Transition Information Enhanced Disentangled Graph Neural Networks for session-based recommendation

Content-based web image retrieval based on the query-by-example (QBE) principle remains a challenging problem due to the semantic gap as well as the gap between a user's intent and the representativeness of a typical image query. In this article, we propose to address this problem by integrating query-related contextual information into an advanced query model to improve the performance of QBE-based web image retrieval. We consider both the local and global context of the query image. The local context can be inferred from the web pages and the click-through log associated with the query image, while the global context is derived from the entire corpus comprising all web images and the associated web pages. To effectively incorporate the local query context we propose a language modeling based approach to deal with the combined structured query representation from the contextual and visual information. The global query context is integrated by the multi-modal relevance model to “reconstruct” the query from the document models indexed in the corpus. In this way, the global query context is employed to address the noise or missing information in the query and its local context, so that a comprehensive and robust query model can be obtained. We evaluated the proposed approach on a representative product image dataset collected from the web and demonstrated that the inclusion of the local and global query contexts significantly improves the performance of QBE-based web image retrieval.

A text corpus typically contains two types of context information -- global context and local context. Global context carries topical information which can be utilized by topic models to discover topic structures from the text corpus, while local context can train word embeddings to capture semantic regularities reflected in the text corpus. This encourages us to exploit the useful information in both the global and the local context information. In this paper, we propose a unified language model based on matrix factorization techniques which 1) takes the complementary global and local context information into consideration simultaneously, and 2) models topics and learns word embeddings collaboratively. We empirically show that by incorporating both global and local context, this collaborative model can not only significantly improve the performance of topic discovery over the baseline topic models, but also learn better word embeddings than the baseline word embedding models. We also provide qualitative analysis that explains how the cooperation of global and local context information can result in better topic structures and word embeddings.

Twitter has attracted millions of users to share and disseminate most up-to-date information, resulting in large volumes of data produced everyday. However, many applications in Information Retrieval (IR) and Natural Language Processing (NLP) suffer severely from the noisy and short nature of tweets. In this paper, we propose a novel framework for tweet segmentation in a batch mode, called HybridSeg. By splitting tweets into meaningful segments, the semantic or context information is well preserved and easily extracted by the downstream applications. HybridSeg finds the optimal segmentation of a tweet by maximizing the sum of the stickiness scores of its candidate segments. The stickiness score considers the probability of a segment being a phrase in English (i.e., global context) and the probability of a segment being a phrase within the batch of tweets (i.e., local context). For the latter, we propose and evaluate two models to derive local context by considering the linguistic features and term-dependency in a batch of tweets, respectively. HybridSeg is also designed to iteratively learn from confident segments as pseudo feedback. Experiments on two tweet data sets show that tweet segmentation quality is significantly improved by learning both global and local contexts compared with using global context alone. Through analysis and comparison, we show that local linguistic features are more reliable for learning local context compared with term-dependency. As an application, we show that high accuracy is achieved in named entity recognition by applying segment-based part-of-speech (POS) tagging.

This article seeks to employ bibliometric analysis to offer a thorough picture of the research environment concerning local, global, and glocal contexts. Amidst the increasing globalization, it is imperative to deeply understand the interplay between local and global contexts and the "glocal" concept. This study investigates the current body of literature on these three areas by assessing patterns of publishing, trends in research, and collaborations among scientists. Using data from famous academic databases, we have found noteworthy keywords, notable authors, and esteemed publications pertaining to this topic. The research indicates a significant rise in the quantity of publications regarding local, global, and global contexts during the previous two decades. This research also illustrates a shift in focus from localized contextual examinations to a more inclusive approach that combines global and local perspectives. These findings are anticipated to offer scholars and practitioners a novel understanding of the dynamics of interaction between local, global, and glocal contexts. Moreover, these discoveries will be a significant reference for forthcoming investigations in this field.

Implicit user relationships across sessions enhanced graph for session-based recommendation

Enhancing context knowledge repositories with justifiable exceptions

Context-based attention models achieve state-of-the-art results on constructing document representations. Current methods build document representations based on a trainable global context vector for all documents, while local contexts capturing document-specific information are ignored. Representing document context as a vector also restricts the model's capacity to capture complete semantic components of documents. In this work, we address these limitations by proposing novel attention-based approaches. We first propose a local and global context attention (LGCA) model, which constructs the context representations of a document as a combination of global and local contexts. The local context is used to capture local document-specific information. Furthermore, a multi-context attention (MCA) model is proposed to capture multiple local contexts within a document, thereby fusing the overall semantic information of documents. We conduct experiments on document-level sentiment classification tasks where extra user and product information is shown to be important. Experimental results indicate that our methods significantly outperform previous models with and without user and product information. It is also demonstrated that our model has great potential of handling extra information to improve document classification performance.

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 user's 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.

In contextual cueing tasks, participants can use a repeating local context to learn to detect the target, yet most contextual cueing studies have relied on repeating global context properties. We examined whether observers can use local context repetitions in a similar manner as they use global context repetitions. In addition, we examined how reward-predicting context features modulate the use of local and global contexts. Participants searched through contexts in which either the entire context configuration or only a local context around the target repeated, intermixed with novel contexts. Half of the context items appeared in a color signaling either low or high reward. We found that local context repetitions led to comparable benefits in response times and fixation count as global context repetitions did. Surprisingly, reward magnitude did not affect performance in local nor in global contexts. The results suggest that a local chunk of distractors can be used for context learning and attention guidance in a similar manner as the global context configuration. We suggest that the proportion of repeated and novel context trials is crucial for context learning and that our combination of locally and globally repeating contexts provided an environment that facilitated learning in both context types because it allowed predicting the target location from the context in most of the trials.

In this chapter, the authors report themes that emerged when a cross-cultural team of researchers involved in a virtual international community of practice (Global Social Justice in Education-GSJE) investigated reflections on activities focused on social justice in local and global contexts. The findings suggested that the activities elicited GSJE community members' understandings of the complexities of social justice associated with naming practices, privilege, and the arts within their own and across contexts. The authors discuss implications of the activities to advance diverse educators' understanding of social justice in global and local contexts. They also unpack the opportunities and challenges that resulted from the collaborative research process and the writing of this chapter completed by the international author team comprised of 11 education scholars from seven countries.

Dynamic intent-aware iterative denoising network for session-based recommendation

Session-based recommendation uses past clicks and interaction sequences from anonymous users to predict the next item most likely to be clicked. Predicting the user’s subsequent behavior in online transactions becomes a problem mainly due to the lack of user information and limited behavioral information. Existing methods, such as recurrent neural network (RNN)-based models that model user’s past behavior sequences and graph neural network (GNN)-based models that capture potential relationships between items, miss different time intervals in the past behavior sequence and can only capture certain types of user interest patterns due to the characteristics of neural networks. Graphic models created to improve the current session reduce the model’s success due to the addition of irrelevant items. Moreover, attention mechanisms in recent approaches have been insufficient due to weak representations of users and products. In this study, we propose a model based on the combination of skip-gram and transformer (SkipGT) to solve the above-mentioned drawbacks in session-based recommendation systems. In the proposed method, skip-gram both captures chained user interest in the session thread through item-specific subreddits and learns complex interaction information between items. The proposed method captures short-term and long-term preference representations to predict the next click with the help of a transformer. The transformer in our proposed model overcomes many limitations in turn-based models and models longer contextual connections between items more effectively. In our proposed model, by giving the transformer trained item embeddings from the skip-gram model as input, the transformer has better performance because it does not learn item representations from scratch. By conducting extensive experiments with three real-world datasets, we confirm that SkipGT significantly outperforms state-of-the-art solutions with an average MRR score of 5.58%.

Session-based Recommender Systems (SRSs), which aim to recommend users’ next action based on their current and historical sessions, play a significant role in many real-world online services. The existing session-based recommendation methods have mainly focused on the accuracy of recommendation, which biases to reinforce popular items/services and loses the recommendation diversity. Diversity is a positive aspect particularly in SRSs as the target user may like to be surprised and interact with a broader range of content in different sessions. In this work, we propose a Personalized Diversification strategy for a Session-based Recommender System (PD-SRS) using graph neural networks. Comprehensive experiments are carried out on two real-world datasets to demonstrate the effectiveness of PD-SRS in making a trade-off between accuracy and personalized diversity over the baselines.KeywordsSession-based recommendationPersonalized diversityFairnessLong-tail recommendationGraph neural network

Session-based recommendations can predict the next item through the user's current session sequence. Session-based recommendation with graph neural network has become the mainstream method in this domain. However, the session sub-graph constructed by the session sequence will bring fake dependencies issue. Adjacent items may be noise, non-adjacent items may have true dependencies; Moreover, the information outside the target session is ignored. To solve these problems, a dependency enhanced session-based recommendation model based on graph neural network (GID-GNN) is proposed. Firstly, it uses GNN to learn session features, meanwhile uses self-attention to capture session items' dependencies to obtain session level representation; Then a global graph is constructed to obtain effective global level information and global level representation; Finally, the prediction is made by combining two part representations. Experiments were tested on two real datasets, the results proved effectiveness of GID-GNN model.

Graph neural networks (GNN) have recently been applied to exploit knowledge graph (KG) for recommendation. Existing GNN-based methods explicitly model the dependency between an entity and its local graph context in KG (i.e., the set of its first-order neighbors), but may not be effective in capturing its non-local graph context (i.e., the set of most related high-order neighbors). In this paper, we propose a novel recommendation framework, named Contextualized Graph Attention Network (CGAT), which can explicitly exploit both local and non-local graph context information of an entity in KG. More specifically, CGAT captures the local context information by a user-specific graph attention mechanism, considering a user's personalized preferences on entities. In addition, CGAT employs a biased random walk sampling process to extract the non-local context of an entity, and utilizes a Recurrent Neural Network (RNN) to model the dependency between the entity and its non-local contextual entities. To capture the user's personalized preferences on items, an item-specific attention mechanism is also developed to model the dependency between a target item and the contextual items extracted from the user's historical behaviors. We compared CGAT with state-of-the-art KG-based recommendation methods on real datasets, and the experimental results demonstrate the effectiveness of CGAT.