8,550 research outputs found
One Model for All: Large Language Models are Domain-Agnostic Recommendation Systems
The purpose of sequential recommendation is to utilize the interaction
history of a user and predict the next item that the user is most likely to
interact with. While data sparsity and cold start are two challenges that most
recommender systems are still facing, many efforts are devoted to utilizing
data from other domains, called cross-domain methods. However, general
cross-domain methods explore the relationship between two domains by designing
complex model architecture, making it difficult to scale to multiple domains
and utilize more data. Moreover, existing recommendation systems use IDs to
represent item, which carry less transferable signals in cross-domain
scenarios, and user cross-domain behaviors are also sparse, making it
challenging to learn item relationship from different domains. These problems
hinder the application of multi-domain methods to sequential recommendation.
Recently, large language models (LLMs) exhibit outstanding performance in world
knowledge learning from text corpora and general-purpose question answering.
Inspired by these successes, we propose a simple but effective framework for
domain-agnostic recommendation by exploiting the pre-trained LLMs (namely
LLM-Rec). We mix the user's behavior across different domains, and then
concatenate the title information of these items into a sentence and model the
user's behaviors with a pre-trained language model. We expect that by mixing
the user's behaviors across different domains, we can exploit the common
knowledge encoded in the pre-trained language model to alleviate the problems
of data sparsity and cold start problems. Furthermore, we are curious about
whether the latest technical advances in nature language processing (NLP) can
transfer to the recommendation scenarios.Comment: 10 pages, 7 figures, 6 table
Personalized Prompt for Sequential Recommendation
Pre-training models have shown their power in sequential recommendation.
Recently, prompt has been widely explored and verified for tuning in NLP
pre-training, which could help to more effectively and efficiently extract
useful knowledge from pre-training models for downstream tasks, especially in
cold-start scenarios. However, it is challenging to bring prompt-tuning from
NLP to recommendation, since the tokens in recommendation (i.e., items) do not
have explicit explainable semantics, and the sequence modeling should be
personalized. In this work, we first introduces prompt to recommendation and
propose a novel Personalized prompt-based recommendation (PPR) framework for
cold-start recommendation. Specifically, we build the personalized soft prefix
prompt via a prompt generator based on user profiles and enable a sufficient
training of prompts via a prompt-oriented contrastive learning with both
prompt- and behavior-based augmentations. We conduct extensive evaluations on
various tasks. In both few-shot and zero-shot recommendation, PPR models
achieve significant improvements over baselines on various metrics in three
large-scale open datasets. We also conduct ablation tests and sparsity analysis
for a better understanding of PPR. Moreover, We further verify PPR's
universality on different pre-training models, and conduct explorations on
PPR's other promising downstream tasks including cross-domain recommendation
and user profile prediction
Deep Interest Evolution Network for Click-Through Rate Prediction
Click-through rate~(CTR) prediction, whose goal is to estimate the
probability of the user clicks, has become one of the core tasks in advertising
systems. For CTR prediction model, it is necessary to capture the latent user
interest behind the user behavior data. Besides, considering the changing of
the external environment and the internal cognition, user interest evolves over
time dynamically. There are several CTR prediction methods for interest
modeling, while most of them regard the representation of behavior as the
interest directly, and lack specially modeling for latent interest behind the
concrete behavior. Moreover, few work consider the changing trend of interest.
In this paper, we propose a novel model, named Deep Interest Evolution
Network~(DIEN), for CTR prediction. Specifically, we design interest extractor
layer to capture temporal interests from history behavior sequence. At this
layer, we introduce an auxiliary loss to supervise interest extracting at each
step. As user interests are diverse, especially in the e-commerce system, we
propose interest evolving layer to capture interest evolving process that is
relative to the target item. At interest evolving layer, attention mechanism is
embedded into the sequential structure novelly, and the effects of relative
interests are strengthened during interest evolution. In the experiments on
both public and industrial datasets, DIEN significantly outperforms the
state-of-the-art solutions. Notably, DIEN has been deployed in the display
advertisement system of Taobao, and obtained 20.7\% improvement on CTR.Comment: 9 pages. Accepted by AAAI 201
Triple Sequence Learning for Cross-domain Recommendation
Cross-domain recommendation (CDR) aims to leverage the users' behaviors in
both source and target domains to improve the target domain's performance.
Conventional CDR methods typically explore the dual relations between the
source and target domains' behavior sequences. However, they ignore modeling
the third sequence of mixed behaviors that naturally reflects the user's global
preference. To address this issue, we present a novel and model-agnostic Triple
sequence learning for cross-domain recommendation (Tri-CDR) framework to
jointly model the source, target, and mixed behavior sequences in CDR.
Specifically, Tri-CDR independently models the hidden user representations for
the source, target, and mixed behavior sequences, and proposes a triple
cross-domain attention (TCA) to emphasize the informative knowledge related to
both user's target-domain preference and global interests in three sequences.
To comprehensively learn the triple correlations, we design a novel triple
contrastive learning (TCL) that jointly considers coarse-grained similarities
and fine-grained distinctions among three sequences, ensuring the alignment
while preserving the information diversity in multi-domain. We conduct
extensive experiments and analyses on two real-world datasets with four
domains. The significant improvements of Tri-CDR with different sequential
encoders on all datasets verify the effectiveness and universality. The source
code will be released in the future.Comment: 11 pages, 5 figures, under revie
MISSRec: Pre-training and Transferring Multi-modal Interest-aware Sequence Representation for Recommendation
The goal of sequential recommendation (SR) is to predict a user's potential
interested items based on her/his historical interaction sequences. Most
existing sequential recommenders are developed based on ID features, which,
despite their widespread use, often underperform with sparse IDs and struggle
with the cold-start problem. Besides, inconsistent ID mappings hinder the
model's transferability, isolating similar recommendation domains that could
have been co-optimized. This paper aims to address these issues by exploring
the potential of multi-modal information in learning robust and generalizable
sequence representations. We propose MISSRec, a multi-modal pre-training and
transfer learning framework for SR. On the user side, we design a
Transformer-based encoder-decoder model, where the contextual encoder learns to
capture the sequence-level multi-modal synergy while a novel interest-aware
decoder is developed to grasp item-modality-interest relations for better
sequence representation. On the candidate item side, we adopt a dynamic fusion
module to produce user-adaptive item representation, providing more precise
matching between users and items. We pre-train the model with contrastive
learning objectives and fine-tune it in an efficient manner. Extensive
experiments demonstrate the effectiveness and flexibility of MISSRec, promising
an practical solution for real-world recommendation scenarios.Comment: Accepted to ACM MM 202
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