A content based approach for recommending personnel for job positions

Almalis, Nikolaos D.; Tsihrintzis, George A.; Karagiannis, Nikolaos

doi:10.1109/iisa.2014.6878720

Cited by 23 publications

(8 citation statements)

References 13 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Except from this shortfall of the existing techniques, there is also another obstacle to be addressed, namely the crowd problem. In many domains, some researchers call it crowd wisdom and it could be wisdom in job seeking and recruiting domain only in some cases (i.e., 1-N and N-1) which we handled well in a previous work of ours (Almalis et al, 2014). But in the N-N case, the crowd problem arises significantly as it is common for many suitable job seekers to be found for one job, but no one for some other.…”

Section: Job Recommendation Systemsmentioning

confidence: 83%

Handling the crowd avoidance problem in job recommendation systems integrating FoDRA

Almalis

Tsihrintzis

2020

IJCISTUDIES

Self Cite

View full text Add to dashboard Cite

In this article, we present the basic principles and approaches of job recommender systems (JRSs). Furthermore, we describe the four different relation types of the job seeking and recruiting problem, derived directly from the formal definition of JRSs. We use our previously published four dimensions recommendation algorithm (FoDRA) to calculate the suitability of a person for a job and then we model a job seeking and recruiting problem consisting of many candidates and many jobs (N-N case). Finally, we test the algorithm and present the results proposing a solution -the minimum acceptable suitability level -for the crowd avoidance problem that occurs. Our study produces good results and shows that this approach can be considered as an important asset in the domain of job seeking and recruiting.

show abstract

Section: Job Recommendation Systemsmentioning

confidence: 83%

Handling the crowd avoidance problem in job recommendation systems integrating FoDRA

Almalis

Tsihrintzis

2020

IJCISTUDIES

Self Cite

View full text Add to dashboard Cite

show abstract

“…by using weighted versions of these operators. Other works have considered simpler fusion strategies such as a sum or product of unidirectional preferences [13,153], whereas other have used logical connectives [89], set intersection between both users' recommendation lists [161] or aggregation of the ranking positions of x and y in each other's recommendation lists [107], to name a few. Some challenges and areas for Harmonic mean combined with sum Sudo et al [148] Sum of similarities/distances Almalis et al [13], Yu et al [166] Product operator Ting et al [153], Li and Li [96] Weighted mean Kleinermann et al [84], Xia et al [158] Multiple averaging and uninorm aggregation functions Neve and Palomares [112,113] Matrix multiplication Jacobsen and Spanakis [75] Set intersection of recommendable users Yacef and McLaren [161], Kutty et al [90] Aggregation (union) of probabilities Pizzato and Silvertrini [125] Average similarity between x and previous successful interactions with y…”

Section: Perspective A: Fusion Strategies and Reciprocitymentioning

confidence: 99%

Reciprocal Recommender Systems: Analysis of State-of-Art Literature, Challenges and Opportunities towards Social Recommendation

Palomares¹,

Porcel²,

Pizzato³

et al. 2020

Preprint

View full text Add to dashboard Cite

Many social services including online dating, social media, recruitment and online learning, largely rely on "matching people with the right people". The success of these services and the user experience with them often depends on their ability to match users. Reciprocal Recommender Systems (RRS) arose to facilitate this process by identifying users who are a potential match for each other, based on information provided by them. These systems are inherently more complex than user-item recommendation approaches and unidirectional user recommendation services, since they need to take into account both users' preferences towards each other in the recommendation process. This entails not only predicting accurate preference estimates as classical recommenders do, but also defining adequate fusion processes for aggregating user-to-user preferential information. The latter is a crucial and distinctive, yet barely investigated aspect in RRS research. This paper presents a snapshot analysis of the extant literature to summarize the state-of-the-art RRS research to date, focusing on the fundamental features that differentiate RRSs from other classes of recommender systems. Following this, we discuss the challenges and opportunities for future research on RRSs, with special focus on (i) fusion strategies to account for reciprocity and (ii) emerging application domains related to social recommendation.

show abstract

“…The principle of a content-based [3] recommender [9][10] is to suggest items that have similar content to ones the target user prefers. The process of content-based recommender is selecting the same feature type and comparing them by calculating their similarity for people and jobs [23].…”

Section: Content Based Recommendation Designmentioning

confidence: 99%

Employment Recommendation System using Matching, Collaborative Filtering and Content Based Recommendation

Belsare¹,

Deshmukh²

2018

IJCATR

View full text Add to dashboard Cite

Abstract:The tremendous growth of both information and usage has led to a so-called information overload problem in which users are finding it increasingly difficult to locate the right information at the right time Thus huge amount of information and easy access to it make recommender systems unavoidable [1]. We use recommender system every day without realizing it and without knowing what exactly happens. Recommender systems have changed the way people find products, information, and even other people. They study patterns of behavior to know what someone will prefer from among a collection of things he/she has never experienced. Benefits of recommender systems to the businesses using them include: The ability to offer unique personalized service for the customer, Increase trust and customer loyalty, Increase sales, click-through rates, conversions, etc., Opportunities for promotion, persuasion and Obtain more knowledge about customers. Recommender systems are software tools and techniques providing suggestions for items to be of use to a user. Job recommender systems are desired to attain a high level of accuracy while making the predictions which are relevant to the customer, as it becomes a very tedious task to explore thousands of jobs, posted on the web, periodically. Although a lot of job recommender systems [2] exist that use different strategies, here efforts have been put to make the job recommendations on the basis of candidates profile matching as well as preserving candidates job behavior or preferences. Firstly, the rules predicting the general preferences of the different user groups are mined. Then the job recommendations to the target candidate are made on the basis of content based matching as well as candidate preferences, which are preserved either in the form of mined rules or obtained by candidates own applied job history.

show abstract

A content based approach for recommending personnel for job positions

Cited by 23 publications

References 13 publications

Handling the crowd avoidance problem in job recommendation systems integrating FoDRA

Handling the crowd avoidance problem in job recommendation systems integrating FoDRA

Reciprocal Recommender Systems: Analysis of State-of-Art Literature, Challenges and Opportunities towards Social Recommendation

Employment Recommendation System using Matching, Collaborative Filtering and Content Based Recommendation

Contact Info

Product

Resources

About