14,878 research outputs found
Latent Relational Metric Learning via Memory-based Attention for Collaborative Ranking
This paper proposes a new neural architecture for collaborative ranking with
implicit feedback. Our model, LRML (\textit{Latent Relational Metric Learning})
is a novel metric learning approach for recommendation. More specifically,
instead of simple push-pull mechanisms between user and item pairs, we propose
to learn latent relations that describe each user item interaction. This helps
to alleviate the potential geometric inflexibility of existing metric learing
approaches. This enables not only better performance but also a greater extent
of modeling capability, allowing our model to scale to a larger number of
interactions. In order to do so, we employ a augmented memory module and learn
to attend over these memory blocks to construct latent relations. The
memory-based attention module is controlled by the user-item interaction,
making the learned relation vector specific to each user-item pair. Hence, this
can be interpreted as learning an exclusive and optimal relational translation
for each user-item interaction. The proposed architecture demonstrates the
state-of-the-art performance across multiple recommendation benchmarks. LRML
outperforms other metric learning models by in terms of Hits@10 and
nDCG@10 on large datasets such as Netflix and MovieLens20M. Moreover,
qualitative studies also demonstrate evidence that our proposed model is able
to infer and encode explicit sentiment, temporal and attribute information
despite being only trained on implicit feedback. As such, this ascertains the
ability of LRML to uncover hidden relational structure within implicit
datasets.Comment: WWW 201
Lifelong Sequential Modeling with Personalized Memorization for User Response Prediction
User response prediction, which models the user preference w.r.t. the
presented items, plays a key role in online services. With two-decade rapid
development, nowadays the cumulated user behavior sequences on mature Internet
service platforms have become extremely long since the user's first
registration. Each user not only has intrinsic tastes, but also keeps changing
her personal interests during lifetime. Hence, it is challenging to handle such
lifelong sequential modeling for each individual user. Existing methodologies
for sequential modeling are only capable of dealing with relatively recent user
behaviors, which leaves huge space for modeling long-term especially lifelong
sequential patterns to facilitate user modeling. Moreover, one user's behavior
may be accounted for various previous behaviors within her whole online
activity history, i.e., long-term dependency with multi-scale sequential
patterns. In order to tackle these challenges, in this paper, we propose a
Hierarchical Periodic Memory Network for lifelong sequential modeling with
personalized memorization of sequential patterns for each user. The model also
adopts a hierarchical and periodical updating mechanism to capture multi-scale
sequential patterns of user interests while supporting the evolving user
behavior logs. The experimental results over three large-scale real-world
datasets have demonstrated the advantages of our proposed model with
significant improvement in user response prediction performance against the
state-of-the-arts.Comment: SIGIR 2019. Reproducible codes and datasets:
https://github.com/alimamarankgroup/HPM
Memory Augmented Graph Neural Networks for Sequential Recommendation
The chronological order of user-item interactions can reveal time-evolving
and sequential user behaviors in many recommender systems. The items that users
will interact with may depend on the items accessed in the past. However, the
substantial increase of users and items makes sequential recommender systems
still face non-trivial challenges: (1) the hardness of modeling the short-term
user interests; (2) the difficulty of capturing the long-term user interests;
(3) the effective modeling of item co-occurrence patterns. To tackle these
challenges, we propose a memory augmented graph neural network (MA-GNN) to
capture both the long- and short-term user interests. Specifically, we apply a
graph neural network to model the item contextual information within a
short-term period and utilize a shared memory network to capture the long-range
dependencies between items. In addition to the modeling of user interests, we
employ a bilinear function to capture the co-occurrence patterns of related
items. We extensively evaluate our model on five real-world datasets, comparing
with several state-of-the-art methods and using a variety of performance
metrics. The experimental results demonstrate the effectiveness of our model
for the task of Top-K sequential recommendation.Comment: Accepted by the 34th AAAI Conference on Artificial Intelligence (AAAI
2020
RUEL: Retrieval-Augmented User Representation with Edge Browser Logs for Sequential Recommendation
Online recommender systems (RS) aim to match user needs with the vast amount
of resources available on various platforms. A key challenge is to model user
preferences accurately under the condition of data sparsity. To address this
challenge, some methods have leveraged external user behavior data from
multiple platforms to enrich user representation. However, all of these methods
require a consistent user ID across platforms and ignore the information from
similar users. In this study, we propose RUEL, a novel retrieval-based
sequential recommender that can effectively incorporate external anonymous user
behavior data from Edge browser logs to enhance recommendation. We first
collect and preprocess a large volume of Edge browser logs over a one-year
period and link them to target entities that correspond to candidate items in
recommendation datasets. We then design a contrastive learning framework with a
momentum encoder and a memory bank to retrieve the most relevant and diverse
browsing sequences from the full browsing log based on the semantic similarity
between user representations. After retrieval, we apply an item-level attentive
selector to filter out noisy items and generate refined sequence embeddings for
the final predictor. RUEL is the first method that connects user browsing data
with typical recommendation datasets and can be generalized to various
recommendation scenarios and datasets. We conduct extensive experiments on four
real datasets for sequential recommendation tasks and demonstrate that RUEL
significantly outperforms state-of-the-art baselines. We also conduct ablation
studies and qualitative analysis to validate the effectiveness of each
component of RUEL and provide additional insights into our method.Comment: CIKM 2023 AD
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