9,641 research outputs found
Incorporating User Micro-behaviors and Item Knowledge into Multi-task Learning for Session-based Recommendation
Session-based recommendation (SR) has become an important and popular
component of various e-commerce platforms, which aims to predict the next
interacted item based on a given session. Most of existing SR models only focus
on exploiting the consecutive items in a session interacted by a certain user,
to capture the transition pattern among the items. Although some of them have
been proven effective, the following two insights are often neglected. First, a
user's micro-behaviors, such as the manner in which the user locates an item,
the activities that the user commits on an item (e.g., reading comments, adding
to cart), offer fine-grained and deep understanding of the user's preference.
Second, the item attributes, also known as item knowledge, provide side
information to model the transition pattern among interacted items and
alleviate the data sparsity problem. These insights motivate us to propose a
novel SR model MKM-SR in this paper, which incorporates user Micro-behaviors
and item Knowledge into Multi-task learning for Session-based Recommendation.
Specifically, a given session is modeled on micro-behavior level in MKM-SR,
i.e., with a sequence of item-operation pairs rather than a sequence of items,
to capture the transition pattern in the session sufficiently. Furthermore, we
propose a multi-task learning paradigm to involve learning knowledge embeddings
which plays a role as an auxiliary task to promote the major task of SR. It
enables our model to obtain better session representations, resulting in more
precise SR recommendation results. The extensive evaluations on two benchmark
datasets demonstrate MKM-SR's superiority over the state-of-the-art SR models,
justifying the strategy of incorporating knowledge learning
Side Information-Driven Session-based Recommendation: A Survey
The session-based recommendation (SBR) garners increasing attention due to
its ability to predict anonymous user intents within limited interactions.
Emerging efforts incorporate various kinds of side information into their
methods for enhancing task performance. In this survey, we thoroughly review
the side information-driven session-based recommendation from a data-centric
perspective. Our survey commences with an illustration of the motivation and
necessity behind this research topic. This is followed by a detailed
exploration of various benchmarks rich in side information, pivotal for
advancing research in this field. Moreover, we delve into how these diverse
types of side information enhance SBR, underscoring their characteristics and
utility. A systematic review of research progress is then presented, offering
an analysis of the most recent and representative developments within this
topic. Finally, we present the future prospects of this vibrant topic.Comment: This is a survey on side information-driven session-based
recommendatio
Hierarchical Multi-Task Learning Framework for Session-based Recommendations
While session-based recommender systems (SBRSs) have shown superior
recommendation performance, multi-task learning (MTL) has been adopted by SBRSs
to enhance their prediction accuracy and generalizability further. Hierarchical
MTL (H-MTL) sets a hierarchical structure between prediction tasks and feeds
outputs from auxiliary tasks to main tasks. This hierarchy leads to richer
input features for main tasks and higher interpretability of predictions,
compared to existing MTL frameworks. However, the H-MTL framework has not been
investigated in SBRSs yet. In this paper, we propose HierSRec which
incorporates the H-MTL architecture into SBRSs. HierSRec encodes a given
session with a metadata-aware Transformer and performs next-category prediction
(i.e., auxiliary task) with the session encoding. Next, HierSRec conducts
next-item prediction (i.e., main task) with the category prediction result and
session encoding. For scalable inference, HierSRec creates a compact set of
candidate items (e.g., 4% of total items) per test example using the category
prediction. Experiments show that HierSRec outperforms existing SBRSs as per
next-item prediction accuracy on two session-based recommendation datasets. The
accuracy of HierSRec measured with the carefully-curated candidate items aligns
with the accuracy of HierSRec calculated with all items, which validates the
usefulness of our candidate generation scheme via H-MTL.Comment: Accepted at the 6th Workshop on Online Recommender Systems and User
Modeling @ ACM RecSys 202
Session-Based Recommendation by Exploiting Substitutable and Complementary Relationships from Multi-behavior Data
Session-based recommendation (SR) aims to dynamically recommend items to a
user based on a sequence of the most recent user-item interactions. Most
existing studies on SR adopt advanced deep learning methods. However, the
majority only consider a special behavior type (e.g., click), while those few
considering multi-typed behaviors ignore to take full advantage of the
relationships between products (items). In this case, the paper proposes a
novel approach, called Substitutable and Complementary Relationships from
Multi-behavior Data (denoted as SCRM) to better explore the relationships
between products for effective recommendation. Specifically, we firstly
construct substitutable and complementary graphs based on a user's sequential
behaviors in every session by jointly considering `click' and `purchase'
behaviors. We then design a denoising network to remove false relationships,
and further consider constraints on the two relationships via a particularly
designed loss function. Extensive experiments on two e-commerce datasets
demonstrate the superiority of our model over state-of-the-art methods, and the
effectiveness of every component in SCRM.Comment: 31 pages,11 figures, accepted by Data Mining and Knowledge
Discovery(2023
User Modeling and User Profiling: A Comprehensive Survey
The integration of artificial intelligence (AI) into daily life, particularly
through information retrieval and recommender systems, has necessitated
advanced user modeling and profiling techniques to deliver personalized
experiences. These techniques aim to construct accurate user representations
based on the rich amounts of data generated through interactions with these
systems. This paper presents a comprehensive survey of the current state,
evolution, and future directions of user modeling and profiling research. We
provide a historical overview, tracing the development from early stereotype
models to the latest deep learning techniques, and propose a novel taxonomy
that encompasses all active topics in this research area, including recent
trends. Our survey highlights the paradigm shifts towards more sophisticated
user profiling methods, emphasizing implicit data collection, multi-behavior
modeling, and the integration of graph data structures. We also address the
critical need for privacy-preserving techniques and the push towards
explainability and fairness in user modeling approaches. By examining the
definitions of core terminology, we aim to clarify ambiguities and foster a
clearer understanding of the field by proposing two novel encyclopedic
definitions of the main terms. Furthermore, we explore the application of user
modeling in various domains, such as fake news detection, cybersecurity, and
personalized education. This survey serves as a comprehensive resource for
researchers and practitioners, offering insights into the evolution of user
modeling and profiling and guiding the development of more personalized,
ethical, and effective AI systems.Comment: 71 page
Enhancing Item-level Bundle Representation for Bundle Recommendation
Bundle recommendation approaches offer users a set of related items on a
particular topic. The current state-of-the-art (SOTA) method utilizes
contrastive learning to learn representations at both the bundle and item
levels. However, due to the inherent difference between the bundle-level and
item-level preferences, the item-level representations may not receive
sufficient information from the bundle affiliations to make accurate
predictions. In this paper, we propose a novel approach EBRec, short of
Enhanced Bundle Recommendation, which incorporates two enhanced modules to
explore inherent item-level bundle representations. First, we propose to
incorporate the bundle-user-item (B-U-I) high-order correlations to explore
more collaborative information, thus to enhance the previous bundle
representation that solely relies on the bundle-item affiliation information.
Second, we further enhance the B-U-I correlations by augmenting the observed
user-item interactions with interactions generated from pre-trained models,
thus improving the item-level bundle representations. We conduct extensive
experiments on three public datasets, and the results justify the effectiveness
of our approach as well as the two core modules. Codes and datasets are
available at https://github.com/answermycode/EBRec
Micro-behavior encoding for session-based recommendation
Session-based recommendation (SR) aims to predict the next item for recommendation based on previously recorded sessions of user interaction. The majority of existing approaches to SR focus on modeling the transition patterns of items. In such models, the so-called micro-behaviors describing how the user locates an item and carries out various activities on it (e.g., click, add-to-cart, and read-comments), are simply ignored. A few recent studies have tried to incorporate the sequential patterns of micro-behaviors into SR models. However, those sequential models still cannot effectively capture all the inherent interdependencies between micro-behavior operations. In this work, we aim to investigate the effects of the micro-behavior information in SR systematically. Specifically, we identify two different patterns of micro-behaviors: ``sequential patterns'' and ``dyadic relational patterns''. To build a unified model of user micro-behaviors, we first devise a multigraph to aggregate the sequential patterns from different items via a graph neural network, and then utilize an extended self-attention network to exploit the pair-wise relational patterns of micro-behaviors. Extensive experiments on three public real-world datasets show the superiority of the proposed approach over the state-of-the-art baselines and confirm the usefulness of these two different micro-behavior patterns for SR
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