Subscribe to the gold package and get unlimited access to Shamra Academy
Register a new userBasConv: Aggregating Heterogeneous Interactions for Basket Recommendation with Graph Convolutional Neural Network
104
0
0.0
(
0
)
Added by
Zhiwei Liu
Publication date
2020
fields
Informatics Engineering
and research's language is
English
Ask ChatGPT about the research
No Arabic abstract
Within-basket recommendation reduces the exploration time of users, where the users intention of the basket matters. The intent of a shopping basket can be retrieved from both user-item collaborative filtering signals and multi-item correlations. By defining a basket entity to represent the basket intent, we can model this problem as a basket-item link prediction task in the User-Basket-Item~(UBI) graph. Previous work solves the problem by leveraging user-item interactions and item-item interactions simultaneously. However, collectivity and heterogeneity characteristics are hardly investigated before. Collectivity defines the semantics of each node which should be aggregated from both directly and indirectly connected neighbors. Heterogeneity comes from multi-type interactions as well as multi-type nodes in the UBI graph. To this end, we propose a new framework named textbf{BasConv}, which is based on the graph convolutional neural network. Our BasConv model has three types of aggregators specifically designed for three types of nodes. They collectively learn node embeddings from both neighborhood and high-order context. Additionally, the interactive layers in the aggregators can distinguish different types of interactions. Extensive experiments on two real-world datasets prove the effectiveness of BasConv. Our code is available online at https://github.com/JimLiu96/basConv.
rate research
Read More
The problem of basket recommendation~(BR) is to recommend a ranking list of items to the current basket. Existing methods solve this problem by assuming the items within the same basket are correlated by one semantic relation, thus optimizing the item embeddings. However, this assumption breaks when there exist multiple intents within a basket. For example, assuming a basket contains {textit{bread, cereal, yogurt, soap, detergent}} where {textit{bread, cereal, yogurt}} are correlated through the breakfast intent, while {textit{soap, detergent}} are of cleaning intent, ignoring multiple relations among the items spoils the ability of the model to learn the embeddings. To resolve this issue, it is required to discover the intents within the basket. However, retrieving a multi-intent pattern is rather challenging, as intents are latent within the basket. Additionally, intents within the basket may also be correlated. Moreover, discovering a multi-intent pattern requires modeling high-order interactions, as the intents across different baskets are also correlated. To this end, we propose a new framework named as textbf{M}ulti-textbf{I}ntent textbf{T}ranslation textbf{G}raph textbf{N}eural textbf{N}etwork~({textbf{MITGNN}}). MITGNN models $T$ intents as tail entities translated from one corresponding basket embedding via $T$ relation vectors. The relation vectors are learned through multi-head aggregators to handle user and item information. Additionally, MITGNN propagates multiple intents across our defined basket graph to learn the embeddings of users and items by aggregating neighbors. Extensive experiments on two real-world datasets prove the effectiveness of our proposed model on both transductive and inductive BR. The code is available online at https://github.com/JimLiu96/MITGNN.
The purpose of the Session-Based Recommendation System is to predict the users next click according to the previous session sequence. The current studies generally learn user preferences according to the transitions of items in the users session sequence. However, other effective information in the session sequence, such as user profiles, are largely ignored which may lead to the model unable to learn the users specific preferences. In this paper, we propose a heterogeneous graph neural network-based session recommendation method, named SR-HetGNN, which can learn session embeddings by heterogeneous graph neural network (HetGNN), and capture the specific preferences of anonymous users. Specifically, SR-HetGNN first constructs heterogeneous graphs containing various types of nodes according to the session sequence, which can capture the dependencies among items, users, and sessions. Second, HetGNN captures the complex transitions between items and learns the item embeddings containing user information. Finally, to consider the influence of users long and short-term preferences, local and global session embeddings are combined with the attentional network to obtain the final session embedding. SR-HetGNN is shown to be superior to the existing state-of-the-art session-based recommendation methods through extensive experiments over two real large datasets Diginetica and Tmall.
Heterogeneous information network (HIN) is widely applied to recommendation systems due to its capability of modeling various auxiliary information with meta-path. However, existing HIN-based recommendation models usually fuse the information from various meta-paths by simple weighted sum or concatenation, which limits the improvement of performance because it lacks the capability of interest compositions among meta-paths. In this article, we propose a HIN-based Interest Composition model for Recommendation (HicRec). Specially, the representations of users and items are learnt with graph neural network on both graph structure and features in each meta-path, and a parameter sharing mechanism is utilized here to ensure the representations of users and items are in the same latent space. Then, users interest on each item from each pair of related meta-paths is calculated by a combination of the representations of users and items. The composed interests of users are obtained by a composition of them from both intra- and inter-meta-paths for recommendation. Extensive experiments are conducted on three real-world datasets and the results demonstrate the outperformance of our proposed HicRec against the baselines.
Collaborative Filtering (CF) is one of the most successful approaches for recommender systems. With the emergence of online social networks, social recommendation has become a popular research direction. Most of these social recommendation models utilized each users local neighbors preferences to alleviate the data sparsity issue in CF. However, they only considered the local neighbors of each user and neglected the process that users preferences are influenced as information diffuses in the social network. Recently, Graph Convolutional Networks~(GCN) have shown promising results by modeling the information diffusion process in graphs that leverage both graph structure and node feature information. To this end, in this paper, we propose an effective graph convolutional neural network based model for social recommendation. Based on a classical CF model, the key idea of our proposed model is that we borrow the strengths of GCNs to capture how users preferences are influenced by the social diffusion process in social networks. The diffusion of users preferences is built on a layer-wise diffusion manner, with the initial user embedding as a function of the current users features and a free base user latent vector that is not contained in the user feature. Similarly, each items latent vector is also a combination of the items free latent vector, as well as its feature representation. Furthermore, we show that our proposed model is flexible when user and item features are not available. Finally, extensive experimental results on two real-world datasets clearly show the effectiveness of our proposed model.
Predicting the next interaction of a short-term interaction session is a challenging task in session-based recommendation. Almost all existing works rely on item transition patterns, and neglect the impact of user historical sessions while modeling user preference, which often leads to non-personalized recommendation. Additionally, existing personalized session-based recommenders capture user preference only based on the sessions of the current user, but ignore the useful item-transition patterns from other users historical sessions. To address these issues, we propose a novel Heterogeneous Global Graph Neural Networks (HG-GNN) to exploit the item transitions over all sessions in a subtle manner for better inferring user preference from the current and historical sessions. To effectively exploit the item transitions over all sessions from users, we propose a novel heterogeneous global graph that contains item transitions of sessions, user-item interactions and global co-occurrence items. Moreover, to capture user preference from sessions comprehensively, we propose to learn two levels of user representations from the global graph via two graph augmented preference encoders. Specifically, we design a novel heterogeneous graph neural network (HGNN) on the heterogeneous global graph to learn the long-term user preference and item representations with rich semantics. Based on the HGNN, we propose the Current Preference Encoder and the Historical Preference Encoder to capture the different levels of user preference from the current and historical sessions, respectively. To achieve personalized recommendation, we integrate the representations of the user current preference and historical interests to generate the final user preference representation. Extensive experimental results on three real-world datasets show that our model outperforms other state-of-the-art methods.
suggested questions
Log in to be able to interact and post comments
comments
Fetching comments
Sign in to be able to follow your search criteria
