29 research outputs found
Dependency Relationships-Enhanced Attentive Group Recommendation in HINs
Recommending suitable items to a group of users, commonly referred to as the
group recommendation task, is becoming increasingly urgent with the development
of group activities. The challenges within the group recommendation task
involve aggregating the individual preferences of group members as the group's
preferences and facing serious sparsity problems due to the lack of
user/group-item interactions. To solve these problems, we propose a novel
approach called Dependency Relationships-Enhanced Attentive Group
Recommendation (DREAGR) for the recommendation task of occasional groups.
Specifically, we introduce the dependency relationship between items as side
information to enhance the user/group-item interaction and alleviate the
interaction sparsity problem. Then, we propose a Path-Aware Attention Embedding
(PAAE) method to model users' preferences on different types of paths. Next, we
design a gated fusion mechanism to fuse users' preferences into their
comprehensive preferences. Finally, we develop an attention aggregator that
aggregates users' preferences as the group's preferences for the group
recommendation task. We conducted experiments on two datasets to demonstrate
the superiority of DREAGR by comparing it with state-of-the-art group
recommender models. The experimental results show that DREAGR outperforms other
models, especially HR@N and NDCG@N (N=5, 10), where DREAGR has improved in the
range of 3.64% to 7.01% and 2.57% to 3.39% on both datasets, respectively.Comment: 14 pages, 9 figures, This paper has been submitted to IEEE
Transactions on Knowledge and Data Engineerin
Multi-Granularity Attention Model for Group Recommendation
Group recommendation provides personalized recommendations to a group of
users based on their shared interests, preferences, and characteristics.
Current studies have explored different methods for integrating individual
preferences and making collective decisions that benefit the group as a whole.
However, most of them heavily rely on users with rich behavior and ignore
latent preferences of users with relatively sparse behavior, leading to
insufficient learning of individual interests. To address this challenge, we
present the Multi-Granularity Attention Model (MGAM), a novel approach that
utilizes multiple levels of granularity (i.e., subsets, groups, and supersets)
to uncover group members' latent preferences and mitigate recommendation noise.
Specially, we propose a Subset Preference Extraction module that enhances the
representation of users' latent subset-level preferences by incorporating their
previous interactions with items and utilizing a hierarchical mechanism.
Additionally, our method introduces a Group Preference Extraction module and a
Superset Preference Extraction module, which explore users' latent preferences
on two levels: the group-level, which maintains users' original preferences,
and the superset-level, which includes group-group exterior information. By
incorporating the subset-level embedding, group-level embedding, and
superset-level embedding, our proposed method effectively reduces group
recommendation noise across multiple granularities and comprehensively learns
individual interests. Extensive offline and online experiments have
demonstrated the superiority of our method in terms of performance
Thinking inside The Box: Learning Hypercube Representations for Group Recommendation
As a step beyond traditional personalized recommendation, group
recommendation is the task of suggesting items that can satisfy a group of
users. In group recommendation, the core is to design preference aggregation
functions to obtain a quality summary of all group members' preferences. Such
user and group preferences are commonly represented as points in the vector
space (i.e., embeddings), where multiple user embeddings are compressed into
one to facilitate ranking for group-item pairs. However, the resulted group
representations, as points, lack adequate flexibility and capacity to account
for the multi-faceted user preferences. Also, the point embedding-based
preference aggregation is a less faithful reflection of a group's
decision-making process, where all users have to agree on a certain value in
each embedding dimension instead of a negotiable interval. In this paper, we
propose a novel representation of groups via the notion of hypercubes, which
are subspaces containing innumerable points in the vector space. Specifically,
we design the hypercube recommender (CubeRec) to adaptively learn group
hypercubes from user embeddings with minimal information loss during preference
aggregation, and to leverage a revamped distance metric to measure the affinity
between group hypercubes and item points. Moreover, to counteract the
long-standing issue of data sparsity in group recommendation, we make full use
of the geometric expressiveness of hypercubes and innovatively incorporate
self-supervision by intersecting two groups. Experiments on four real-world
datasets have validated the superiority of CubeRec over state-of-the-art
baselines.Comment: To appear in SIGIR'2
Attentive Aspect Modeling for Review-aware Recommendation
In recent years, many studies extract aspects from user reviews and integrate
them with ratings for improving the recommendation performance. The common
aspects mentioned in a user's reviews and a product's reviews indicate indirect
connections between the user and product. However, these aspect-based methods
suffer from two problems. First, the common aspects are usually very sparse,
which is caused by the sparsity of user-product interactions and the diversity
of individual users' vocabularies. Second, a user's interests on aspects could
be different with respect to different products, which are usually assumed to
be static in existing methods. In this paper, we propose an Attentive
Aspect-based Recommendation Model (AARM) to tackle these challenges. For the
first problem, to enrich the aspect connections between user and product,
besides common aspects, AARM also models the interactions between synonymous
and similar aspects. For the second problem, a neural attention network which
simultaneously considers user, product and aspect information is constructed to
capture a user's attention towards aspects when examining different products.
Extensive quantitative and qualitative experiments show that AARM can
effectively alleviate the two aforementioned problems and significantly
outperforms several state-of-the-art recommendation methods on top-N
recommendation task.Comment: Camera-ready manuscript for TOI
UNICON: A unified framework for behavior-based consumer segmentation in e-commerce
Data-driven personalization is a key practice in fashion e-commerce,
improving the way businesses serve their consumers needs with more relevant
content. While hyper-personalization offers highly targeted experiences to each
consumer, it requires a significant amount of private data to create an
individualized journey. To alleviate this, group-based personalization provides
a moderate level of personalization built on broader common preferences of a
consumer segment, while still being able to personalize the results. We
introduce UNICON, a unified deep learning consumer segmentation framework that
leverages rich consumer behavior data to learn long-term latent representations
and utilizes them to extract two pivotal types of segmentation catering various
personalization use-cases: lookalike, expanding a predefined target seed
segment with consumers of similar behavior, and data-driven, revealing
non-obvious consumer segments with similar affinities. We demonstrate through
extensive experimentation our framework effectiveness in fashion to identify
lookalike Designer audience and data-driven style segments. Furthermore, we
present experiments that showcase how segment information can be incorporated
in a hybrid recommender system combining hyper and group-based personalization
to exploit the advantages of both alternatives and provide improvements on
consumer experience