Guide Me: A Research Work Area Recommender System

Sharma, Richa; Vinayak, Sharu; Singh, Robert P.

doi:10.5815/ijisa.2016.09.04

Cited by 9 publications

(9 citation statements)

References 11 publications

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“…On the other hand, to better consider characteristics such as hobbies, topics of interest ... Sharma et al [31] propose an RS named Research Work Area Recommender System (RWARS) that suggests POIs based on the similarity calculation between users using the cosine similarity approach of collaborative filtering.…”

Section: Related Workmentioning

confidence: 99%

Towards an Incremental Recommendation of POIs for Mobile Tourists without Profiles

Dennouni

Peter

Lancieri

et al. 2018

IJISA

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Mobile tourism or m-tourism can assist and help tourists anywhere and anytime face the overload of information that may appear in their smartphones. Indeed, these mobile users find difficulties in the choice of points of interest (POIs) that may interest them during their discovery of a new environment (a city, a university campus ...). In order to reduce the number of POIs to visit, the recommendation systems (RS) represent a good solution to guide each tourist towards personalized paths close to his instantaneous location during his visit. In this article, we focus on (1) the detection of the spatiotemporal context of the tourist to filter the POIs and (2) the use of the previous notations of the places. These two criteria make it possible to integrate the evolutionary context of the visit in order to predict incrementally the most relevant transitions to be borrowed by the tourists without profile. These predictions are calculated using collaborative filtering algorithms that require the collection of traces of tourists to better refine the recommendation of POIs. In our software prototype, we have adapted the SLOPE ONE algorithm to our context of discovering the city of Chlef to avoid problems like data scarcity, cold start and scalability. In order to validate the use of this prototype, we conducted experiments by tourists in order to calculate indicators to assess the relevance of the recommendations provided by our system.

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Section: Related Workmentioning

confidence: 99%

Towards an Incremental Recommendation of POIs for Mobile Tourists without Profiles

Dennouni

Peter

Lancieri

et al. 2018

IJISA

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“…The difference is that, in context-aware systems [15], the selection is based on the user's context which allows the users to trust in the recommended items [29,30] while the recommender systems rely on the user's interest. These two systems are complementary to each other, hence the need for their integration.…”

Section: Related Workmentioning

confidence: 99%

Experimental Validation of Contextual Variables for Research Resources Recommender System

Isinkaye¹,

Folajimi²

2018

IJISA

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Abstract-Context-aware recommender system (CARS) is a promising technique for recommending research resources to users (researchers) by predicting their preferences (resources) under different situations. If the contextual information given to such a system is inappropriate, it will certainly have a negative effect on the nature of recommendation output generated by the system as well as making the system to have high dimensionality complexity. Currently, several CARS recommendation algorithms have been developed but they have failed to bring to bear the means and importance of experimentally validating the contextual information used in different domains of application of CARS. Hence, this paper experimentally validates the contextual variables in the domain of research resources by splitting a research resource (article) into three major sections (introduction, review and methodology). These sections are the contextual variables validated in order to authenticate their viability as context that could be used in recommending research resources based on the specific section of an article a researcher is interested in. The result of our experiment shows that irrespective of the domain of articles, journal articles have higher variability in their citations at introduction, very significant variability between the articles in the review and high variability in the methodology contextual variable respectively than the articles in the proceeding under the three contextual variables. This experiment shows that these three variables could be used as context .It also shows the percentage of splitting that could be used within journals and proceedings for context-aware research resources recommendations.

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“…If anyone wanted to visit tourist places, he normally took recommendations from his friends who were known to that place or who have visited there earlier. Even while selecting the clothes, people ask opinion of their friends and family members [1]. As internet is becoming popular, users started to take recommendations from the web [2].…”

Section: Introductionmentioning

confidence: 99%

User-Review Oriented Social Recommender System for Event Planning

Singh¹,

Singh²,

Bathla³

2020

ISI

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Recommender system is used to suggest product or topic based on user’s interest. Existing recommender system have focused on books, product, music etc. The problem in existing recommender system is that wedding/event based suggestions are not available. In the modern information era; storage, communication has been a challenge due to information veracity, volume, and velocity. Due to the constant and exponential growth of information, the utilization of information for context-oriented services is not productive. In this paper, a wedding planner recommender system framework has been proposed based on hybrid approach i.e., content based, collaborative filtering technique. The motive of proposed framework is to generate user-specific recommendations for different tasks related to the event specially wedding event, analyzed from the user comments on his social networking portal. Its main objective is to assist the user for organizing the events by suggesting specific vendors needed to arrange the event activities. Also, it would enhance the sales of location sensitive products in social commerce. The trial study conducted using a set of Facebook users is carried out to validate the proposed recommendation system framework. The success of the proposed framework is reported in terms of the level of user satisfaction achieved.

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Guide Me: A Research Work Area Recommender System

Cited by 9 publications

References 11 publications

Towards an Incremental Recommendation of POIs for Mobile Tourists without Profiles

Towards an Incremental Recommendation of POIs for Mobile Tourists without Profiles

Experimental Validation of Contextual Variables for Research Resources Recommender System

User-Review Oriented Social Recommender System for Event Planning

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