204 research outputs found
Hybrid Collaborative Filtering with Autoencoders
Collaborative Filtering aims at exploiting the feedback of users to provide
personalised recommendations. Such algorithms look for latent variables in a
large sparse matrix of ratings. They can be enhanced by adding side information
to tackle the well-known cold start problem. While Neu-ral Networks have
tremendous success in image and speech recognition, they have received less
attention in Collaborative Filtering. This is all the more surprising that
Neural Networks are able to discover latent variables in large and
heterogeneous datasets. In this paper, we introduce a Collaborative Filtering
Neural network architecture aka CFN which computes a non-linear Matrix
Factorization from sparse rating inputs and side information. We show
experimentally on the MovieLens and Douban dataset that CFN outper-forms the
state of the art and benefits from side information. We provide an
implementation of the algorithm as a reusable plugin for Torch, a popular
Neural Network framework
Data augmentation for recommender system: A semi-supervised approach using maximum margin matrix factorization
Collaborative filtering (CF) has become a popular method for developing
recommender systems (RS) where ratings of a user for new items is predicted
based on her past preferences and available preference information of other
users. Despite the popularity of CF-based methods, their performance is often
greatly limited by the sparsity of observed entries. In this study, we explore
the data augmentation and refinement aspects of Maximum Margin Matrix
Factorization (MMMF), a widely accepted CF technique for the rating
predictions, which have not been investigated before. We exploit the inherent
characteristics of CF algorithms to assess the confidence level of individual
ratings and propose a semi-supervised approach for rating augmentation based on
self-training. We hypothesize that any CF algorithm's predictions with low
confidence are due to some deficiency in the training data and hence, the
performance of the algorithm can be improved by adopting a systematic data
augmentation strategy. We iteratively use some of the ratings predicted with
high confidence to augment the training data and remove low-confidence entries
through a refinement process. By repeating this process, the system learns to
improve prediction accuracy. Our method is experimentally evaluated on several
state-of-the-art CF algorithms and leads to informative rating augmentation,
improving the performance of the baseline approaches.Comment: 20 page
Reviewing Developments of Graph Convolutional Network Techniques for Recommendation Systems
The Recommender system is a vital information service on today's Internet.
Recently, graph neural networks have emerged as the leading approach for
recommender systems. We try to review recent literature on graph neural
network-based recommender systems, covering the background and development of
both recommender systems and graph neural networks. Then categorizing
recommender systems by their settings and graph neural networks by spectral and
spatial models, we explore the motivation behind incorporating graph neural
networks into recommender systems. We also analyze challenges and open problems
in graph construction, embedding propagation and aggregation, and computation
efficiency. This guides us to better explore the future directions and
developments in this domain.Comment: arXiv admin note: text overlap with arXiv:2103.08976 by other author
A Hierarchical Self-Attentive Model for Recommending User-Generated Item Lists
User-generated item lists are a popular feature of many different platforms.
Examples include lists of books on Goodreads, playlists on Spotify and YouTube,
collections of images on Pinterest, and lists of answers on question-answer
sites like Zhihu. Recommending item lists is critical for increasing user
engagement and connecting users to new items, but many approaches are designed
for the item-based recommendation, without careful consideration of the complex
relationships between items and lists. Hence, in this paper, we propose a novel
user-generated list recommendation model called AttList. Two unique features of
AttList are careful modeling of (i) hierarchical user preference, which
aggregates items to characterize the list that they belong to, and then
aggregates these lists to estimate the user preference, naturally fitting into
the hierarchical structure of item lists; and (ii) item and list consistency,
through a novel self-attentive aggregation layer designed for capturing the
consistency of neighboring items and lists to better model user preference.
Through experiments over three real-world datasets reflecting different kinds
of user-generated item lists, we find that AttList results in significant
improvements in NDCG, Precision@k, and Recall@k versus a suite of
state-of-the-art baselines. Furthermore, all code and data are available at
https://github.com/heyunh2015/AttList.Comment: Accepted by CIKM 201
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