Social-aware Sparse Attention Network for Session-based Social Recommendation (2022)

Abstract: Session-based Social Recommendation (SSR) aims to use users’ social networks and historical sessions to provide more personalized recommendations for the current session.Unfortunately, existing SSR methods have two limitations.First, they do not screen users’ useless social relationships and noisy irrelevant interactions.However, user preferences are mainly affected by several close friends and key interactions.Second, when modeling the current session, they do not take full advantage of user preference information.To tackle these issues, we propose a novel Social-aware Sparse Attention Network for SSR, abbreviated as SSAN.It mainly consists of the Heterogeneous Graph Embedding (HGE) module and the Social-aware Encoder-decoder Network (SEN) module.In the HGE module, we adopt a modified heterogeneous graph neural network, which focuses more on close friends and key historical interactions, to enhance user/item representations. In the SEN module, we use the user representation as a bridge between the Encoder and Decoder to incorporate user preferences when modeling the current session.Extensive experiments on two benchmark datasets demonstrate the superiority of SSAN over the state-of-the-art models.

## introduction session-based social recommendation (ssr) is proposed based on session-based recommendation (sr). initially, sr aims to predict the next item for the current anonymous session. the anonymous session is a sequence of items clicked in a transaction, without user ids and users' social networks. later, with the boom of modern network and social media, user ids can be tracked, and it becomes more common for users to have a social network. therefore, ssr has been proposed, which aims to capture user preferences based on their social networks and historical sessions to provide more personalized recommendations @xcite . ssr is initially proposed in dgrec @xcite , which uses graph neural networks (gnns) to aggregate the preferences of neighbors for each user. afterward, serec @xcite proposes to use a heterogeneous graph neural network to learn user and item representations that integrate the knowledge from social networks. since they take advantage of users' social networks and historical interactions, they have a significant performance improvement over previous conventional sr methods @xcite . however, most of the existing ssr models suffer from two defects: (a) they follow a strong underlying assumption that users' all friends and interactions can influence their preferences. therefore, they aggregate the features of all friends and historical interactions to model the user and do not screen the irrelevant items when modeling the current session. however, user preferences are mainly affected by several close friends and key interactions. moreover, it has been shown in the literature that irrelevant items can interfere with session modeling @xcite . in other words, aggregating all information without filtering can lead to bias in modeling user preferences. (b) when modeling the current session, they do not make full use of the user's personalized information. for example, serec @xcite only concatenates user representations at the last stage of model inference, which limits the expressiveness of the personalized knowledge of the user. to tackle these issues, we propose to eliminate the low-confidence information, and incorporate personalized knowledge into the modeling of the current session. hence, we put forward a novel social-aware sparse attention network for ssr, abbreviated as ssan. it mainly consists of the heterogeneous graph embedding (hge) module and the social-aware encoder-decoder network (sen) module. the hge module aims to model user preferences based on users' social relationships and historical interactions. in the hge module, we use a heterogeneous graph neural network, which focuses more on close friends and important historical interactions, to enhance user/item representations. it can alleviate the impact of invalid social relationships and useless historical interactions on user modeling. the sen module aims to model the current session based on user preferences and interactions in the current session. in the sen module, we mine the latent intents from the interactions and inject user preference information into the modeling process of the current session. it can alleviate the impact of unreliable interactions and take full advantage of the personalized knowledge of users. extensive experiments on two public benchmark datasets demonstrate the superiority of ssan. further ablation experiments demonstrate the effectiveness of the hge and sen modules. to summarize, we mainly make the following contributions: ## related work in this section, we retrospect the existing work related to our research, which mainly consists of the following three subsections. ## task definition let @xmath0, u 2 , . . . , u m }, @xmath1, i 2 , ..., i n } denote the set of users and items, respectively. m, n are the total number of users and items, respectively. let d represents the set of all historical sessions of users. the set of all sessions of a user is represented by the task of ssr is to predict the next item of a new session s u / ∈ d u based on social network g and the set of all previous sessions d u . it can be formalized as predicting the probability of user interaction with each item @xmath2: ## method in this section, we introduce the ssan in detail. ## experiments we conduct experiments on two real-world benchmark datasets and mainly aim to answer the following research questions: rq1: how does ssan compare to other stateof-the-art (sota) models? rq2: whether using the users' social networks and historical sessions is conducive to predicting the user's next click? rq3: whether hge module is beneficial to the final performance? rq4: whether ssan is efficient? rq5: how the modules and layers of ssan affect the final performance? ## conclusion in this paper, we summarize two issues of previous ssr methods. to tackle these issues, we propose a novel social-aware sparse attention network, abbreviated as ssan. in this model, we construct the hge module based on the improved heterogeneous graph neural network. it can inject the high-confidence social knowledge and historical interaction information into the modeling process of users and items. meanwhile, we construct the sen module based on the sparse attention mechanism to integrate user preference information when modeling the current session. extensive experimental results on two datasets demonstrate the superiority of ssan over the state-of-the-art models. in future work, we plan to explore more efficient methods to capture the social knowledge and enhance the ability in screening irrelevant items.