A Survey on Accuracy-oriented Neural Recommendation: From Collaborative Filtering to Information-rich Recommendation

Wu, Le; He, Xiangnan; Wang, Xiang; Zhang, Kun; Wang, Meng

doi:10.48550/arxiv.2104.13030

Cited by 8 publications

(9 citation statements)

References 184 publications

(310 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Recommender systems have been widely utilized to help users find their potential interests in many areas [1,7,30]. Classical models utilize MF techniques to learn user and item embeddings [16].…”

Section: Collaborative Filtering In Recommender Systemsmentioning

confidence: 99%

EKT: Exercise-Aware Knowledge Tracing for Student Performance Prediction

Liu

Huang

Yin

et al. 2021

IEEE Trans. Knowl. Data Eng.

300

View full text Add to dashboard Cite

For offering proactive services (e.g., personalized exercise recommendation) to the students in computer supported intelligent education, one of the fundamental tasks is predicting student performance (e.g., scores) on future exercises, where it is necessary to track the change of each student's knowledge acquisition during her exercising activities. Unfortunately, to the best of our knowledge, existing approaches can only exploit the exercising records of students, and the problem of extracting rich information existed in the materials (e.g., knowledge concepts, exercise content) of exercises to achieve both more precise prediction of student performance and more interpretable analysis of knowledge acquisition remains underexplored. To this end, in this paper, we present a holistic study of student performance prediction. To directly achieve the primary goal of performance prediction, we first propose a general Exercise-Enhanced Recurrent Neural Network (EERNN) framework by exploring both student's exercising records and the text content of corresponding exercises. In EERNN, we simply summarize each student's state into an integrated vector and trace it with a recurrent neural network, where we design a bidirectional LSTM to learn the encoding of each exercise from its content. For making final predictions, we design two implementations on the basis of EERNN with different prediction strategies, i.e., EERNNM with Markov property and EERNNA with Attention mechanism. Then, to explicitly track student's knowledge acquisition on multiple knowledge concepts, we extend EERNN to an explainable Exercise-aware K nowledge T racing (EKT) framework by incorporating the knowledge concept information, where the student's integrated state vector is now extended to a knowledge state matrix. In EKT, we further develop a memory network for quantifying how much each exercise can affect the mastery of students on multiple knowledge concepts during the exercising process. Finally, we conduct extensive experiments and evaluate both EERNN and EKT frameworks on a large-scale real-world data. The results in both general and cold-start scenarios clearly demonstrate the effectiveness of two frameworks in student performance prediction as well as the superior interpretability of EKT.

show abstract

Section: Collaborative Filtering In Recommender Systemsmentioning

confidence: 99%

EKT: Exercise-Aware Knowledge Tracing for Student Performance Prediction

Liu

Huang

Yin

et al. 2021

IEEE Trans. Knowl. Data Eng.

300

View full text Add to dashboard Cite

show abstract

“…Later on, with the ever prospering of deep neural networks, people have designed a large amount of neural recommender models. Correspondingly, many comprehensive surveys on deep recommender algorithms are proposed [Zhang, 2019;Wu, 2021;Fang, 2020]. Beyond concluding recommender models from the architecture perspective, many surveys focus on how to leverage the side information.…”

Section: Related Surveymentioning

confidence: 99%

Measuring "Why" in Recommender Systems: a Comprehensive Survey on the Evaluation of Explainable Recommendation

Chen¹,

Zhang²,

Wen³

2022

Preprint

View full text Add to dashboard Cite

Explainable recommendation has shown its great advantages for improving recommendation persuasiveness, user satisfaction, system transparency and among others. A fundamental problem of explainable recommendation is how to evaluate the explanations. In the past few years, various evaluation strategies have been proposed. However, they are scattered in different papers, and there lacks a systematic and detailed comparison between them. To bridge this gap, in this paper, we comprehensively review the previous work, and provide different taxonomies according to the evaluation perspectives and evaluation methods. Beyond summarization, we also analyze the (dis)advantages of existing evaluation methods, and provide a series of guidelines on how to select them. The contents of this survey are concluded from more than 100 papers from top-tier conferences like IJCAI, AAAI, TheWebConf, Recsys, UMAP and IUI, and the complete comparisons are presented at https://shimo.im/sheets/VKrpYTcwVH6KXgdy/MO DOC/. With this survey, we finally aim to provide a clear and comprehensive review on the evaluation of explainable recommendation.

show abstract

“…Pairwise models are based on the pairwise comparison between a relevant item and an irrelevant item at each time [27,31,51]. E.g., the most widely used pairwise method of Bayesian Personalized Ranking, assumes that a user prefers an observed item than a randomly selected unobserved item [31,44]. Most listwise models optimize the top-N ranking oriented measure or maximize the permutation probability of the most likely permutation of the defined list, such as the overall item set [14,47], or the list that is composed of an observed item and unobserved items [45].…”

Section: Introductionmentioning

confidence: 99%

Set2setRank: Collaborative Set to Set Ranking for Implicit Feedback based Recommendation

Chen,

Wu,

Zhang

et al. 2021

Preprint

Self Cite

View full text Add to dashboard Cite

As users often express their preferences with binary behavior data (implicit feedback), such as clicking items or buying products, implicit feedback based Collaborative Filtering (CF) models predict the top ranked items a user might like by leveraging implicit user-item interaction data. For each user, the implicit feedback is divided into two sets: an observed item set with limited observed behaviors, and a large unobserved item set that is mixed with negative item behaviors and unknown behaviors. Given any user preference prediction model, researchers either designed ranking based optimization goals or relied on negative item mining techniques for better optimization. Despite the performance gain of these implicit feedback based models, the recommendation results are still far from satisfactory due to the sparsity of the observed item set for each user. To this end, in this paper, we explore the unique characteristics of the implicit feedback and propose Set2setRank framework for recommendation. The optimization criteria of Set2setRank are two folds: First, we design an item to an item set comparison that encourages each observed item from the sampled observed set is ranked higher than any unobserved item from the sampled unobserved set. Second, we model set level comparison that encourages a margin between the distance summarized from the observed item set and the most "hard" unobserved item from the sampled negative set. Further, an adaptive sampling technique is designed to implement these two goals. We have to note that our proposed framework is model-agnostic and can be easily applied to most recommendation prediction approaches, and is time efficient in practice. Finally, extensive experiments on three real-world datasets demonstrate the superiority of our proposed approach.

show abstract

A Survey on Accuracy-oriented Neural Recommendation: From Collaborative Filtering to Information-rich Recommendation

Cited by 8 publications

References 184 publications

EKT: Exercise-Aware Knowledge Tracing for Student Performance Prediction

EKT: Exercise-Aware Knowledge Tracing for Student Performance Prediction

Measuring "Why" in Recommender Systems: a Comprehensive Survey on the Evaluation of Explainable Recommendation

Set2setRank: Collaborative Set to Set Ranking for Implicit Feedback based Recommendation

Contact Info

Product

Resources

About