Multi-Behavior Recommendation with Cascading Graph Convolution Networks

Multi-behavior recommendation, which exploits auxiliary behaviors (e.g., click and cart) to help predict users' potential interactions on the target behavior (e.g., buy), is regarded as an effective way to alleviate the data sparsity or cold-start issues in recommendation. Multi-behaviors are often taken in certain orders in real-world applications (e.g., click>cart>buy). In a behavior chain, a latter behavior usually exhibits a stronger signal of user preference than the former one does. Most existing multi-behavior models fail to capture such dependencies in a behavior chain for embedding learning. In this work, we propose a novel multi-behavior recommendation model with cascading graph convolution networks (named MB-CGCN). In MB-CGCN, the embeddings learned from one behavior are used as the input features for the next behavior's embedding learning after a feature transformation operation. In this way, our model explicitly utilizes the behavior dependencies in embedding learning. Experiments on two benchmark datasets demonstrate the effectiveness of our model on exploiting multi-behavior data. It outperforms the best baseline by 33.7% and 35.9% on average over the two datasets in terms of Recall@10 and NDCG@10, respectively.Comment: Accepted by WWW 202

Graph-based Alignment and Uniformity for Recommendation

Collaborative filtering-based recommender systems (RecSys) rely on learning representations for users and items to predict preferences accurately. Representation learning on the hypersphere is a promising approach due to its desirable properties, such as alignment and uniformity. However, the sparsity issue arises when it encounters RecSys. To address this issue, we propose a novel approach, graph-based alignment and uniformity (GraphAU), that explicitly considers high-order connectivities in the user-item bipartite graph. GraphAU aligns the user/item embedding to the dense vector representations of high-order neighbors using a neighborhood aggregator, eliminating the need to compute the burdensome alignment to high-order neighborhoods individually. To address the discrepancy in alignment losses, GraphAU includes a layer-wise alignment pooling module to integrate alignment losses layer-wise. Experiments on four datasets show that GraphAU significantly alleviates the sparsity issue and achieves state-of-the-art performance. We open-source GraphAU at https://github.com/YangLiangwei/GraphAU.Comment: 4 page

TruGRC: Trust-Aware Group Recommendation with Virtual Coordinators

© 2018 Elsevier B.V. In recent years, an increase in group activities on websites has led to greater demand for highly-functional group recommender systems. The goal of group recommendation is to capture and distill the preferences of each group member into a single recommendation list that meets the needs of all group members. Existing aggregation functions perform well in harmonious and congruent scenarios, but tend not to generate satisfactory results when group members hold conflicting preferences. Moreover, most of current studies improve group recommendation only based on a single aggregation strategy and explicit trust information is still ignored in group recommender systems. Motivated by these concerns, this paper presents TruGRC, a novel Trust-aware Group Recommendation method with virtual Coordinators, that combines two different aggregation strategies: result aggregation and profile aggregation. As each individual's preferences are modeled, a virtual user is built as a coordinator to represent the profile aggregation strategy. This coordinator provides a global view of the preferences for all group members by interacting with each user to resolve conflicting preferences. Then, we also model the impact from group members to the virtual coordinator in accordance with personal social influence inferred by trust information on social networks. Group preferences can be easily generated by the average aggregation method under the effect of the virtual coordinator. Experimental results on two benchmark datasets with a range of different group sizes show that TruGRC method has significant improvements compared to other state-of-the-art methods

Exploring deep learning techniques to tackle the sparsity problem in recommender systems

With the inception of e-commerce in the early twenty-first century, people's lifestyles have drastically changed. People today tend to do many of their daily routines online, such as shopping, reading the news, and watching movies. Nevertheless, consumers often face diffculties while exploring related items such as new fashion trends because they are not aware of their existence due to the overwhelming amount of information available online. This phenomenon is widely known as "information overload". Therefore, recommender systems (RSs) are a critical solution for helping users make decisions when there are lots of choices. RSs have been integrated into and have become an essential part of every website due to their effectiveness in increasing customer interactions, attracting new customers, and growing business revenue. Machine learning, and deep learning (DL) in particular, have achieved a great success in resolving various computer science problems. Generally, DL-based approaches have enhanced performance remarkably compared with traditional approaches. Specifically, DL-based approaches have become the state-of-the-art techniques in RSs. Therefore, in this dissertation, three DL-based contributions are presented to address the natural data sparsity problem in RSs: (1) DeepHCF, a deep-hybrid, collaborative-filtering model that trains two deep models via joint training for rating prediction tasks; (2) CATA, a collaborative attentive autoencoder that integrates the attention mechanism to enhance the recommendation quality for ranking prediction tasks; and (3) CATA++, an extended version of CATA that employs a dual attentive autoencoder to leverage more of the item's content. All proposed models have gone through comprehensive experiments to evaluate their performance against state-of-the-art models using real-world datasets. Our experimental results show the superiority of our models over state-of-the-art models according to various evaluation metrics.Includes bibliographical references (pages 114-128)

Systematic literature review on enhancing recommendation system by eliminating data sparsity

The aim of this project is to develop an approach using machine learning and matrix factorization to improve recommendation system. Nowadays, recommendation system has become an important part of our lives. It has helped us to make our decision-making process easier and faster as it could recommend us products that are similar with our taste. These systems can be seen everywhere such as online shopping or browsing through film catalogues. Unfortunately, the system still has its weakness where it faced difficulty in recommending products if there are insufficient reviews left by the users on products. It is difficult for the system to recommend said products because it is difficult to pinpoint what kind of users would be interested in the products. Research studies have used matrix factorization as the standard to solve this issue but lately, machine learning has come up as a good alternative to solve data sparsity. This project compares results of the recommendation system using RMSE to see how each proposed methods performs using three different datasets from MovieLens. We have selected two models – matrix factorization with SVD and deep learning-based model to evaluate these approaches and understand why they are popular solution to data sparsity. We have found that SVD brought in a lower RMSE as compared to deep learning. The reason behind this was discussed in the latter chapter of this thesis. We have also found possible research in capitalising categorical variables in recommendation system and the experiment achieved a lower RMSE score as compared to SVD and deep learning, showing the many possibilities of the future directions of the research in recommendation system

Multiple social network integration framework for recommendation across system domain

A recommender system is a special software that recommends items to a user based on the user’s history. A recommender system comprises users, items and a rating matrix. Rating matrix stores the interactions between users and items. The system faces a variety of problems among which three are the main concerns of this research. These problems are cold start, sparsity, and diversity. Majority of the research use a conventional framework for solving these problems. In a conventional recommender system, user profiles are generated from a single feedback source, whereas, Cross Domain Recommender Systems (CDRS) research relies on more than one source. Recently researchers have started using “Social Network Integration Framework”, that integrates social network as an additional feedback source. Although the existing framework alleviates recommendation problems better than the conventional framework, it still faces limitations. Existing framework is designed only for a single source domain and requires the same user participation in both the source and the target domain. Existing techniques are also designed to integrate knowledge from one social network only. To integrate multiple sources, this research developed a “Multiple Social Network Integration Framework”, that consists of two models and three techniques. Firstly, the Knowledge Generation Model generates interaction matrices from “n” number of source domains. Secondly, the Knowledge Linkage Model links the source domains to the target domain. The outputs of the models are inputs of the techniques. Then multiple techniques were developed to address cold start, sparsity and diversity problem using multiple source networks. Three techniques addressed the cold start problem. These techniques are Multiple Social Network integration with Equal Weights Participation (MSN-EWP), Multiple Social Network integration with Local Adjusted Weights Participation (MSNLAWP) and Multiple Social Network integration with Target Adjusted Weights Participation (MSN-TAWP). Experimental results showed that MSN-TAWP performed best by producing 47% precision improvement over popularity ranking as the baseline technique. For the sparsity problem, Multiple Social Network integration for K Nearest Neighbor identification (MSN-KNN) technique performed at least 30% better in accuracy while decreasing the error rate by 20%. Diversity problem was addressed by two combinations of the cold start and sparsity techniques. These combinations, EWP + MSN-KNN, TAWP + MSN-KNN and TAWP + MSN-KNN outperformed the rest of the diversity combinations by 56% gain in diversity with a precision loss of 1%. In conclusion, the techniques designed for multiple sources outperformed existing techniques for addressing cold start, sparsity and diversity problem. Finally, an extension of multiple social network integration framework for content-based and hybrid recommendation techniques should be considered future work