74 research outputs found
Heterogeneous Knowledge Fusion: A Novel Approach for Personalized Recommendation via LLM
The analysis and mining of user heterogeneous behavior are of paramount
importance in recommendation systems. However, the conventional approach of
incorporating various types of heterogeneous behavior into recommendation
models leads to feature sparsity and knowledge fragmentation issues. To address
this challenge, we propose a novel approach for personalized recommendation via
Large Language Model (LLM), by extracting and fusing heterogeneous knowledge
from user heterogeneous behavior information. In addition, by combining
heterogeneous knowledge and recommendation tasks, instruction tuning is
performed on LLM for personalized recommendations. The experimental results
demonstrate that our method can effectively integrate user heterogeneous
behavior and significantly improve recommendation performance.Comment: Accepted at RecSys 202
Adaptive Collaborative Filtering with Personalized Time Decay Functions for Financial Product Recommendation
Classical recommender systems often assume that historical data are
stationary and fail to account for the dynamic nature of user preferences,
limiting their ability to provide reliable recommendations in time-sensitive
settings. This assumption is particularly problematic in finance, where
financial products exhibit continuous changes in valuations, leading to
frequent shifts in client interests. These evolving interests, summarized in
the past client-product interactions, see their utility fade over time with a
degree that might differ from one client to another. To address this challenge,
we propose a time-dependent collaborative filtering algorithm that can
adaptively discount distant client-product interactions using personalized
decay functions. Our approach is designed to handle the non-stationarity of
financial data and produce reliable recommendations by modeling the dynamic
collaborative signals between clients and products. We evaluate our method
using a proprietary dataset from BNP Paribas and demonstrate significant
improvements over state-of-the-art benchmarks from relevant literature. Our
findings emphasize the importance of incorporating time explicitly in the model
to enhance the accuracy of financial product recommendation.Comment: 10 pages, 1 figure, 2 tables, to be published in the Seventeenth ACM
Conference on Recommender Systems (RecSys '23
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
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