Improving Scalability of Personalized Recommendation Systems for Enterprise Knowledge Workers

Verma, Chetan; Hart, Michael; Bhatkar, Sandeep; Parker-Wood, Aleatha; Dey, Sujit

doi:10.1109/access.2015.2513000

Cited by 19 publications

(14 citation statements)

References 15 publications

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“…Zhao et al proposed an e-commerce recommender system that automatically sets price discounts while recommending a product using Linear Regression [24]. A recommendation system with improved scalability to help knowledge workers in determining valuable new content is developed by Verma et al [25] using The et al [29] to perform collaborative filtering. Above the embedding layer, the proposed model uses an exterior composition to clearly characterize the pair wise correlations to result in a two-dimensional interaction map in which the higher-order correlations are learned using a CNN.…”

Section: Related Workmentioning

confidence: 99%

Enhancing Context-Aware Recommendation Using Trust-Based Contextual Attentive Autoencoder

Abinaya¹,

Alphonse²,

Abirami³

et al. 2023

Neural Process Lett

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Context-aware recommender systems (CARS) are intended primarily to consider the circumstances under which a user encounters an item to provide better-personalized recommendations. Users acquire point-of-interest, movies, products, and various online resources as suggestions. Classical collaborative filtering algorithms are shown to be satisfactory in a variety of recommendation activities processes, but cannot often capture complicated interactions between item and user, along with sparsity and cold start constraints. Hence it becomes a surge to apply a deep learning-based recommender model owing to its dynamic modeling potential and sustained success in other fields of application. In this work, a Trust-based Attentive Contextual Denoising Autoencoder(TACDA) for enhanced Top-N context-aware recommendation is proposed. Specifically, the TCADA model takes the sparse preference of the user that is integrated with trust data as input into the autoencoder to prevail over the cold start and sparsity obstacle and efficiently accumulates the context condition into the model via attention framework. Thereby, the attention technique is used to encode context features into a latent space of the user's trust data that is integrated with their preferences, which interconnects personalized context circumstances with active user's choice to deliver recommendations suited to that active user. Experiments conducted on Epinions and Caio dataset make obvious the efficiency of the TACDA model persistently outperforms the state-of-the-art methods.

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Section: Related Workmentioning

confidence: 99%

Enhancing Context-Aware Recommendation Using Trust-Based Contextual Attentive Autoencoder

Abinaya¹,

Alphonse²,

Abirami³

et al. 2023

Neural Process Lett

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“…In the process of training, we adopt the mini-batch method [42]. The batch size is set to 256 for the deep learning models [43]. We randomly initialize the item embedding with dimension d = 50.…”

Section: Baselines and Parameter Settingsmentioning

confidence: 99%

A Joint Neural Network for Session-Aware Recommendation

et al. 2020

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Session-aware recommendation is a special form of sequential recommendation, where users' previous interactions before current session are available. Recently, Recurrent Neural Network (RNN) based models are widely used in sequential recommendation tasks with great success. Previous works mainly focus on the interaction sequences of the current session without analyzing a user's long-term preferences. In this paper, we propose a joint neural network (JNN) for session-aware recommendation, which employs a Convolutional Neural Network(CNN) and a RNN to process the long-term historical interactions and the short-term sequential interactions respectively. Then, we apply a fully-connected neural network to study the complex relationship between these two types of features, which aims to generate a unified representation of the current session. Finally, a recommendation score for given items is generated by a bi-linear scheme upon the session representation. We conduct our experiments on three public datasets, showing that JNN outperforms the state-of-the-art baselines on all datasets in terms of Recall and Mean Reciprocal Rank (MRR). The outperforming results indicate that proper handling of historical interactions can improve the effectiveness of recommendation. The experimental results show that JNN is more prominent in samples with short current session or long historical interactions. INDEX TERMS Session-aware recommendation, sequential recommendation, recurrent neural networks, convolutional neural networks.

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“…Zhen et al [50] present a knowledge recommendation model to support personal knowledge management in a collaborative work environment. Verma et al [51] also propose a knowledge recommender system to support engineers to obtain knowledge. Yan et al [52] propose a new knowledge recommendation approach by refining the contextual and the relationship of engineers.…”

Section: ) Knowledge Recommendation Modelmentioning

confidence: 99%

Product Development-Oriented Knowledge Service: Status Review, Framework, and Solutions

Ming

Lin

et al. 2020

IEEE Access

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The product development knowledge stored in manufacturing firms can be obtained and re-used to assist the development engineers in new product development and facilitate the use of knowledge resources, which in turn enhances the quality and reduces the time-cost of product innovation. This study presents a knowledge service integrated framework for product development, which is refined from the literature and supported by practice needs. The proposed integrated framework is supported by four pillars of work, namely, how to represent knowledge exactly, the model of knowledge integration, the method of knowledge recommendation, and the mechanism and performance evaluation of knowledge service. A demonstration of a gantry crane design is provided to show the effectiveness and benefit of the proposed integrated framework and technology solutions. Finally, we offer some pointers for future research.

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Improving Scalability of Personalized Recommendation Systems for Enterprise Knowledge Workers

Cited by 19 publications

References 15 publications

Enhancing Context-Aware Recommendation Using Trust-Based Contextual Attentive Autoencoder

Enhancing Context-Aware Recommendation Using Trust-Based Contextual Attentive Autoencoder

A Joint Neural Network for Session-Aware Recommendation

Product Development-Oriented Knowledge Service: Status Review, Framework, and Solutions

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