12 research outputs found
Probabilistic Latent Factor Model for Collaborative Filtering with Bayesian Inference
Latent Factor Model (LFM) is one of the most successful methods for
Collaborative filtering (CF) in the recommendation system, in which both users
and items are projected into a joint latent factor space. Base on matrix
factorization applied usually in pattern recognition, LFM models user-item
interactions as inner products of factor vectors of user and item in that space
and can be efficiently solved by least square methods with optimal estimation.
However, such optimal estimation methods are prone to overfitting due to the
extreme sparsity of user-item interactions. In this paper, we propose a
Bayesian treatment for LFM, named Bayesian Latent Factor Model (BLFM). Based on
observed user-item interactions, we build a probabilistic factor model in which
the regularization is introduced via placing prior constraint on latent
factors, and the likelihood function is established over observations and
parameters. Then we draw samples of latent factors from the posterior
distribution with Variational Inference (VI) to predict expected value. We
further make an extension to BLFM, called BLFMBias, incorporating
user-dependent and item-dependent biases into the model for enhancing
performance. Extensive experiments on the movie rating dataset show the
effectiveness of our proposed models by compared with several strong baselines.Comment: 8 pages, 5 figures, ICPR2020 conferenc
Knowledge-Enhanced Top-K Recommendation in Poincar\'e Ball
Personalized recommender systems are increasingly important as more content
and services become available and users struggle to identify what might
interest them. Thanks to the ability for providing rich information, knowledge
graphs (KGs) are being incorporated to enhance the recommendation performance
and interpretability. To effectively make use of the knowledge graph, we
propose a recommendation model in the hyperbolic space, which facilitates the
learning of the hierarchical structure of knowledge graphs. Furthermore, a
hyperbolic attention network is employed to determine the relative importances
of neighboring entities of a certain item. In addition, we propose an adaptive
and fine-grained regularization mechanism to adaptively regularize items and
their neighboring representations. Via a comparison using three real-world
datasets with state-of-the-art methods, we show that the proposed model
outperforms the best existing models by 2-16% in terms of NDCG@K on Top-K
recommendation.Comment: Accepted by the 35th AAAI Conference on Artificial Intelligence (AAAI
2021
GraphSAIL: Graph Structure Aware Incremental Learning for Recommender Systems
Given the convenience of collecting information through online services,
recommender systems now consume large scale data and play a more important role
in improving user experience. With the recent emergence of Graph Neural
Networks (GNNs), GNN-based recommender models have shown the advantage of
modeling the recommender system as a user-item bipartite graph to learn
representations of users and items. However, such models are expensive to train
and difficult to perform frequent updates to provide the most up-to-date
recommendations. In this work, we propose to update GNN-based recommender
models incrementally so that the computation time can be greatly reduced and
models can be updated more frequently. We develop a Graph Structure Aware
Incremental Learning framework, GraphSAIL, to address the commonly experienced
catastrophic forgetting problem that occurs when training a model in an
incremental fashion. Our approach preserves a user's long-term preference (or
an item's long-term property) during incremental model updating. GraphSAIL
implements a graph structure preservation strategy which explicitly preserves
each node's local structure, global structure, and self-information,
respectively. We argue that our incremental training framework is the first
attempt tailored for GNN based recommender systems and demonstrate its
improvement compared to other incremental learning techniques on two public
datasets. We further verify the effectiveness of our framework on a large-scale
industrial dataset.Comment: Accepted by CIKM2020 Applied Research Trac
GAG: Global Attributed Graph Neural Network for Streaming Session-based Recommendation
Streaming session-based recommendation (SSR) is a challenging task that
requires the recommender system to do the session-based recommendation (SR) in
the streaming scenario. In the real-world applications of e-commerce and social
media, a sequence of user-item interactions generated within a certain period
are grouped as a session, and these sessions consecutively arrive in the form
of streams. Most of the recent SR research has focused on the static setting
where the training data is first acquired and then used to train a
session-based recommender model. They need several epochs of training over the
whole dataset, which is infeasible in the streaming setting. Besides, they can
hardly well capture long-term user interests because of the neglect or the
simple usage of the user information. Although some streaming recommendation
strategies have been proposed recently, they are designed for streams of
individual interactions rather than streams of sessions. In this paper, we
propose a Global Attributed Graph (GAG) neural network model with a Wasserstein
reservoir for the SSR problem. On one hand, when a new session arrives, a
session graph with a global attribute is constructed based on the current
session and its associate user. Thus, the GAG can take both the global
attribute and the current session into consideration to learn more
comprehensive representations of the session and the user, yielding a better
performance in the recommendation. On the other hand, for the adaptation to the
streaming session scenario, a Wasserstein reservoir is proposed to help
preserve a representative sketch of the historical data. Extensive experiments
on two real-world datasets have been conducted to verify the superiority of the
GAG model compared with the state-of-the-art methods