HybRecSys: Content-based contextual hybrid venue recommender system

Bozanta, Aysun; Kutlu, Birgül

doi:10.1177/0165551518786678

Cited by 8 publications

(4 citation statements)

References 38 publications

(43 reference statements)

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“…Answering question Q4 defined in Table 1, if we focus on the different filtering methods used by the analyzed systems, we can see a tendency in the use of the collaborative filter. This is not a common result; normally content-based filters are used as starting filtering methods [32][33][34][35][36][37][38][39][40][41][42][43][44][45][46][47], and once a considerable amount of data from the users are obtained, collaborative filters are launched. This might be because some studies were started with some initial data about the users.…”

Section: Discussionmentioning

confidence: 99%

Systematic Review of Nutritional Recommendation Systems

2021

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In recent years, the promotion of healthy habits, and especially diet-oriented habits, has been one of the priority interests of our society. There are many apps created to count calories based on what we eat, or to estimate calorie consumption according to the sport we do, or to recommend recipes, but very few are capable of giving personalized recommendations. This review tries to see what studies exist and what recommendation systems are used for this purpose, over the last 5 years in the main databases. Among the results obtained, it is observed that the existing works focus on the recommendation system (usually collaborative filtering), and not so much on the description of the data or the sample analyzed; the indices used for the calculation of calories or nutrients are not specified. Therefore, it is necessary to work with open data, or well-described data, which allows the experience to be reproduced by third parties, or at least to be comparable. In recent years, the promotion of healthy habits, and especially diet-oriented habits, has been one of the priority interests of our society.

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Section: Discussionmentioning

confidence: 99%

Systematic Review of Nutritional Recommendation Systems

2021

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“…Deng et al [10] proposed the amalgamation of item rating data that user has given plus consolidated features of item to propose a novel recommendation model. Bozanta and Kutlu [6] proposed to gathered client visit chronicles, scene related data (separation, class, notoriety and cost) and relevant data (climate, season, date and time of visits) identified with singular client visits from different sources as each current scene suggestion framework calculation has its own disadvantages. Another issue is that basic data about setting is not ordinarily utilized in scene suggestion frameworks.…”

Section: Related Researchesmentioning

confidence: 99%

Content Recommendation Based on Topic Modeling

Papneja

Sharma

Khilwani³

2020

Computational Methods and Data Engineering

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With the proliferation in Internet usage and communicating devices, plenty amount of information is available at user disposal but on other side, it leads to a challenge to provide the fruitful information to end users. To overcome this problem, recommendation system plays a decisive role in providing pragmatic information to end users at appropriate time. This paper proposes a topic modeling based recommendation system to provide contents related to end users interest. Recommendation systems are based on different filtering mechanisms which are classified as content based, collaborative based, knowledge based, utility based and hybrid filtering, etc. The objective of this research is thus to proffer a recommendation system based on topic modeling. Benefit of latent Dirichlet allocation (LDA) is to uncover latent semantic structure from the text documents. By analyzing the contents using topic modeling, system can recommend the right articles to end users based on user interest.

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“…e Contextually personalized HybRecSys was compared with five algorithms: user-based K-nearest neighborhood (KNN) [68], item-based KNN [69], biased matrix factorization [70], SVD++ [71], and HybRecSys [1]. First four algorithms were available in the LibRec, a Java library for recommender systems.…”

Section: Offline Experimentsmentioning

confidence: 99%

“…is study is an expanded version of the previous study [1]. e scientific value of this study can be listed as below:…”

Section: Introductionmentioning

confidence: 99%

Developing a Contextually Personalized Hybrid Recommender System

Bozanta

Kutlu

2018

Mobile Information Systems

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It is hard to choose places to go from an endless number of options for some specific circumstances. Recommender systems are supposed to help us deal with these issues and make decisions that are more appropriate. The aim of this study is to recommend new venues to users according to their preferences. For this purpose, a hybrid recommendation model is proposed to integrate user-based and item-based collaborative filtering, content-based filtering together with contextual information in order to get rid of the disadvantages of each approach. Besides that, in which specific circumstances the user will like a specific venue is predicted for each user-venue pair. Moreover, threshold values determining the user’s liking toward a venue are determined separately for each user. Results are evaluated with both offline experiments (precision, recall, F-1 score) and a user study. Both the experimental evaluation with a real-world dataset and a user study of the proposed system showed improvement upon the baseline approaches.

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HybRecSys: Content-based contextual hybrid venue recommender system

Cited by 8 publications

References 38 publications

Systematic Review of Nutritional Recommendation Systems

Systematic Review of Nutritional Recommendation Systems

Content Recommendation Based on Topic Modeling

Developing a Contextually Personalized Hybrid Recommender System

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