Deep Learning based Recommender System: A Survey and New Perspectives

With the ever-growing volume of online information, recommender systems have been an effective strategy to overcome such information overload. The utility of recommender systems cannot be overstated, given its widespread adoption in many web applications, along with its potential impact to ameliorate many problems related to over-choice. In recent years, deep learning has garnered considerable interest in many research fields such as computer vision and natural language processing, owing not only to stellar performance but also the attractive property of learning feature representations from scratch. The influence of deep learning is also pervasive, recently demonstrating its effectiveness when applied to information retrieval and recommender systems research. Evidently, the field of deep learning in recommender system is flourishing. This article aims to provide a comprehensive review of recent research efforts on deep learning based recommender systems. More concretely, we provide and devise a taxonomy of deep learning based recommendation models, along with providing a comprehensive summary of the state-of-the-art. Finally, we expand on current trends and provide new perspectives pertaining to this new exciting development of the field.Comment: The paper has been accepted by ACM Computing Surveys. https://doi.acm.org/10.1145/328502

Context-Aware Systems for Sequential Item Recommendation

Quizlet is the most popular online learning tool in the United States, and is used by over 2/3 of high school students, and 1/2 of college students. With more than 95% of Quizlet users reporting improved grades as a result, the platform has become the de-facto tool used in millions of classrooms. In this paper, we explore the task of recommending suitable content for a student to study, given their prior interests, as well as what their peers are studying. We propose a novel approach, i.e. Neural Educational Recommendation Engine (NERE), to recommend educational content by leveraging student behaviors rather than ratings. We have found that this approach better captures social factors that are more aligned with learning. NERE is based on a recurrent neural network that includes collaborative and content-based approaches for recommendation, and takes into account any particular student's speed, mastery, and experience to recommend the appropriate task. We train NERE by jointly learning the user embeddings and content embeddings, and attempt to predict the content embedding for the final timestamp. We also develop a confidence estimator for our neural network, which is a crucial requirement for productionizing this model. We apply NERE to Quizlet's proprietary dataset, and present our results. We achieved an R^2 score of 0.81 in the content embedding space, and a recall score of 54% on our 100 nearest neighbors. This vastly exceeds the recall@100 score of 12% that a standard matrix-factorization approach provides. We conclude with a discussion on how NERE will be deployed, and position our work as one of the first educational recommender systems for the K-12 space

TPMCF: Temporal QoS Prediction using Multi-Source Collaborative Features

Recently, with the rapid deployment of service APIs, personalized service recommendations have played a paramount role in the growth of the e-commerce industry. Quality-of-Service (QoS) parameters determining the service performance, often used for recommendation, fluctuate over time. Thus, the QoS prediction is essential to identify a suitable service among functionally equivalent services over time. The contemporary temporal QoS prediction methods hardly achieved the desired accuracy due to various limitations, such as the inability to handle data sparsity and outliers and capture higher-order temporal relationships among user-service interactions. Even though some recent recurrent neural-network-based architectures can model temporal relationships among QoS data, prediction accuracy degrades due to the absence of other features (e.g., collaborative features) to comprehend the relationship among the user-service interactions. This paper addresses the above challenges and proposes a scalable strategy for Temporal QoS Prediction using Multi-source Collaborative-Features (TPMCF), achieving high prediction accuracy and faster responsiveness. TPMCF combines the collaborative-features of users/services by exploiting user-service relationship with the spatio-temporal auto-extracted features by employing graph convolution and transformer encoder with multi-head self-attention. We validated our proposed method on WS-DREAM-2 datasets. Extensive experiments showed TPMCF outperformed major state-of-the-art approaches regarding prediction accuracy while ensuring high scalability and reasonably faster responsiveness.Comment: 10 Pages, 7 figure