3,399 research outputs found
Deep Learning based Recommender System: A Survey and New Perspectives
With the ever-growing volume of online information, recommender systems have
been an effective strategy to overcome such information overload. The utility
of recommender systems cannot be overstated, given its widespread adoption in
many web applications, along with its potential impact to ameliorate many
problems related to over-choice. In recent years, deep learning has garnered
considerable interest in many research fields such as computer vision and
natural language processing, owing not only to stellar performance but also the
attractive property of learning feature representations from scratch. The
influence of deep learning is also pervasive, recently demonstrating its
effectiveness when applied to information retrieval and recommender systems
research. Evidently, the field of deep learning in recommender system is
flourishing. This article aims to provide a comprehensive review of recent
research efforts on deep learning based recommender systems. More concretely,
we provide and devise a taxonomy of deep learning based recommendation models,
along with providing a comprehensive summary of the state-of-the-art. Finally,
we expand on current trends and provide new perspectives pertaining to this new
exciting development of the field.Comment: The paper has been accepted by ACM Computing Surveys.
https://doi.acm.org/10.1145/328502
Trie-NLG: Trie Context Augmentation to Improve Personalized Query Auto-Completion for Short and Unseen Prefixes
Query auto-completion (QAC) aims at suggesting plausible completions for a
given query prefix. Traditionally, QAC systems have leveraged tries curated
from historical query logs to suggest most popular completions. In this
context, there are two specific scenarios that are difficult to handle for any
QAC system: short prefixes (which are inherently ambiguous) and unseen
prefixes. Recently, personalized Natural Language Generation (NLG) models have
been proposed to leverage previous session queries as context for addressing
these two challenges. However, such NLG models suffer from two drawbacks: (1)
some of the previous session queries could be noisy and irrelevant to the user
intent for the current prefix, and (2) NLG models cannot directly incorporate
historical query popularity. This motivates us to propose a novel NLG model for
QAC, Trie-NLG, which jointly leverages popularity signals from trie and
personalization signals from previous session queries. We train the Trie-NLG
model by augmenting the prefix with rich context comprising of recent session
queries and top trie completions. This simple modeling approach overcomes the
limitations of trie-based and NLG-based approaches and leads to
state-of-the-art performance. We evaluate the Trie-NLG model using two large
QAC datasets. On average, our model achieves huge ~57% and ~14% boost in MRR
over the popular trie-based lookup and the strong BART-based baseline methods,
respectively. We make our code publicly available.Comment: Accepted at Journal Track of ECML-PKDD 202
Knowledge-Augmented Large Language Models for Personalized Contextual Query Suggestion
Large Language Models (LLMs) excel at tackling various natural language
tasks. However, due to the significant costs involved in re-training or
fine-tuning them, they remain largely static and difficult to personalize.
Nevertheless, a variety of applications could benefit from generations that are
tailored to users' preferences, goals, and knowledge. Among them is web search,
where knowing what a user is trying to accomplish, what they care about, and
what they know can lead to improved search experiences. In this work, we
propose a novel and general approach that augments an LLM with relevant context
from users' interaction histories with a search engine in order to personalize
its outputs. Specifically, we construct an entity-centric knowledge store for
each user based on their search and browsing activities on the web, which is
then leveraged to provide contextually relevant LLM prompt augmentations. This
knowledge store is light-weight, since it only produces user-specific aggregate
projections of interests and knowledge onto public knowledge graphs, and
leverages existing search log infrastructure, thereby mitigating the privacy,
compliance, and scalability concerns associated with building deep user
profiles for personalization. We then validate our approach on the task of
contextual query suggestion, which requires understanding not only the user's
current search context but also what they historically know and care about.
Through a number of experiments based on human evaluation, we show that our
approach is significantly better than several other LLM-powered baselines,
generating query suggestions that are contextually more relevant, personalized,
and useful
Recommending on graphs: a comprehensive review from a data perspective
Recent advances in graph-based learning approaches have demonstrated their
effectiveness in modelling users' preferences and items' characteristics for
Recommender Systems (RSS). Most of the data in RSS can be organized into graphs
where various objects (e.g., users, items, and attributes) are explicitly or
implicitly connected and influence each other via various relations. Such a
graph-based organization brings benefits to exploiting potential properties in
graph learning (e.g., random walk and network embedding) techniques to enrich
the representations of the user and item nodes, which is an essential factor
for successful recommendations. In this paper, we provide a comprehensive
survey of Graph Learning-based Recommender Systems (GLRSs). Specifically, we
start from a data-driven perspective to systematically categorize various
graphs in GLRSs and analyze their characteristics. Then, we discuss the
state-of-the-art frameworks with a focus on the graph learning module and how
they address practical recommendation challenges such as scalability, fairness,
diversity, explainability and so on. Finally, we share some potential research
directions in this rapidly growing area.Comment: Accepted by UMUA
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