An Implicit Preference-Aware Sequential Recommendation Method Based on Knowledge Graph

Sequential recommendation system has received widespread attention due to its good performance in solving data overload. However, most of the sequential recommendation methods assume that user’s preferences only depend on specific items in the current sequence and do not consider user’s implicit interests. In addition, most of the previous works mainly focus on exploiting relationships between items in the sequence and seldom consider quantifying the degree of preferences for items implied by user’s different behaviors. In order to address these above two problems, we propose an implicit preference-aware sequential recommendation method based on knowledge graph (IPAKG). Firstly, this method introduces knowledge graph to exploit user’s implicit preference representations. Secondly, we integrate recurrent neural network and attention mechanism to capture user’s evolving interests and relationships between different items in the sequence. Thirdly, we introduce the concept of behavior intensity and design a behavior activation unit to exploit the degree of preferences for items implied by a user’s different behaviors. Through the activation unit, the user’s preferences on different items are further quantified. Finally, we conduct experiments on an Amazon electronics dataset and Tmall dataset to evaluate the performance of our method. Experimental results demonstrate that our proposed method has better performance than those baseline methods.

Data literacy and the cognitive challenges of a data-rich business environment: an analysis of perceived data overload, technostress and their relationship to individual performance

PurposeConsidering the cognitive challenges associated with a data-rich business environment, this research aims to investigate the relationship between data literacy (DL), perceived data overload (PDO), and technostress (TS), besides the effect of these constructs on professional's individual performance (IP).Design/methodology/approachThrough survey research, the authors collected data from 321 professionals who work in data-rich and highly technological business environments. To test the hypotheses proposed, the authors developed the partial least squares structural equation modeling (PLS-SEM) procedures.FindingsThe results showed that DL is positively associated with IP and negatively with PDO. PDO is positively associated with TS and negatively with IP. The authors found no significant negative association between TS and IP.Research limitations/implicationsWith this research, the authors seek to contribute to the gap in the literature concerning two cognitive challenges associated with data-rich business environments: PDO and TS, analyzing from the point of view of the individual, and highlighting the importance of DL in this context.Practical implicationsThe results can assist managers in effectively being concerned with the DL level of their workforce. This is important considering not only the professionals' IP but also the cognitive challenges such as PDO and TS.Originality/valueThe innovation of this study lies in the empirical analysis of DL in the business context and its relationship with two cognitive challenges inherent in data-rich environments: PDO, and TS. Besides, the authors highlight the importance of understanding such phenomena in terms of IP.

Incorporating Contextual Information into Personalized Mobile Applications Recommendation

With the rise of the mobile internet, the number of mobile applications (apps) has shown explosive growth, which directly leads to the apps data overload. Currently, the recommender system has become the most effective method to solve the app data overload. App has the functional exclusiveness feature, which means the target users will not reuse apps with the same function in a certain spatiotemporal information. Most of the existing recommended methods for apps ignore the functional exclusiveness feature which makes it difficult to further improve the recommendation performance of the app recommendation. To solve this problem, we aim to improve the app recommendation performance, and propose a Personalized Context-aware Mobile App Recommendation Approach, called PCMARA. PCMARA comprehensively considers the user and app contextual information, which can mine the users app usage preference effectively. Specifically, (1) PCMARA explores the contextual characteristic of app, and constructs the app contextual factors for app which represent the function of app. (2) For the app functional exclusiveness problem, PCMARA leverages the app contextual factor to design a novel app similarity model, which enable to effectively eliminate this problem. (3) PCMARA considers the contextual information of users and apps to generates a recommendation list for target users based on the target users' current time and location. We applied the PCMARA to a real-world dataset and conducted a large-scale recommendation effect experiment. The experimental results show that the recommendation effect of PCMARA is satisfactory.

A window to the future: Proposal for a lexicography-assisted writing assistant

The paper initially discusses some of the challenges posed to contemporary lexicography and stresses the need to move upstream in the value chain to guarantee future work. Today’s lexicographers must accept that their product par excellence is not dictionaries, but lexicographical data that can either be presented to the users in the form of dictionaries or be integrated into various types of tools, platforms, and services. From this perspective, the paper describes the functionalities of various digital writing assistants and focuses on one of them, namely the Spanish-English Write Assistant. It illustrates some decisions that have to be taken to prepare a database to feed both this tool and a series of online dictionaries. A proposal on how a big amount of lexicographical data can be presented in a small pop-up window without resorting to data overload will be discussed. In this connection, alternative ways of doing user testing in a digital environment are introduced. Finally, the paper stresses the importance of a human-centered design and terminology.

Perancangan Manajemen Insiden pada Layanan Teknologi Informasi Inventory Menggunakan Framework ITIL Versi3 (Studi Kasus : PT. Genta Semar Mandiri Semarang)

Teknologi informasi digunakan oleh PT. Genta Semar Mandiri untuk mempermudah proses bisnis inventory. Namun pada kenyataanya masih sering terjadi insiden yang menghambat proses bisnis inventory. Insiden yang sering terjadi adalah aplikasi sering error, part number tidak terintegrasi dengan sistem, dan basis data overload. Selama ini insiden tersebut tidak dikelola dengan baik, hal ini disebabkan karena tidak adanya divisi yang khusus menangani insiden TI, serta belum dibuatnya SOP yang digunakan sebagai acuan menangani insiden TI. Penelitian ini bertujuan untuk mendapatkan hasil kesenjangan antara kondisi eksisting dengan kondisi ideal proses manajemen insiden pada PT. Genta Semar Mandiri serta mendapatkan SOP yang di perlukan dalam manajemen insiden. Penelitian ini menggunakan Framework ITIL versi 3 dalam merancang kerangka kerja dalam manajemen layanan insiden. Penelitian menunjukkan adanya beberapa perbedaan antara kondisi eksisting dengan kondisi ideal yaitu tidak adanya divisi khusus yang menangani manajamen layanan insiden, serta tidak adanya service desk yang bertugas sebagai pintu gerbang layanan insiden. Sehingga dihasilkan beberapa perubahan yaitu penambahan struktur organisasi pada perusahaan, SOP, serta form yang diperlukan pada penanganan manajemen layanan insiden berdasarkan framework ITIL versi 3. Kata kunci: Tata kelola TI, Manajemen insiden, ITIL versi 3, SOP, PT.Genta Semar Mandiri

Expert Finding Systems: A Systematic Review

The data overload problem and the specific nature of the experts’ knowledge can hinder many users from finding experts with the expertise they required. There are several expert finding systems, which aim to solve the data overload problem and often recommend experts who can fulfil the users’ information needs. This study conducted a Systematic Literature Review on the state-of-the-art expert finding systems and expertise seeking studies published between 2010 and 2019. We used a systematic process to select ninety-six articles, consisting of 57 journals, 34 conference proceedings, three book chapters, and one thesis. This study analyses the domains of expert finding systems, expertise sources, methods, and datasets. It also discusses the differences between expertise retrieval and seeking. Moreover, it identifies the contextual factors that have been combined into expert finding systems. Finally, it identifies five gaps in expert finding systems for future research. This review indicated that ≈65% of expert finding systems are used in the academic domain. This review forms a basis for future expert finding systems research.

A Visualization Technique to Support Searching Filtering

This paper discusses the need for an integrative literature review on Information Visualization for exploratory search particularly in handling data overload. The paper analyses many applications and web sites across disciplines. Certain search engines incorporate visualization to allow for better understanding of the information and at the same time reduce information overload. Current search engines use the query and response (lookup) process. Exploratory search allows for open-ended search. Visual representation is one feature in exploratory search that can be used to improve the overall search. The main contribution of this paper is the review of previous exploratory-search-based works, the utilised features as well as its existing applications, visualizations as the mechanism for developing filters to narrow down the results of searching. Many studies have shown that replacing traditional search engines with exploratory search by using the features of exploratory search can reduce the data overload.