563 research outputs found
Deep Learning based Recommender System: A Survey and New Perspectives
With the ever-growing volume of online information, recommender systems have
been an effective strategy to overcome such information overload. The utility
of recommender systems cannot be overstated, given its widespread adoption in
many web applications, along with its potential impact to ameliorate many
problems related to over-choice. In recent years, deep learning has garnered
considerable interest in many research fields such as computer vision and
natural language processing, owing not only to stellar performance but also the
attractive property of learning feature representations from scratch. The
influence of deep learning is also pervasive, recently demonstrating its
effectiveness when applied to information retrieval and recommender systems
research. Evidently, the field of deep learning in recommender system is
flourishing. This article aims to provide a comprehensive review of recent
research efforts on deep learning based recommender systems. More concretely,
we provide and devise a taxonomy of deep learning based recommendation models,
along with providing a comprehensive summary of the state-of-the-art. Finally,
we expand on current trends and provide new perspectives pertaining to this new
exciting development of the field.Comment: The paper has been accepted by ACM Computing Surveys.
https://doi.acm.org/10.1145/328502
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
Topic-enhanced memory networks for personalised point-of-interest recommendation
Point-of-Interest (POI) recommender systems play a vital role in people's
lives by recommending unexplored POIs to users and have drawn extensive
attention from both academia and industry. Despite their value, however, they
still suffer from the challenges of capturing complicated user preferences and
fine-grained user-POI relationship for spatio-temporal sensitive POI
recommendation. Existing recommendation algorithms, including both shallow and
deep approaches, usually embed the visiting records of a user into a single
latent vector to model user preferences: this has limited power of
representation and interpretability. In this paper, we propose a novel
topic-enhanced memory network (TEMN), a deep architecture to integrate the
topic model and memory network capitalising on the strengths of both the global
structure of latent patterns and local neighbourhood-based features in a
nonlinear fashion. We further incorporate a geographical module to exploit
user-specific spatial preference and POI-specific spatial influence to enhance
recommendations. The proposed unified hybrid model is widely applicable to
various POI recommendation scenarios. Extensive experiments on real-world
WeChat datasets demonstrate its effectiveness (improvement ratio of 3.25% and
29.95% for context-aware and sequential recommendation, respectively). Also,
qualitative analysis of the attention weights and topic modeling provides
insight into the model's recommendation process and results.China Scholarship Council and Cambridge Trus
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