Integrated Cosine And Tuned Cosine Similarity Measure To Alleviate Data Sparsity Issues For Personalized Recommendation

Mohana, H. S.; Suriakala, M.

doi:10.1109/csitss.2018.8768763

Cited by 1 publication

(1 citation statement)

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“…The next and major step of the work is calculation of the similarity between user preference which obtained through the user interface of the recommendation application. The system uses combination of two types of similarity techniques namely Jaccard similarity index [7] and cosine similarity index [8] along with consideration of sentiment attributes [9] to match sentences with correct meaning.…”

Section: Similarity Calculationmentioning

confidence: 99%

User Preference and Reviews Analysis with Neural Networks for Travel Recommender Systems

Bairavel¹,

Krishnamurthy²

2020

International Journal of Engineering Research and Technology

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These days' people like to explore numerous places all over the world predominantly which are highly recommended. For instance, people who are new to a particular location regularly choose places to visit manually by typing some of them wished by the user working on search engine applications. It becomes difficult whenever to search manually and plan accordingly which might be not accurate. To overcome the above issues, the travel recommendation system applies sentiment analysis comparing user preferences, number of days, number of people and delivers the top recommended places to the user considering the pre-existing reviews of the places which are recommended. While processing it calculates the similarity between user preferences/inclinations and reviews using sentimental attributes (positive and negative) to match the similarity. Travel recommendation system takes sentiment polarity and similarity value as parameters on the whole, also the days to be visited and suggests esteem places.

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Section: Similarity Calculationmentioning

confidence: 99%