Automated Collaborative Filtering Applications for Online Recruitment Services

Rafter, Rachael; Bradley, Keith; Smyth, Barry

doi:10.1007/3-540-44595-1_48

Cited by 37 publications

(19 citation statements)

References 2 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The exponential growth of Internet allowed the developement of an online jobsearch sites market [1,2]. The answers of candidates represent a lot of information that can not be managed efficiently by companies [4,5].…”

Section: Introductionmentioning

confidence: 99%

Automatic Profiling System for Ranking Candidates Answers in Human Resources

Kessler

Béchet

Roche

et al. 2008

On the Move to Meaningful Internet Systems: OTM 2008 Workshops

View full text Add to dashboard Cite

Abstract. The exponential growth of Internet allowed the development of a market of online job search sites. This work aims at presenting the E-Gen system (Automatic Job Offer Processing system for Human Resources). E-Gen will implement several complex tasks: an analysis and categorization of jobs offers which are unstructured text documents (e-mails of job offers possibly with an attached document), an analysis and a relevance ranking of the candidate answers. We present a strategy to resolve the last task: After a process of filtering and lemmatisation, we use vectorial representation and different similarity measures. The quality of ranking obtained is evaluated using ROC curves.

show abstract

Section: Introductionmentioning

confidence: 99%

Automatic Profiling System for Ranking Candidates Answers in Human Resources

Kessler

Béchet

Roche

et al. 2008

On the Move to Meaningful Internet Systems: OTM 2008 Workshops

View full text Add to dashboard Cite

show abstract

“…Until recently collaborative recommender systems have been styled on a single-shot model of recommendation, where a single set of recommendations is generated based entirely on a user's stored preference information, for example (Konstan et al, 1997;Rafter et al, 2000;Rafter and Smyth, 2001). The process is a non-interactive one; no current information is sought from users at recommendation time regarding what they are looking for, and the recommendations are based solely on what the users have liked or disliked in the past.…”

Section: Introductionmentioning

confidence: 99%

Conversational Collaborative Recommendation – An Experimental Analysis

Rafter

Smyth

2005

Artif Intell Rev

Self Cite

View full text Add to dashboard Cite

Publication informationArtificial Intelligence Review, 24 (3-4): 301-308 Abstract. Traditionally, collaborative recommender systems have been based on a single-shot model of recommendation where a single set of recommendations is generated based on a user's (past) stored preferences. However, content-based recommender system research has begun to look towards more conversational models of recommendation, where the user is actively engaged in directing search at recommendation time. Such interactions can range from high-level dialogues with the user, possibly in natural language, to more simple interactions where the user is, for example, asked to indicate a preference for one of k suggested items. Importantly, the feedback attained from these interactions can help to differentiate between the user's long-term stored preferences, and her current (short-term) requirements, which may be quite different. We argue that such interactions can also be beneficial to collaborative recommendation and provide experimental evidence to support this claim.

show abstract

“…These profiles can then be used in different ways to help influence subsequent recommendations as we will see. First, it is worth highlighting how this approach to profiling stands in contrast to the type of ratingsbased profiles that are exploited by collaborative filtering systems (see, for example [5,30,47,74,77,85,89] and also Chapter 9 [83] of this book). Ratings-based profiles are content-free, in the sense that they are devoid of any item content; ratings may be associated with a particular item identifier but the information about the item itself (other than the ratings information) is generally not available, at least in pure collaborative filtering systems.…”

Section: Case-based Profilingmentioning

confidence: 99%

“…CASPER is online recruitment system (see Figure 11.15) that uses single-shot casebased recommendation to suggest jobs to users based on some initial query. It monitors the responses of users to these recommendations in order to construct case-based profiles that can be used to personalise future recommendation sessions; see [10,74,88]. Upon receiving a job recommendation a user has various action choices.…”

Section: Case-based Profilingmentioning

confidence: 99%

Case-Based Recommendation

Smyth

Lecture Notes in Computer Science

Self Cite

154

View full text Add to dashboard Cite

Abstract.Recommender systems try to help users access complex information spaces. A good example is when they are used to help users to access online product catalogs, where recommender systems have proven to be especially useful for making product suggestions in response to evolving user needs and preferences. Case-based recommendation is a form of content-based recommendation that is well suited to many product recommendation domains where individual products are described in terms of a well defined set of features (e.g., price, colour, make, etc.). These representations allow case-based recommenders to make judgments about product similarities in order to improve the quality of their recommendations and as a result this type of approach has proven to be very successful in many e-commerce settings, especially when the needs and preferences of users are ill-defined, as they often are. In this chapter we will describe the basic approach to case-based recommendation, highlighting how it differs from other recommendation technologies, and introducing some recent advances that have led to more powerful and flexible recommender systems. IntroductionRecently I wanted to buy a new digital camera. I had a vague idea of what I wanted-a 6 mega-pixel digital SLR from a good manufacturer-but it proved difficult and time consuming to locate a product online that suited my needs, especially as these needs evolved during my investigations. Many online stores allowed me to browse or navigate through their product catalog by choosing from a series of static features (e.g., manufacturer, camera type, resolution, level of zoom etc.). Each time I selected a feature I was presented with the set of cameras with this feature and I could then go on to choose another feature to further refine the presented products. Other stores allowed me to search for my ideal camera by entering a query (e.g. "digital slr, 6 mega-pixels") and presented me with a list of results which I could then browse at my leisure.Both of these access options were helpful in different ways-in the beginning I preferred to browse through catalogs but, after getting a feel for the various features and compromises, I tended to use search-based interfaces-however neither provided P. Brusilovsky,

show abstract

Automated Collaborative Filtering Applications for Online Recruitment Services

Cited by 37 publications

References 2 publications

Automatic Profiling System for Ranking Candidates Answers in Human Resources

Automatic Profiling System for Ranking Candidates Answers in Human Resources

Conversational Collaborative Recommendation – An Experimental Analysis

Case-Based Recommendation

Contact Info

Product

Resources

About