An approach to task-oriented knowledge recommendation based on multi-granularity fuzzy linguistic method

Abstract

Purpose -In organizations, knowledge intensive activities are mainly task oriented. Finding relevant completed tasks to the new task and providing task-related knowledge to workers facilitate the knowledge reuse. However, relevant tasks are not easily found in the huge amount of completed tasks. The purpose of this paper is to assist the worker to find the required knowledge for the task at hand by reusing the knowledge related to relevant competed tasks. Design/methodology/approach -First, the task profile is constructed. Relevant degrees to categories which tasks to are derived by multi-granularity fuzzy linguistic method. The stages of completed tasks are identified by the modified KNN method. Second, similar completed tasks on categories are retrieved and then the relevant tasks are selected from the retrieved similar tasks by multi-granularity fuzzy linguistic method. Third, the worker's current task stage is derived by calculating the similarity between the rated knowledge and the knowledge in the stage of completed tasks. Finally, the knowledge is recommend based on stage relevance, relevance of the completed tasks and importance of the knowledge. Findings -The proposed method helps the worker find the knowledge related to the task at hand by finding and reusing the completed tasks. The experimental results show that the proposed method performs well and can fulfill the worker's' knowledge needs. The use of the linguistic term set with preferred granularities instead of precise numbers facilitates the expression of the opinions. The recommendation stage by stage makes the knowledge recommended more precisely. The obtained linguistic weight of the knowledge makes the recommended results understood more easily than the numerical values. Research limitations/implications -In the study, the authors just focus on the codified knowledge recommendation. However, there is another kind of knowledge named tacit knowledge, which exists in the mind of the experts. The constructing and updating of the expert profile can be investigated. Meanwhile, the new recommendation method which considers more factors also needs to be studied further. Practical implications -The paper includes implications for the development of the knowledge management system. The proposed approach can be applied as a tool of knowledge sharing. It facilitates the finding of the knowledge that is related to the task at hand. Originality/value -The paper provides new ways to find the relevant tasks and the related knowledge to the task at hand. Meanwhile, the new method to recommend the knowledge stage by stage is also proposed. It expands the research in the knowledge sharing and knowledge recommendation.

Advertisement recommendation based on personal interests and ad push fairness

“…Recommendation systems involve a filtering technology based on the users' preferences or interests which filters off the information the user does not need (Alyari and Jafari Navimipour, 2018). There are several domains which apply recommender systems, such as movies (Resnick et al, 1994), knowledge recommendation for workers (Li et al, 2015), destinations and tour services (Yuan and Yang, 2017), documents and books (Liu et al, 2012;Mooney and Roy, 2000), alliance partners (Yuan et al, 2015), colleague recommendations (Hazratzadeh and Navimipour, 2016) and products (Li et al, 2017;Liu et al, 2018). In general, there are some commonly used recommendation methods, including collaborative filtering (CF) and content-based filtering (CBF).…”

Integrating collaborative topic modeling and diversity for movie recommendations during news browsing

“…Recommender systems adopt filtering techniques to solve the problem of information overload for users by analyzing their historical preferences or interests and exploring items they may like (Resnick and Varian, 1997). Nowadays, recommender systems have widely been applied in several domains, such as products (Huang et al, 2019;Li et al, 2017;Liu et al, 2018), music (Patel and Wadhvani, 2018), advertisement (Liu et al, 2019), tasks or knowledge (Li et al, 2015;Zhang and Su, 2018), and tour services (Yuan and Yang, 2017). Recommender systems can be broadly divided into three categories based on how recommendations are provided (Alyari and Jafari Navimipour, 2018).…”

Differentiating the Personalized Information of the Physician‐Patient Communication for the Chronic Obstructive Pulmonary Disease with General Probabilistic Vector Linguistic Terms

“…us, the physician-patient communication of COPD is a multigranularity linguistic decision-making [34][35][36][37], and there is a gap between the doctor and patient's understandings of the same linguistic evaluation "slight serious." If they fail to properly recognize and deal with the gap in the communication process, it easily leads to poor communication quality or arising conflict.…”

“…Multigranularity linguistic decision-making is a kind of linguistic decision-making problem that a group of experts are invited to evaluate the same object together based on several different LESs. In the existing results of multigranularity linguistic evaluation, the LESs are assigned into the different hierarchies according to the characteristic granularities of them [34][35][36][37]. In the multigranularity linguistic decision-making process, there is an exploitation phase that should be taken before getting the final alternative solution.…”