142 research outputs found
Alleviating New User Cold-Start in User-Based Collaborative Filtering via Bipartite Network
The recommender system (RS) can help us extract valuable data from a huge amount of raw information. User-based collaborative filtering (UBCF) is widely employed in practical RSs due to its outstanding performance. However, the traditional UBCF is subject to the new user cold-start issue because a new user is often extreme lack of available rating information. In this article, we develop a novel approach that incorporates a bipartite network into UBCF for enhancing the recommendation quality of new users. First, through the statistic and analysis of new users\u27 rating characteristics, we collect niche items and map the corresponding rating matrix to a weighted bipartite network. Furthermore, a new weighted bipartite modularity index merging normalized rating information is present to conduct the community partition that realizes coclustering of users and items. Finally, for each individual clustering that is much smaller than the original rating matrix, a localized low-rank matrix factorization is executed to predict rating scores for unrated items. Items with the highest predicted rating scores are recommended to a new user. Experimental results from two real-world data sets suggest that without requiring additional complex information, the proposed approach is superior in terms of both recommendation accuracy and diversity and can alleviate the new user cold-start issue of UBCF effectively
Harnessing heterogeneous social networks for better recommendations: A grey relational analysis approach
Most of the extant studies in social recommender system are based on explicit social relationships, while the potential of implicit relationships in the heterogeneous social networks remains largely unexplored. This study proposes a new approach to designing a recommender system by employing grey relational analysis on the heterogeneous social networks. It starts with the establishment of heterogeneous social networks through the user-item bipartite graph, user social network graph and user-attribute bipartite graph; and then uses grey relational analysis to identify implicit social relationships, which are then incorporated into the matrix factorization model. Five experiments were conducted to test the performance of our approach against four state-of-the-art baseline methods. The results show that compared with the baseline methods, our approach can effectively alleviate the sparsity problem, because the heterogeneous social network provides richer information. In addition, the grey relational analysis method has the advantage of low requirements for data size and efficiently relieves the cold start problem. Furthermore, our approach saves processing time, thus increases recommendation efficiency. Overall, the proposed approach can effectively improve the accuracy of rating prediction in social recommendations and provide accurate and efficient recommendation service for users
Knowledge-refined Denoising Network for Robust Recommendation
Knowledge graph (KG), which contains rich side information, becomes an
essential part to boost the recommendation performance and improve its
explainability. However, existing knowledge-aware recommendation methods
directly perform information propagation on KG and user-item bipartite graph,
ignoring the impacts of \textit{task-irrelevant knowledge propagation} and
\textit{vulnerability to interaction noise}, which limits their performance. To
solve these issues, we propose a robust knowledge-aware recommendation
framework, called \textit{Knowledge-refined Denoising Network} (KRDN), to prune
the task-irrelevant knowledge associations and noisy implicit feedback
simultaneously. KRDN consists of an adaptive knowledge refining strategy and a
contrastive denoising mechanism, which are able to automatically distill
high-quality KG triplets for aggregation and prune noisy implicit feedback
respectively. Besides, we also design the self-adapted loss function and the
gradient estimator for model optimization. The experimental results on three
benchmark datasets demonstrate the effectiveness and robustness of KRDN over
the state-of-the-art knowledge-aware methods like KGIN, MCCLK, and KGCL, and
also outperform robust recommendation models like SGL and SimGCL
A Network Resource Allocation Recommendation Method with An Improved Similarity Measure
Recommender systems have been acknowledged as efficacious tools for managing
information overload. Nevertheless, conventional algorithms adopted in such
systems primarily emphasize precise recommendations and, consequently, overlook
other vital aspects like the coverage, diversity, and novelty of items. This
approach results in less exposure for long-tail items. In this paper, to
personalize the recommendations and allocate recommendation resources more
purposively, a method named PIM+RA is proposed. This method utilizes a
bipartite network that incorporates self-connecting edges and weights.
Furthermore, an improved Pearson correlation coefficient is employed for better
redistribution. The evaluation of PIM+RA demonstrates a significant enhancement
not only in accuracy but also in coverage, diversity, and novelty of the
recommendation. It leads to a better balance in recommendation frequency by
providing effective exposure to long-tail items, while allowing customized
parameters to adjust the recommendation list bias
Search Behavior Prediction: A Hypergraph Perspective
Although the bipartite shopping graphs are straightforward to model search
behavior, they suffer from two challenges: 1) The majority of items are
sporadically searched and hence have noisy/sparse query associations, leading
to a \textit{long-tail} distribution. 2) Infrequent queries are more likely to
link to popular items, leading to another hurdle known as
\textit{disassortative mixing}. To address these two challenges, we go beyond
the bipartite graph to take a hypergraph perspective, introducing a new
paradigm that leverages \underline{auxiliary} information from anonymized
customer engagement sessions to assist the \underline{main task} of query-item
link prediction. This auxiliary information is available at web scale in the
form of search logs. We treat all items appearing in the same customer session
as a single hyperedge. The hypothesis is that items in a customer session are
unified by a common shopping interest. With these hyperedges, we augment the
original bipartite graph into a new \textit{hypergraph}. We develop a
\textit{\textbf{D}ual-\textbf{C}hannel \textbf{A}ttention-Based
\textbf{H}ypergraph Neural Network} (\textbf{DCAH}), which synergizes
information from two potentially noisy sources (original query-item edges and
item-item hyperedges). In this way, items on the tail are better connected due
to the extra hyperedges, thereby enhancing their link prediction performance.
We further integrate DCAH with self-supervised graph pre-training and/or
DropEdge training, both of which effectively alleviate disassortative mixing.
Extensive experiments on three proprietary E-Commerce datasets show that DCAH
yields significant improvements of up to \textbf{24.6\% in mean reciprocal rank
(MRR)} and \textbf{48.3\% in recall} compared to GNN-based baselines. Our
source code is available at
\url{https://github.com/amazon-science/dual-channel-hypergraph-neural-network}.Comment: WSDM 202
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
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