specifically, we sample natural language (NL)
questions spanning diverse themes from the Stack Exchange platform, and conduct
a large-scale conversion experiment where crowdworkers submit search queries
they would use when looking for equivalent information. We provide a careful
analysis of this data, accounting for possible sources of bias during
conversion, along with insights into user-specific linguistic patterns and
search behaviors. We release a dataset of 7,000 question-query pairs from this
study to facilitate further research on query understanding.Comment: ECIR 2020 Short Pape
PU GNN: Chargeback Fraud Detection in P2E MMORPGs via Graph Attention Networks with Imbalanced PU Labels
The recent advent of play-to-earn (P2E) systems in massively multiplayer
online role-playing games (MMORPGs) has made in-game goods interchangeable with
real-world values more than ever before. The goods in the P2E MMORPGs can be
directly exchanged with cryptocurrencies such as Bitcoin, Ethereum, or Klaytn
via blockchain networks. Unlike traditional in-game goods, once they had been
written to the blockchains, P2E goods cannot be restored by the game operation
teams even with chargeback fraud such as payment fraud, cancellation, or
refund. To tackle the problem, we propose a novel chargeback fraud prediction
method, PU GNN, which leverages graph attention networks with PU loss to
capture both the players' in-game behavior with P2E token transaction patterns.
With the adoption of modified GraphSMOTE, the proposed model handles the
imbalanced distribution of labels in chargeback fraud datasets. The conducted
experiments on three real-world P2E MMORPG datasets demonstrate that PU GNN
achieves superior performances over previously suggested methods.Comment: Under Review, Industry Trac
Query-LIFE: Query-aware Language Image Fusion Embedding for E-Commerce Relevance
Relevance module plays a fundamental role in e-commerce search as they are
responsible for selecting relevant products from thousands of items based on
user queries, thereby enhancing users experience and efficiency. The
traditional approach models the relevance based product titles and queries, but
the information in titles alone maybe insufficient to describe the products
completely. A more general optimization approach is to further leverage product
image information. In recent years, vision-language pre-training models have
achieved impressive results in many scenarios, which leverage contrastive
learning to map both textual and visual features into a joint embedding space.
In e-commerce, a common practice is to fine-tune on the pre-trained model based
on e-commerce data. However, the performance is sub-optimal because the
vision-language pre-training models lack of alignment specifically designed for
queries. In this paper, we propose a method called Query-LIFE (Query-aware
Language Image Fusion Embedding) to address these challenges. Query-LIFE
utilizes a query-based multimodal fusion to effectively incorporate the image
and title based on the product types. Additionally, it employs query-aware
modal alignment to enhance the accuracy of the comprehensive representation of
products. Furthermore, we design GenFilt, which utilizes the generation
capability of large models to filter out false negative samples and further
improve the overall performance of the contrastive learning task in the model.
Experiments have demonstrated that Query-LIFE outperforms existing baselines.
We have conducted ablation studies and human evaluations to validate the
effectiveness of each module within Query-LIFE. Moreover, Query-LIFE has been
deployed on Miravia Search, resulting in improved both relevance and conversion
efficiency
Analyzing Granger causality in climate data with time series classification methods
Attribution studies in climate science aim for scientifically ascertaining the influence of climatic variations on natural or anthropogenic factors. Many of those studies adopt the concept of Granger causality to infer statistical cause-effect relationships, while utilizing traditional autoregressive models. In this article, we investigate the potential of state-of-the-art time series classification techniques to enhance causal inference in climate science. We conduct a comparative experimental study of different types of algorithms on a large test suite that comprises a unique collection of datasets from the area of climate-vegetation dynamics. The results indicate that specialized time series classification methods are able to improve existing inference procedures. Substantial differences are observed among the methods that were tested
Leveraging Large Language Models in Conversational Recommender Systems
A Conversational Recommender System (CRS) offers increased transparency and
control to users by enabling them to engage with the system through a real-time
multi-turn dialogue. Recently, Large Language Models (LLMs) have exhibited an
unprecedented ability to converse naturally and incorporate world knowledge and
common-sense reasoning into language understanding, unlocking the potential of
this paradigm. However, effectively leveraging LLMs within a CRS introduces new
technical challenges, including properly understanding and controlling a
complex conversation and retrieving from external sources of information. These
issues are exacerbated by a large, evolving item corpus and a lack of
conversational data for training. In this paper, we provide a roadmap for
building an end-to-end large-scale CRS using LLMs. In particular, we propose
new implementations for user preference understanding, flexible dialogue
management and explainable recommendations as part of an integrated
architecture powered by LLMs. For improved personalization, we describe how an
LLM can consume interpretable natural language user profiles and use them to
modulate session-level context. To overcome conversational data limitations in
the absence of an existing production CRS, we propose techniques for building a
controllable LLM-based user simulator to generate synthetic conversations. As a
proof of concept we introduce RecLLM, a large-scale CRS for YouTube videos
built on LaMDA, and demonstrate its fluency and diverse functionality through
some illustrative example conversations
Large Language Model based Long-tail Query Rewriting in Taobao Search
In the realm of e-commerce search, the significance of semantic matching
cannot be overstated, as it directly impacts both user experience and company
revenue. Along this line, query rewriting, serving as an important technique to
bridge the semantic gaps inherent in the semantic matching process, has
attached wide attention from the industry and academia. However, existing query
rewriting methods often struggle to effectively optimize long-tail queries and
alleviate the phenomenon of "few-recall" caused by semantic gap. In this paper,
we present BEQUE, a comprehensive framework that Bridges the sEmantic gap for
long-tail QUEries. In detail, BEQUE comprises three stages: multi-instruction
supervised fine tuning (SFT), offline feedback, and objective alignment. We
first construct a rewriting dataset based on rejection sampling and auxiliary
tasks mixing to fine-tune our large language model (LLM) in a supervised
fashion. Subsequently, with the well-trained LLM, we employ beam search to
generate multiple candidate rewrites, and feed them into Taobao offline system
to obtain the partial order. Leveraging the partial order of rewrites, we
introduce a contrastive learning method to highlight the distinctions between
rewrites, and align the model with the Taobao online objectives. Offline
experiments prove the effectiveness of our method in bridging semantic gap.
Online A/B tests reveal that our method can significantly boost gross
merchandise volume (GMV), number of transaction (#Trans) and unique visitor
(UV) for long-tail queries. BEQUE has been deployed on Taobao, one of most
popular online shopping platforms in China, since October 2023.Comment: WWW Industry Under Revie
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