750 research outputs found
Hierarchical Transformer with Spatio-Temporal Context Aggregation for Next Point-of-Interest Recommendation
Next point-of-interest (POI) recommendation is a critical task in
location-based social networks, yet remains challenging due to a high degree of
variation and personalization exhibited in user movements. In this work, we
explore the latent hierarchical structure composed of multi-granularity
short-term structural patterns in user check-in sequences. We propose a
Spatio-Temporal context AggRegated Hierarchical Transformer (STAR-HiT) for next
POI recommendation, which employs stacked hierarchical encoders to recursively
encode the spatio-temporal context and explicitly locate subsequences of
different granularities. More specifically, in each encoder, the global
attention layer captures the spatio-temporal context of the sequence, while the
local attention layer performed within each subsequence enhances subsequence
modeling using the local context. The sequence partition layer infers positions
and lengths of subsequences from the global context adaptively, such that
semantics in subsequences can be well preserved. Finally, the subsequence
aggregation layer fuses representations within each subsequence to form the
corresponding subsequence representation, thereby generating a new sequence of
higher-level granularity. The stacking of encoders captures the latent
hierarchical structure of the check-in sequence, which is used to predict the
next visiting POI. Extensive experiments on three public datasets demonstrate
that the proposed model achieves superior performance whilst providing
explanations for recommendations. Codes are available at
https://github.com/JennyXieJiayi/STAR-HiT
Multi-Relational Contrastive Learning for Recommendation
Personalized recommender systems play a crucial role in capturing users'
evolving preferences over time to provide accurate and effective
recommendations on various online platforms. However, many recommendation
models rely on a single type of behavior learning, which limits their ability
to represent the complex relationships between users and items in real-life
scenarios. In such situations, users interact with items in multiple ways,
including clicking, tagging as favorite, reviewing, and purchasing. To address
this issue, we propose the Relation-aware Contrastive Learning (RCL) framework,
which effectively models dynamic interaction heterogeneity. The RCL model
incorporates a multi-relational graph encoder that captures short-term
preference heterogeneity while preserving the dedicated relation semantics for
different types of user-item interactions. Moreover, we design a dynamic
cross-relational memory network that enables the RCL model to capture users'
long-term multi-behavior preferences and the underlying evolving cross-type
behavior dependencies over time. To obtain robust and informative user
representations with both commonality and diversity across multi-behavior
interactions, we introduce a multi-relational contrastive learning paradigm
with heterogeneous short- and long-term interest modeling. Our extensive
experimental studies on several real-world datasets demonstrate the superiority
of the RCL recommender system over various state-of-the-art baselines in terms
of recommendation accuracy and effectiveness.Comment: This paper has been published as a full paper at RecSys 202
A personality aware recommendation system
Les systèmes de recommandation conversationnels (CRSs) sont des systèmes qui fournissent
des recommandations personnalisées par le biais d’une session de dialogue en langage
naturel avec les utilisateurs. Contrairement aux systèmes de recommandation traditionnels
qui ne prennent comme vérité de base que les préférences anciennes des utilisateurs, les
CRS impliquent aussi les préférences actuelles des utilisateurs durant la conversation. Des
recherches récentes montrent que la compréhension de la signification contextuelle des
préférences des utilisateurs et des dialogues peut améliorer de manière significative les
performances du système de recommandation. Des chercheurs ont également montré un
lien fort entre les traits de personnalité des utilisateurs et les systèmes de recommandation.
La personnalité et les préférences sont des variables essentielles en sciences sociales. Elles
décrivent les différences entre les personnes, que ce soit au niveau individuel ou collectif.
Les approches récentes de recommandation basées sur la personnalité sont des systèmes non
conversationnels. Par conséquent, il est extrêmement important de détecter et d’utiliser les
traits de personnalité des individus dans les systèmes conversationnels afin d’assurer une
performance de recommandation et de dialogue plus personnalisée. Pour ce faire, ce travail
propose un système de recommandation conversationnel sensible à la personnalité qui est
basé sur des modules qui assurent une session de dialogue et recommandation personnalisée
en utilisant les traits de personnalité des utilisateurs. Nous proposons également une
nouvelle approche de détection de la personnalité, qui est un modèle de langage spécifique
au contexte pour détecter les traits des individus en utilisant leurs données publiées sur les
réseaux sociaux. Les résultats montrent que notre système proposé a surpassé les approches
existantes dans différentes mesures.A Conversational Recommendation System (CRS) is a system that provides personalized
recommendations through a session of natural language dialogue turns with users. Unlike
traditional one-shot recommendation systems, which only assume the user’s previous
preferences as the ground truth, CRS uses both previous and current user preferences.
Recent research shows that understanding the contextual meaning of user preferences and
dialogue turns can significantly improve recommendation performance. It also shows a
strong link between users’ personality traits and recommendation systems. Personality
and preferences are essential variables in computational sociology and social science.
They describe the differences between people, both at the individual and collective level.
Recent personality-based recommendation approaches are traditional one-shot systems, or
“non conversational systems”. Therefore, there is a significant need to detect and employ
individuals’ personality traits within the CRS paradigm to ensure a better and more
personalized dialogue recommendation performance.
Driven by the aforementioned facts, this study proposes a modularized, personality-
aware CRS that ensures a personalized dialogue recommendation session using the users’
personality traits. We also propose a novel personality detection approach, which is a
context-specific language model for detecting individuals’ personality traits using their
social media data. The goal is to create a personality-aware and topic-guided CRS model
that performs better than the standard CRS models. Experimental results show that our
personality-aware conversation recommendation system has outperformed state-of-the-art
approaches in different considered metrics on the topic-guided conversation recommendation
dataset
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