A Comprehensive Study and Evaluation of Recommender Systems

Vineela, A.; Devi, G. Lavanya; Nelaturi, Naresh; Yadav, Goldy

doi:10.1007/978-981-15-3828-5_5

Cited by 5 publications

(2 citation statements)

References 9 publications

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“…( 26)). The more related topics of attention shown at the head of the proposed list, the higher the NDCG score [3,72]:…”

Section: Precisionmentioning

confidence: 99%

“…In general, Recommender Systems (RSs) assist users in discovering the content, products, or services they need from a large amount of information on the web [1]. The tourism industry, which attempts to deliver personalized user experience and context, is one of the most prevalent implementations of RS [2,3]. There has been an increase in the number of articles utilizing Location-Based Social Networks (LBSN) and spatial-temporal information in tourist RS during the last several years [1,4].…”

Section: Introductionmentioning

confidence: 99%

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A hybrid recommender system using topic modeling and prefixspan algorithm in social media

Avval

Harounabadi

2023

Complex Intell. Syst.

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Route schema is difficult to plan for tourists, because they demand to pick points of interest (POI) in unknown areas that align with their preferences and limitations. This research proposes a novel personalized method for POI route recommendation that employs contextual data. The proposed approach enhances the existing methods by considering user preferences and multifaceted tourism contexts. Due to the sparsity of the data, the proposed method employs two-level clustering (DBSCAN based on the Manhattan distance) that reduces the time to discover POI. In specific, this approach utilizes the following: first, a topic pattern model is employed to discover the users’ attraction diffusion while improving the user–user similarity model using a novel asymmetric schema. Second, it has used explicit demographic information to alleviate the cold start issue, and third, it proposes a new strategy for assessing user preferences and also combined the context parameters in the form of a vector model with the Term Frequency Inverse Document Frequency technique to find contexts’ similarity. Furthermore, our framework discovers a list of optimal candidate trips by involving personalized POIs in sequential patterns’ mining (SPM); also, it used an adjusted forgotten function to involve the date context of each trip. Based on two datasets (Flickr and Gowalla), our methodology beats other prior approaches in F-score, RMSE, MAP, and NDCG factors in the experimental evaluation.

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“…( 26)). The more related topics of attention shown at the head of the proposed list, the higher the NDCG score [3,72]:…”

Section: Precisionmentioning

confidence: 99%