Recommending Words Using a Bayesian Network

Funding Information: This work was supported by the REV@CONSTRUCTION mobiliser project, under the grant LISBOA-01-0247-FEDER-046123 from ANI (National Innovation Agency), with financial support from FCT (Fundação para a Ciência e a Tecnologia), through national funds. This work contributes to the Strategic Research Plan of the Centre for Marine Technology and Ocean Engineering (CENTEC), which is financed by FCT under contract UIDB/UIDP/00134/2020. Publisher Copyright: © 2023 by the authors.Asset management involves the coordinated activities of an organisation to derive value from assets, which may include physical assets. It encompasses activities related to design, construction, installation, operation, maintenance, renewal, and asset disposal. Asset management ensures the coordination of all activities, resources, and data related to physical assets. Recording and monitoring all maintenance activities is a key part of asset management, often done using work orders (WOs). Technicians typically create WOs using “free text”, which can result in missing or ungrammatical words, making it difficult to identify trends and analyse information. To standardise the terminology used for the same asset maintenance operation, this paper proposes a method that suggests words to technicians as they complete WOs. The word suggestion algorithm is based on past maintenance records, and a Bayesian network-based recommender system adapts to present needs verified by technicians using implicit user feedback. Implementing this system aims to normalise the terms used by technicians when filling in a WO. The corpus for this work comes from asset management records collected in a health facility in Portugal operated by a private company.publishersversionpublishe

Recommendations for item set completion: On the semantics of item co-occurrence with data sparsity, input size, and input modalities

We address the problem of recommending relevant items to a user in order to "complete" a partial set of items already known. We consider the two scenarios of citation and subject label recommendation, which resemble different semantics of item co-occurrence: relatedness for co-citations and diversity for subject labels. We assess the influence of the completeness of an already known partial item set on the recommender performance. We also investigate data sparsity through a pruning parameter and the influence of using additional metadata. As recommender models, we focus on different autoencoders, which are particularly suited for reconstructing missing items in a set. We extend autoencoders to exploit a multi-modal input of text and structured data. Our experiments on six real-world datasets show that supplying the partial item set as input is helpful when item co-occurrence resembles relatedness, while metadata are effective when co-occurrence implies diversity. This outcome means that the semantics of item co-occurrence is an important factor. The simple item co-occurrence model is a strong baseline for citation recommendation. However, autoencoders have the advantage to enable exploiting additional metadata besides the partial item set as input and achieve comparable performance. For the subject label recommendation task, the title is the most important attribute. Adding more input modalities sometimes even harms the result. In conclusion, it is crucial to consider the semantics of the item co-occurrence for the choice of an appropriate recommendation model and carefully decide which metadata to exploit

Knowledge-Enhanced Personalized Review Generation with Capsule Graph Neural Network

Personalized review generation (PRG) aims to automatically produce review text reflecting user preference, which is a challenging natural language generation task. Most of previous studies do not explicitly model factual description of products, tending to generate uninformative content. Moreover, they mainly focus on word-level generation, but cannot accurately reflect more abstractive user preference in multiple aspects. To address the above issues, we propose a novel knowledge-enhanced PRG model based on capsule graph neural network~(Caps-GNN). We first construct a heterogeneous knowledge graph (HKG) for utilizing rich item attributes. We adopt Caps-GNN to learn graph capsules for encoding underlying characteristics from the HKG. Our generation process contains two major steps, namely aspect sequence generation and sentence generation. First, based on graph capsules, we adaptively learn aspect capsules for inferring the aspect sequence. Then, conditioned on the inferred aspect label, we design a graph-based copy mechanism to generate sentences by incorporating related entities or words from HKG. To our knowledge, we are the first to utilize knowledge graph for the PRG task. The incorporated KG information is able to enhance user preference at both aspect and word levels. Extensive experiments on three real-world datasets have demonstrated the effectiveness of our model on the PRG task.Comment: Accepted by CIKM 2020 (Long Paper

Recommendation Systems: An Insight Into Current Development and Future Research Challenges

Research on recommendation systems is swiftly producing an abundance of novel methods, constantly challenging the current state-of-the-art. Inspired by advancements in many related fields, like Natural Language Processing and Computer Vision, many hybrid approaches based on deep learning are being proposed, making solid improvements over traditional methods. On the downside, this flurry of research activity, often focused on improving over a small number of baselines, makes it hard to identify reference methods and standardized evaluation protocols. Furthermore, the traditional categorization of recommendation systems into content-based, collaborative filtering and hybrid systems lacks the informativeness it once had. With this work, we provide a gentle introduction to recommendation systems, describing the task they are designed to solve and the challenges faced in research. Building on previous work, an extension to the standard taxonomy is presented, to better reflect the latest research trends, including the diverse use of content and temporal information. To ease the approach toward the technical methodologies recently proposed in this field, we review several representative methods selected primarily from top conferences and systematically describe their goals and novelty. We formalize the main evaluation metrics adopted by researchers and identify the most commonly used benchmarks. Lastly, we discuss issues in current research practices by analyzing experimental results reported on three popular datasets

Machine learning applied to asset management

Dissertação para obtenção do Grau de Mestre em Engenharia Informática e de ComputadoresA gestão de ativos consiste num processo que pretende coordenar o ciclo de vida dos ativos de uma dada empresa. Implica manter um registo dos vários objetos físicos que estão sob a propriedade da empresa, tais como equipamento, ferramentas e máquinas. Uma ordem de trabalho (OT) consiste num documento (em papel ou digital) que descreve todos os passos que permitem completar uma operação de manutenção num dado ativo. Ao serem criadas ordens de trabalho de um modo livre, isto é, sem qualquer restrição no que é escrito, o critério na sua elaboração é deixado ao cargo do técnico, o que pode levar a palavras não existentes ou gramaticalmente incorretas, ou até mesmo dificultar a identificação de tendências por parte de algoritmos de análise. O objetivo é atingir a normalização das palavras que são utilizadas quando se considera a mesma situação da gestão de ativos. A normalização é atingida através da sugestão de palavras enquanto um técnico está a preencher uma ordem de trabalho. O algoritmo que proporciona as sugestões funciona com base no passado, ao analisar situações passadas da gestão de ativos. O corpus provém de ordens de trabalhos recolhidas numa unidade de saúde em Portugal, que é operada por uma empresa privada. Em primeiro lugar, a aplicação de técnicas de Processamento Natural de Linguagem sobre as descrições das ordens de trabalho permite entender como é que as palavras são usadas neste domínio técnico. De seguida, é implementado um sistema de recomendação baseado numa Rede Bayesiana. Este sistema de recomendação tem a capacidade de se adaptar às necessidades verificadas no presente pelos técnicos, através da inclusão de um mecanismo implícito de feedback. A implementação deste sistema de recomendação pretende atingir o objetivo de alcançar a normalização dos termos que são utilizados por um técnico aquando a criação de uma ordem de trabalho.Asset management is a process that aims to coordinate the life cycle of a company’s assets. It involves keeping track of all of the various physical objects that are under the ownership of a given company, such as equipment, tools, and machines. A work order (WO) is a document (paper or digital) that describes all steps to perform the maintenance operation. When creating a work order using “free text”, the sentences are left to the technician’s discretion, which can lead to non-existent or grammatically incorrect words, or even make it difficult to identify tendencies and perform an appropriate analysis. The purpose is achieving a normalisation of the words that are used when the same asset maintenance situation is considered. The normalisation is reached by suggesting words while a technician is filling a work order. The word suggestion algorithm works based on the past of similar maintenance occurrences. The corpus comes from asset management records (work orders) collected in a health facility in Portugal, which is operated by a private company. Each maintenance record is used to characterise and keep history of a given asset management occurrence. First, the application of Natural Language Processing (NLP) techniques to process the work order’s description allow to understand how the words are used in this technical domain. Then, a recommender system based on a Bayesian Network is implemented. This recommender system has the capability of adapting itself to the present needs verified by the technicians through the inclusion of an implicit user feedback mechanism. The implementation of this recommender system aims at the objective of achieving the normalisation of the terms used by the technicians when filling a work order.N/

Context-aware Deep Model for Entity Recommendation in Search Engine at Alibaba

Entity recommendation, providing search users with an improved experience via assisting them in finding related entities for a given query, has become an indispensable feature of today's search engines. Existing studies typically only consider the queries with explicit entities. They usually fail to handle complex queries that without entities, such as "what food is good for cold weather", because their models could not infer the underlying meaning of the input text. In this work, we believe that contexts convey valuable evidence that could facilitate the semantic modeling of queries, and take them into consideration for entity recommendation. In order to better model the semantics of queries and entities, we learn the representation of queries and entities jointly with attentive deep neural networks. We evaluate our approach using large-scale, real-world search logs from a widely used commercial Chinese search engine. Our system has been deployed in ShenMa Search Engine and you can fetch it in UC Browser of Alibaba. Results from online A/B test suggest that the impression efficiency of click-through rate increased by 5.1% and page view increased by 5.5%.Comment: CIKM2019 International Workshop on Entity Retrieval. arXiv admin note: text overlap with arXiv:1511.08996 by other author

TM-vector: A Novel Forecasting Approach for Market stock movement with a Rich Representation of Twitter and Market data

Stock market forecasting has been a challenging part for many analysts and researchers. Trend analysis, statistical techniques, and movement indicators have traditionally been used to predict stock price movements, but text extraction has emerged as a promising method in recent years. The use of neural networks, especially recurrent neural networks, is abundant in the literature. In most studies, the impact of different users was considered equal or ignored, whereas users can have other effects. In the current study, we will introduce TM-vector and then use this vector to train an IndRNN and ultimately model the market users' behaviour. In the proposed model, TM-vector is simultaneously trained with both the extracted Twitter features and market information. Various factors have been used for the effectiveness of the proposed forecasting approach, including the characteristics of each individual user, their impact on each other, and their impact on the market, to predict market direction more accurately. Dow Jones 30 index has been used in current work. The accuracy obtained for predicting daily stock changes of Apple is based on various models, closed to over 95\% and for the other stocks is significant. Our results indicate the effectiveness of TM-vector in predicting stock market direction.Comment: 24 pag