Context Neighbor Recommender: Integrating contexts via neighbors for recommendations

Zheng, Lin; Zhu, Fang; Huang, Sheng; Xie, Jin

doi:10.1016/j.ins.2017.05.034

Cited by 27 publications

(12 citation statements)

References 17 publications

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“…Nearest neighbor technology has been widely used in MF models [9], [15], [25] to mine user preferences. The nearest neighbor technique [26] can be combined with neural networks not only to select a candidate list of recommendation items but also, more importantly, to enhance performance [27].…”

Section: Neighbor and Memory Networkmentioning

confidence: 99%

“…However, this way of modeling existing topic information is not sufficient because it considers only the topics of the current user and the current item; while ignoring the rich topic information provided by user neighbors and item neighbors. The neighborhood-based factorization model [9] and the neural network [10] both fuse user-item interactions with neighbors to provide recommendations. An advanced method of neighborhood modeling [11], [12] is to integrate neighbor information into the memory network [13], [14], which can enhance collaborative filtering (CF) performance.…”

Section: Introductionmentioning

confidence: 99%

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Memory Reorganization: A Symmetric Memory Network for Reorganizing Neighbors and Topics to Complete Rating Prediction

et al. 2020

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Using pre-trained topic information to assist in training neural networks can effectively support the completion of the rating prediction task. However, existing neural-topic methods consider only the use of topic information corresponding to current users and items without neighbors, whereas existing memory-based neighborhood approaches are inappropriate for the direct modeling of neighbors with topics. To address the limitations, we argue that memory networks have the ability to organize neighbors with corresponding topics well and can provide a general solution to this problem. To confirm our hypothesis, we propose two approaches. One is an augmented memory network to couple with and enhance existing neural-topic models. The other is a symmetric memory network activated by a memory reorganization mechanism, which is a compact and generalized method for rating prediction. The experimental results demonstrate the effectiveness of the memory reorganization mechanism and show that the two proposed methods have advantages over existing state-of-the-art topic modeling approaches.

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Section: Neighbor and Memory Networkmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Memory Reorganization: A Symmetric Memory Network for Reorganizing Neighbors and Topics to Complete Rating Prediction

et al. 2020

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“…In the medical field, valuable patient-oriented recommendations were introduced in [46,7,17]. Zheng et al introduced a context neighbor recommender platform, which integrates contexts via neighbors for recommendations [46]. Chen et al proposed an autocratic decision-making system using group recommendation methods [7].…”

Section: Related Workmentioning

confidence: 99%

A disease diagnosis and treatment recommendation system based on big data mining and cloud computing

Chen

Rong

et al. 2018

Information Sciences

158

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It is crucial to provide compatible treatment schemes for a disease according to various symptoms at different stages. However, most classification methods might be ineffective in accurately classifying a disease that holds the characteristics of multiple treatment stages, various symptoms, and multi-pathogenesis. Moreover, there are limited exchanges and cooperative actions in disease diagnoses and treatments between different departments and hospitals. Thus, when new diseases occur with atypical symptoms, inexperienced doctors might have difficulty in identifying them promptly and accurately. Therefore, to maximize the utilization of the advanced medical technology of developed hospitals and the rich medical knowledge of experienced doctors, a Disease Diagnosis and Treatment Recommendation System (DDTRS) is proposed in this paper. First, to effectively identify disease symptoms more accurately, a Density-Peaked Clustering Analysis (DPCA) algorithm is introduced for disease-symptom clustering. In addition, association analyses on Disease-Diagnosis (D-D) rules and Disease-Treatment (D-T) rules are conducted by the Apriori algorithm separately. The appropriate diagnosis and treatment schemes are recommended for patients and inexperienced doctors, even if they are in a limited therapeutic environment. Moreover, to reach the goals of high performance and low latency response, we implement a parallel solution for DDTRS using the Apache Spark cloud platform. Extensive experimental results demonstrate that the proposed DDTRS realizes disease-symptom clustering effectively and derives disease treatment recommendations intelligently and accurately.

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“…First, the connection between the new user's profile P n and the old user's profile P o is not enough because the information related to a user includes not only the user's own profile, but also related information between users, such as a friend relationship. In the relevant literature [12], the information associated with the user is referred to as ''user context''. Therefore, effectively utilizing the user context is the key to determining the similarities between new users and old users.…”

Section: Introductionmentioning

confidence: 99%

“…In particular, the rich information associated with old users can include not only user contexts, but also item attributes, and user-item interactions [8], [13]. To clearly express different types of user information, we uniformly define the information of both new users and old users from the perspective of context-aware recommender systems (CARS) [12], [14] as follows.…”

Section: Introductionmentioning

confidence: 99%

Factorization Meets Neural Networks: A Scalable and Efficient Recommender for Solving the New User Problem

et al. 2020

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Predicting new user behavior has always been a challenging issue in intelligent recommender systems. This challenge is mainly due to the extreme asymmetry of information between new users and old users. Existing factorization models can efficiently process and map asymmetric information, but they are not good at mining deep relationships between contexts when compressing high-dimensional data. In contrast, neural network methods can deeply exploit the relationship between contexts; however, their training cost is much larger than factorization approaches. Therefore, this paper proposes a scalable and efficient recommender to solve the new user problem by filling the gap between factorization models and neural networks. The scalable part is a neural network that can jointly encode, compress, and fuse various types of contexts. The efficient part is a factorization model based on a correlation constraint mechanism and a projection strategy, which enables an asymmetric mapping of information between old and new users. The entire recommender fuses the two parts so that the factorization model and the neural network can complement each other. The experimental results show that our approach can achieve a good balance between performance and training efficiency compared to state-of-the-art methods. INDEX TERMS Recommender systems, new user problems, user behavior, context-aware recommendation, efficient training.

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Context Neighbor Recommender: Integrating contexts via neighbors for recommendations

Cited by 27 publications

References 17 publications

Memory Reorganization: A Symmetric Memory Network for Reorganizing Neighbors and Topics to Complete Rating Prediction

Memory Reorganization: A Symmetric Memory Network for Reorganizing Neighbors and Topics to Complete Rating Prediction

A disease diagnosis and treatment recommendation system based on big data mining and cloud computing

Factorization Meets Neural Networks: A Scalable and Efficient Recommender for Solving the New User Problem

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